Natural history and prognostic model of untreated breast cancer: a study based on the SEER database

Natural history and prognostic model of untreated breast cancer: a study based on the SEER database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Natural history and prognostic model of untreated breast cancer: a study based on the SEER database Dasong Wang, Yan Yang, Hongwei Yang, Lei Yang, Maoshan Chen, Li Fan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4108112/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose : The primary aim of this study was to explore the natural history of breast cancer in the absence of treatment, aiming to identify the main factors affecting the prognosis of untreated breast cancer patients. Methods : Data for patients diagnosed with invasive breast cancer between 2010 and 2020 were obtained from the SEER database. The disease progression time was estimated by subtracting the median survival time of higher stages from that of lower stages. Untreated patients were divided into a training set and a validation set in a 7:3 ratio. In the training set, independent risk factors affecting prognosis were identified through univariate and multivariate cox proportional hazards regression analysis. A nomogram model was constructed using these risk factors to predict the prognosis of untreated patients, which was then validated in the validation set. Results : In untreated breast cancer patients, the progression time from stage I to stage II was 43 months, from stage II to stage III was 18 months, and from stage III to stage IV was 9 months. Age, tumor stage, ER status, PR status, histological grade, and marital status were identified as independent predictive factors for the overall survival of untreated breast cancer patients, and a nomogram model was constructed with these factors. Conclusion : The disease progression speed of breast cancer patients accelerates with the increase in tumor stage in the absence of treatment, and prognosis progressively worsens. Age, tumor stage, ER status, PR status, histological grade, and marital status are independent risk factors affecting the prognosis of untreated patients. breast cancer natural history nomogram SEER database Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Breast cancer is the most common malignant tumor among women worldwide, accounting for 25–30% of all new cancer cases in women globally. It is also a leading cause of cancer-related deaths among women, contributing to about 15% of all cancer deaths[ 1 – 3 ]. In 2020, approximately 2.3 million women were newly diagnosed with breast cancer worldwide, and about 685,000 deaths were attributed to the disease[ 2 , 4 ]. The incidence of breast cancer varies by region but is increasing in almost all areas of the world[ 2 , 4 ]. Breast cancer generally has a favorable prognosis and a relatively long survival period. In recent years, the survival rate for breast cancer has significantly improved due to advancements in treatment techniques and the widespread availability of screening[ 3 , 5 ]. In the 1970s, the 5-year cancer-specific survival rate for breast cancer patients was about 75%, but by 2015, this figure had risen to approximately 90%[ 5 – 8 ]. However, even with the continuous improvement in breast cancer prognoses, a minority of patients remain untreated due to lack of medical access or cultural biases[ 9 , 10 ]. The prognosis for untreated breast cancer patients is significantly worse. In most studies reported, breast cancer patients who receive no treatment only survive for 3–4 years, and only 5–10% of untreated patients live longer than 10 years[ 10 ]. The natural history of a disease refers to the natural behavior of the disease when left untreated[ 11 ]. For breast cancer, studying its natural history is beneficial for enhancing understanding of the disease and for learning about its natural progression in the absence of treatment intervention. It also helps to understand the impact of the disease on the lifespan of the affected population[ 10 , 11 ]. Furthermore, knowledge of the natural history of the disease can inform the benefits of certain treatment options, serving as a foundation for further clinical research to evaluate the effectiveness of these treatments[ 10 , 11 ]. Currently, research on the natural history of breast cancer is limited, with most conclusions drawn from small-sample, retrospective studies, which offer limited guidance for clinical practice and lack effective tools for predicting survival times in untreated patients[ 10 ]. Under the current diagnostic and treatment system, standardized treatment is generally recommended for breast cancer patients without significant contraindications upon diagnosis[ 12 ]. However, studying the natural history of breast cancer requires a large number of patients diagnosed with the disease who have not undergone any treatment, along with a sufficiently long follow-up period. This is not in line with current standards for breast cancer diagnosis and treatment, making it almost impossible to conduct large-scale clinical trials to study its natural history in clinical practice. The National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database ( https://seer.cancer.gov/ ) is a large public database that covers about 28% of the U.S. population, providing clinical and pathological information about patient demographics, tumor characteristics, diagnosis, treatment, and prognosis[ 13 ]. The SEER database includes a significant number of untreated breast cancer patients and possesses relatively complete follow-up data and sufficiently long follow-up periods. Therefore, in this study, we utilize the SEER database to statistically analyze the natural histroy of untreated breast cancer, study the factors affecting breast cancer prognosis, and construct a prognostic model to guide clinical diagnosis and treatment. Materials and methods Data source and study population We obtained data from the National Cancer Institute’s SEER program. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All information from the SEER program is available and free for public, so the agreement of the medical ethics committee board was not necessary. Moreover, due to the strict register-based nature of the study, informed consent was waived. Patient data from the SEER database (Version:8.4.1, https://seer.cancer.gov/data-software/), SEER Research Data, 17 registries, Nov 2022 Sub (2000-2020) were screened. Patients with newly diagnosed breast cancer from 2010 to 2020 were initially enrolled in this study. The including and excluding criteria of patients with breast cancer were as follows. A schematic representation of the patient screening process can be observed in Figure 1. Inclusion criteria: Pathologically diagnosed with invasive breast cancer. Diagnosis year between 2010 and 2020. Age at diagnosis over 18 years. Exclusion criteria: Not the only primary malignant tumor. Follow-up time less than one month or follow-up incomplete. Incomplete follow-up data. Special pathological types of malignant breast tumors. Male patients. Unknown tumor size or tumor stage. Unknown treatment information. Unknown histologic grade/ estrogen receptor (ER)/ progesterone receptor (PR)/ human epidermal growth factor receptor-2 (HER2) status. Variable assessment The treatment information of breast cancer contained in the SEER database includes surgery, chemotherapy and radiotherapy. Considering these treatment modalities are the most important and common for breast cancer, patients who did not receive surgery, radiotherapy, or chemotherapy were defined as the untreated group in this study. Based on the inclusion and exclusion criteria, a total of 367,175 breast cancer patients were included in the analysis. Of these, 357,768 patients received treatment, while 9,407 patients did not undergo any treatment. Demographic parameters included age at diagnosis (categorized as ≤50 years, 51-70 years, >70 years), race (including White, Black, Other), and marital status (including married, unmarried). Clinical and pathological parameters included tumor size (categorized as 0-20 mm, 21-50 mm, >50 mm), tumor location (categorized as central, medial, lateral, other), laterality (including left, right), histological type (including invasive ductal carcinoma, invasive lobular carcinoma, mixed ductal and lobular carcinoma), histological grade (including I, II, III), tumor stage (adjusted to the 7th edition American Joint Committee on Cancer (AJCC) staging system, including I, II, III, IV), T stage (including T1, T2, T3, T4), N stage (including N0, N1, N2, N3), M stage (including M0, M1), ER status (including positive, negative), PR status (including positive, negative), HER2 status (including positive, negative), and subtype (including HR+/HER2-, HR+/HER2+, HR-/HER2+, and HR-/HER2-). Survival analysis The primary endpoint of this study was the overall survival of breast cancer patients, defined as the time from the diagnosis date of breast cancer to the patient's death. Median survival time (MST) was defined as the time at which 50% of patients reach the survival endpoint. The progression time of the disease could be estimated by subtracting the MST of a more severe stage from its preceding stage. In this study, we used the estimated disease progression time to assess the natural history of breast cancer. Additionally, kaplan-Meier survival curves and survival rates are also used to compare the survival differences among breast cancers with different characteristics. Construction and validation of the nomogram To construct and validate the nomogram, the untreated patients were randomly divided into training and validation sets in a ratio of 7:3. Univariate cox regression was used to identify risk factors associated with the prognosis of the untreated patients, and statistically significant variables were included in multivariate cox regression analysis. Finally, variables with p<0.05 in multivariate cox regression were determined as independent risk factors and used to construct a nomogram for predicting the prognosis of the untreated patients with breast cancer. The concordance index (C-index) and the receiver operating characteristic curve (ROC) were used to evaluate the discriminative ability of the nomogram. The calibration curves were used to compare the association between the actual outcomes and predicted probabilities. The clinical usefulness and benefits of the predictive model were estimated through decision curve analysis (DCA). Statistical analysis The chi-square test or Fisher exact test was performed for comparison of categorical data, and t test was used for comparison of continuous variables. Cumulative survival time was calculated using the Kaplan-Meier method, and the differences in survival curves were analyzed using the log-rank test. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify variables that significantly affected the prognosis of the untreated patients with breast cancer in the training set. All statistical analyses were performed using Stata15.0 Software. P values <0.05 denoted statistically significant differences. Results Clinical and pathological characteristics This study included a total of 367,175 patients diagnosed with breast cancer between 2010 and 2020. Among them, 357,768 patients received treatment, while 9,407 patients did not receive any treatment. In the entire patient cohort, the median age was 60 years, with the majority being white (78.01%) and married (56.90%). In terms of clinical and pathological characteristics, the majority of patients (58.78%) had tumor sizes less than 20mm. The most common tumor location was the outer quadrant of the breast, accounting for 43.32% of all patients. Invasive ductal carcinoma was the most common histological type, representing 84.67% of all patients. Histological grade II was observed in 45.60% of all patients. The distribution of tumors in the left and right breasts was nearly equal. Most patients were diagnosed at stage I (57.86%) or stage II (28.62%). The untreated group had later-stage tumors compared to the treated group. During the follow-up period of the study, a total of 44,190 (12.04%) patients died, including 39,727 (11.10%) in the treated group and 4,463 (47.44%) in the untreated group. There was a significant difference in survival rates between the treated and untreated groups. The patients in the treated and untreated groups exhibited significant differences in the distribution of demographic and clinical-pathological characteristics. Therefore, propensity score matching (PSM) was performed to compare the impact of treatment factors on the prognosis of breast cancer patients. The demographic and clinical distributions for pre- and post-PSM groups can be found in Tables 1 and 2, respectively. Table1 Demographic and clinicopathological characteristics of breast cancer patients before PSM Characteristics All patients(%) Treated group(%) Untreated group(%) P value 367175(100) 357768(97.44) 9407(2.56) Age(years) 70 81833(22.29) 77109(21.55) 4724(50.22) Race <0.001 White 286436(78.01) 279454(78.11) 6982(74.22) Black 38563(10.50) 37243(10.41) 1320(14.03) Others 42176(11.49) 41071(11.48) 1105(11.75) Marital status <0.001 Married 208916(56.90) 205714(57.50) 3202(34.04) Unmarried 158259(43.10) 152054(42.50) 6205(65.96) Tumorsize 50 28731(7.82) 26937(7.53) 1794(19.07) Location <0.001 central 17177(4.68) 16543(4.62) 634(6.74) inner 68144(18.55) 66687(18.64) 1427(15.17) outer 159074(43.32) 155614(43.50) 3460(36.78) Other* 122810(33.45) 118924(33.24) 3886(41.31) laterality 0.014 left 186291(50.74) 181400(50.70) 4891(51.99) right 180884(49.26) 176368(49.30) 4516(48.01) Histology <0.001 IDC 310889(84.67) 303045(84.70) 7844(83.38) ILC 35435(9.92) 35258(9.85) 1177(12.51) IDLC 19851(5.41) 19465(5.44) 386(4.10) Grade <0.001 I 80699(21.98) 78870(22.05) 1829(19.44) II 167433(45.60) 162624(45.46) 4809(51.12) III 119043(32.42) 116274(32.50) 2769(29.44) AJCC stage <0.001 I 212461(57.86) 209318(58.51) 3143(33.41) II 105097(28.62) 102233(28.58) 2864(30.45) III 35884(9.77) 34882(9.75) 1002(10.65) IV 13733(3.74) 11335(3.17) 2398(25.49) T stage <0.001 T1 214727(58.48) 211557(59.13) 3170(33.70) T2 117894(32.11) 114114(31.90) 3780(40.18) T3 23197(6.32) 22030(6.16) 1167(12.41) T4 11357(3.09) 10067(2.81) 1290(13.71) N stage <0.001 N0 249187(67.87) 243499(68.06) 5688(60.47) N1 89403(24.35) 86340(24.13) 3063(32.56) N2 17347(4.72) 16990(4.75) 357(3.80) N3 11238(3.06) 10939(3.06) 299(3.18) M stage <0.001 M0 353442(96.26) 346433(96.83) 7009(74.51) M1 13733(3.74) 11335(3.17) 2398(25.49) ER status <0.001 positive 307949(83.87) 299812(83.80) 8137(86.50) negative 59226(16.13) 57956(16.20) 1270(13.50) PR status 0.019 positive 270291(73.61) 263267(73.59) 7024(74.67) negative 96884(26.39) 94501(26.41) 2383(25.33) Her2 status 0.735 positive 56507(15.39) 55071(15.39) 1436(15.27) negative 310668(84.61) 302697(84.61) 7971(84.73) Subtype <0.001 HR+/HER2- 271192(73.86) 264009(73.79) 7183(76.36) HR+/HER2+ 40464(11.02) 39402(11.01) 1062(11.29) HR-/HER2+ 16043(4.37) 15669(4.38) 374(3.98) HR-/HER2- 39476(10.75) 38688(10.81) 788(8.38) Surgery <0.001 Yes 346017(94.24) 346017(96.72) 0(0) No 21158(5.76) 11751(3.28) 9407(100) Radiation <0.001 Yes 221660(60.37) 221660(61.96) 0(0) No 145515(39.63) 136108(38.04) 9407(100) Chemotherapy Yes 155745(42.42) 155745(43.53) 0(0) <0.001 No 211430(57.58) 202023(56.47) 9407(100) OS <0.001 Alive 322985(87.96) 318041(88.90) 4944(52.56) Dead 44190(12.04) 39727(11.10) 4463(47.44) CSS <0.001 Alive/dead of other cause 341396(92.98) 334961(93.63) 6435(68.41) Dead of breast cancer 25779(7.02) 22807(6.37) 2972(31.59) Other*: axillary and overlapping of the breast; IDC = invasive ductal cacinoma; ILC = invasive lobular carcinoma; IDLC =invasive duct and lobular carcinoma; AJCC, American Joint Committee on Cancer; ER = estrogen receptor; PR = progesterone receptor; HER2 = human epidermal growth factor receptor type 2; OS, overall survival; CSS, cancer specific survival Table2 Demographic and clinicopathological characteristics of breast cancer patients after PSM Characteristics All patients(%) Treated group(%) Untreated group(%) P value 14968(100) 7484(50) 7484(50) Age(years) 1.000 ≤50 2130(14.23) 1065(14.23) 1065(14.23) 51-70 5554(37.11) 2777(37.11) 2777(37.11) >70 7284(48.66) 3642(48.66) 3642(48.66) Race 1.000 White 11600(77.50) 5800(77.50) 5800(77.50) Black 1810(12.09) 905(12.09) 905(12.09) Others 1558(10.41) 779(10.41) 779(10.41) Marital status 1.000 Married 5166(34.51) 2583(34.51) 2583(34.51) Unmarried 9802(65.49) 4901(65.49) 4901(65.49) Tumorsize 1.000 0-20 5912(39.50) 2956(39.50) 2956(39.50) 21-50 6882(45.98) 3441(45.98) 3441(45.98) >50 2174(14.52) 1087(14.52) 1087(14.52) Location 1.000 central 732(4.89) 366(4.89) 366(4.89) inner 2208(14.75) 1104(14.75) 1104(14.75) outer 5806(38.79) 2903(38.79) 2903(38.79) Other* 6222(41.57) 3111(41.57) 3111(41.57) laterality 1.000 left 7756(51.82) 3878(51.82) 3878(51.82) right 7212(48.18) 3606(48.18) 3606(48.18) Histology 1.000 IDC 12882(86.06) 6441(86.06) 6441(86.06) ILC 1624(10.85) 812(10.85) 812(10.85) IDLC 462(3.09) 231(3.09) 231(3.09) Grade 1.000 I 2190(19.44) 1455(19.44) 1455(19.44) II 7810(52.18) 3905(52.18) 3905(52.18) III 4248(28.38) 2124(28.38) 2124(28.38) AJCC stage 1.000 I 6178(41.27) 3089(41.27) 3089(41.27) II 5236(34.98) 2618(34.98) 2618(34.98) III 1238(8.27) 619(8.27) 619(8.27) IV 2316(15.47) 1158(15.47) 1158(15.47) T stage 1.000 T1 5848(39.07) 2924(39.07) 2924(39.07) T2 6434(42.99) 3217(42.99) 3217(42.99) T3 1502(10.03) 751(10.03) 751(10.03) T4 1184(7.91) 592(7.91) 592(7.91) N stage 1.000 N0 9964(66.57) 4982(66.57) 4982(66.57) N1 4408(29.45) 2204(29.45) 2204(29.45) N2 306(2.04) 153(2.04) 153(2.04) N3 290(1.94) 145(1.94) 145(1.94) M stage 1.000 M0 12652(84.53) 6326(84.53) 6326(84.53) M1 2316(15.47) 1158(15.47) 1158(15.47) ER status 1.000 positive 13132(87.73) 6566(87.73) 6566(87.73) negative 1836(12,27) 918(12.27) 918(12.27) PR status 1.000 positive 11616(77.61) 5808(77.61) 5808(77.61) negative 3352(22.39) 1676(22.39) 1676(22.39) Her2 status 1.000 positive 1902(12.71) 951(12.71) 951(12.71) negative 13066(87.29) 6533(87.29) 6533(87.29) Subtype 1.000 HR+/HER2- 11824(79.00) 5912(79.00) 5912(79.00) HR+/HER2+ 1384(9.25) 692(9.25) 692(9.25) HR-/HER2+ 518(3.46) 259(3.46) 259(3.46) HR-/HER2- 1242(8.30) 621(8.30) 621(8.30) Surgery Yes 6688(44.68) 6688(89.36) 0(0) <0.001 No 8280(55.32) 796(10.64) 7484(100) Radiation <0.001 Yes 4087(27.30) 4087(54.61) 0(0) No 10881(72.70) 3397(45.39) 7484(100) Chemotherapy <0.001 Yes 2792(18.65) 2792(37.31) 0(0) No 12176(81.35) 4692(62.69) 7484(100) OS <0.001 Alive 9505(63.50) 5206(69.56) 4299(57.44) Dead 5463(36.50) 2278(30.44) 3185(43.56) CSS <0.001 Alive/dead of other cause 11832(79.05) 6287(84.01) 5545(74.09) Dead of breast cancer 3136(20.95) 1197(15.99) 1939(25.91) Other*: axillary and overlapping of the breast; IDC = invasive ductal cacinoma; ILC = invasive lobular carcinoma; IDLC =invasive duct and lobular carcinoma; AJCC, American Joint Committee on Cancer; ER = estrogen receptor; PR = progesterone receptor; HER2 = human epidermal growth factor receptor type 2; OS, overall survival; CSS, cancer specific survival Impact of treatment on the prognosis of breast cancer patients In this study, 9,407 (2.56%) patients did not receive any treatment, 346,017 (94.24%) patients underwent surgical treatment, 221,660 (60.37%) patients received radiation therapy, and 155,745 (42.42%) patients underwent chemotherapy. Regardless of before or after PSM, treatment was a crucial factor affecting the prognosis of breast cancer patients. As shown in Figure 2, the treated group had higher overall survival rates and cancer-specific survival rates compared to the untreated group. As shown in Table 3, in the subgroup analysis after PSM, the treated group had better prognosis in different tumor stages compared to the untreated group. Table 3 Ten-year overall survival rate after PSM in treated and untreated groups Treated(N%, 95%CI) Untreated(N%, 95%CI) P value All patients 61.73(60.34-63.09) 27.40(25.14-29.70) <0.001 AJCC stage I 78.22(76.27-80.03) 41.75(37.29-46.14) <0.001 II 62.48(60.15-64.71) 25.83(22.41-29.36) <0.001 III 46.62(41.44-51.62) 15.43(9.88-22.11) <0.001 IV 14.00(10.65-17.80) 7.82(4.94-11.55) <0.001 T stage T1 76.96(75.08-78.71) 39.64(35.62-43.63) <0.001 T2 54.55(52.21-56.82) 23.78(20.52-27.19) <0.001 T3 47.31(42.24-52.20) 14.11(9.00-20.35) <0.001 T4 22.28(17.40-27.54) 4.77(1.34-11.68) <0.001 N stage N0 68.94(67.32-70.50) 32.44(29.41-35.51) <0.001 N1 48.70(45.92-51.42) 18.53(15.28-22.03) <0.001 N2 30.48(20.69-40.83) NA* <0.001 N3 22.90(15.24-31.51) NA* <0.001 M stage M0 68.60(67.15-70.00) 31.48(28.87-34.11) <0.001 M1 14.00(10.65-17.80) 7.82(4.94-11.55) <0.001 NA* There was not enough sample size for analysis Natural progression of untreated breast cancer patients Kaplan-Meier survival analysis was conducted in the untreated group of patients. As shown in Figure 3, the prognosis of untreated patients worsened with increasing stages. In all untreated patients, the median overall survival time was 47 months (95% CI: 46-49), with median survival times of 94 months (95% CI: 85-104) for stage I, 51 months (95% CI: 47-55) for stage II, 33 months (95% CI: 29-37) for stage III, and 24 months (95% CI: 22-26) for stage IV. The development of breast cancer typically starts with genetic mutations in cells, gradually progressing to early-stage breast cancer and further advancing to intermediate and late-stage breast cancer without treatment intervention[14]. In this study, we estimated the progression time of breast cancer by calculating the difference in median survival time between patients with lower-stage breast cancer and those with higher-stage breast cancer. As shown in Table 4, the progression times from stage I to II, from II to III, and from III to IV were 43 months, 18 months, and 9 months, respectively. Factors related to the prognosis of untreated breast cancer patients In the case of untreated breast cancer patients, 6,583 cases were randomly assigned to the training set, and 2,824 cases to the validation set. There was no significant difference in the distribution of demographic and clinicopathologic characteristics between the training and validation sets, indicating comparability between the two groups. In the training set, variables related to the prognosis of breast cancer patients were assessed using the log-rank test and univariate Cox regression analysis. The results showed that age, race, marital status, tumor size, tumor location, histological grade, tumor stage, T stage, N stage, M stage, ER status, PR status, HER2 status, and molecular subtype were associated with the overall survival of breast cancer. All variables with statistical significance in the univariate analysis were included in a multivariate Cox regression model, which revealed that age, marital status, histological grade, tumor stage, ER status, and PR status were independent predictive factors affecting the overall survival of breast cancer patients. The results are presented in Table 5. Table 4 Median survival time and disease progression time in untreated patients Patients at risk Median survival time(months,95%CI) Progression time(months) All patients 8842 47(46-49) NA Stage I 3008 94(85-104) 43 Stage II 2761 51(47-55) 18 Stage III 961 33(29-37) 9 Stage IV 2112 24(22-26) NA Table5 Univariate and multivariate cox regression analysis (Training Cohort). Characteristics Univariate analysis Multivariate analysis HR(95%CI) P HR(95%CI) P Age(years) ≤50 reference reference 51-70 1.37(1.21-1.54) <0.001 1.43(1.23-1.65) 70 2.75(2.46-3.08) <0.001 3.10(2.70-3.56) <0.001 Race White reference reference Black 1.10(1.01-1.20) 0.023 1.07(0.97-1.19) 0.179 Others 0.65(0.58-0.73) <0.001 0.75(0.48-1.27) 0.298 Marital status Married reference reference Unmarried 1.51(1.41-1.62) <0.001 1.33(1.22-1.44) <0.001 Tumorsize 0-20 reference reference 21-50 1.78(1.66-1.92) 50 2.46(2.26-2.68) <0.001 0.85(0.65-1.11) 0.245 Location central reference reference inner 0.63(0.55-0.72) <0.001 0.76(0.56-1.05) 0.093 outer 0.72(0.64-0.81) <0.001 0.87(0.76-1.01) 0.051 Other* 0.80(0.71-0.90) <0.001 0.87(0.76-1.01) 0.055 laterality left reference NA right 0.95(0.89-1.01) 0.096 NA Histology IDC reference NA ILC 1.06(0.97-1.16) 0.199 NA IDLC 0.88(0.76-1.03) 0.117 NA Grade I reference reference II 1.46(1.33-1.59) <0.001 1.14(1.02-1.27) 0.019 III 2.01(1.83-2.20) <0.001 1.36(1.20-1.53) <0.001 AJCC stage I reference reference II 1.82(1.67-1.99) <0.001 1.58(1.42-1.77) <0.001 III 2.86(2.56-3.19) <0.001 2.40(2.10-2.74) <0.001 IV 3.87(3.55-4.23) <0.001 3.89(3.49-4.33) <0.001 T stage T1 reference reference T2 1.78(1.65-1.93) <0.001 1.12(0.98-1.29) 0.080 T3 2.33(2.11-2.58) <0.001 1.13(0.95-1.34) 0.155 T4 3.03(2.76-3.33) <0.001 1.30(0.79-2.11) 0.302 N stage N0 reference reference N1 1.70(1.59-1.81) <0.001 1.02(0.93-1.12) 0.649 N2 2.20(1.91-2.52) <0.001 1.03(0.86-1.23) 0.784 N3 1.99(1.70-2.31) <0.001 0.89(0.73-1.07) 0.225 M stage M0 reference Reference M1 2.46(2.31-2.62) <0.001 1.44(0.93-2.24) 0.103 ER status positive reference reference negative 1.76(1.62-1.92) <0.001 1.58(1.38-1.80) <0.001 PR status positive reference reference negative 1.62(1.51-1.73) <0.001 1.32(1.19-1.46) <0.001 Her2 status positive reference reference negative 0.80(0.74-0.87) <0.001 1.04(0.93-1.16) 0.427 Subtype HR+/HER2- reference reference HR+/HER2+ 1.21(1.10-1.34) <0.001 0.97(0.57-1.66) 0.922 HR-/HER2+ 1.67(1.44-1.94) <0.001 1.10(0.75-1.63) 0.597 HR-/HER2- 1.84(1.66-2.03) <0.001 1.30(0.77-2.16) 0.317 Other*: axillary and overlapping of the breast; IDC = invasive ductal cacinoma; ILC = invasive lobular carcinoma; IDLC =invasive duct and lobular carcinoma; AJCC, American Joint Committee on Cancer; ER = estrogen receptor; PR = progesterone receptor; HER2 = human epidermal growth factor receptor type 2; OS, overall survival; CSS, cancer specific survival. Construction and validation of a nomogram By utilizing the independent risk factors affecting the prognosis of breast cancer patients identified through Cox regression, a Nomogram model was developed to predict the 5-year and 10-year overall survival rates for untreated breast cancer patients (Figure 4). The Nomogram model indicates that tumor stage has the greatest impact on the prognosis of breast cancer patients, followed by age. ER status has a more significant influence compared to PR status, while histological grade and marital status have relatively smaller effects. By adding up the scores of all prognostic indicators to calculate a total score, the 5-year and 10-year overall survival rates for each untreated breast cancer patient can be estimated. To assess the predictive accuracy of the nomogram, both the C-index and ROC curves were employed. The C-index of the nomogram was calculated as 0.7763(95% CI: 0.7655-0.7872) in the training set and 0.7857(95% CI: 0.7702-0.8012) in the validation set. The ROC curves of the nomogram (Figure 5) were consistent with the C-index, indicating excellent predictive ability of the models. As shown in Figure 6, the 95% CI of calibration belt in both training and validation sets hardly cross the diagonal bisector line. This observation indicated that the calibration plot demonstrates favorable concordance between the predicted probabilities and the actual observed outcomes. The DCA was applied to make comparisons of the availability and advantages between the nomogram model and AJCC TNM stage system (Figure 7). The findings indicate that the nomogram demonstrates greater practicality in predicting the prognosis of patients with breast cancer when compared to the TNM stage system. Discussion The natural history of a disease refers to the disease's natural behavior in the absence of treatment. Studying the natural history of untreated cases is foundational for understanding any disease and serves as the real backdrop for evaluating the effectiveness of treatment modalities[ 10 , 11 ]. For breast cancer, researching its natural history means investigating the disease's changes without treatment intervention, which can be approached through various methods[ 11 ]. Describing and recording the clinical manifestation changes in breast cancer patients without treatment may be the most direct method of study. Most breast cancer patients present with a lump in the breast as the main clinical manifestation, typically appearing 3–12 months before diagnosis. Usually, without treatment, the tumor will infiltrate the skin, leading to skin ulcers and fixation to the chest wall. If the patient survives, the tumor often invades the other breast and may lead to distant organ metastasis, with death typically occurring after evident metastasis[ 10 ]. Describing the changes in clinical manifestations of breast cancer patients is an intuitive study method, but subjective descriptions can hardly reflect objective changes in the condition. Using imaging to document changes in the size of breast cancer lesions is a more objective method of study. In another study, researchers investigated the progression speed of breast cancer in untreated cases by comparing X-ray images of patients with delayed diagnosis[ 15 ]. The study showed that tumors in 94% of breast cancer patients gradually grew over time, with a median tumor volume doubling time of 385 days. The growth rate of tumors varied greatly, with histological grade and tumor size being positively correlated with tumor growth speed[ 15 ]. However, this method of documenting changes in lesion size does not directly reflect the impact of disease progression on breast cancer patients. Some studies have recorded the survival time of breast cancer patients without treatment. In most reports, the majority of patients survived nearly 3–4 years without any treatment, and a very few patients exhibited impressively good prognoses even without any treatment, with about 5–10% of untreated breast cancer patients living for more than 10 years[ 10 , 16 , 17 ]. In our study, among all untreated breast cancer patients, the median survival time was 47 months (approximately 3.9 years), which is consistent with most previous reports; the estimated 10-year survival rate for all untreated patients was 22.68%, which is better than the previously reported 5–10%. The difference might be partly due to improved screening methods leading to relatively earlier staging at diagnosis[ 18 , 19 ]. Another consideration is that even if patients did not receive any anti-tumor treatment, the use of some nonspecific modern therapeutic drugs (such as antibiotics) could also prolong the survival time of some patients[ 10 , 17 ]. Describing the survival time of untreated patients does not fully capture the natural history of breast cancer. Researching the natural history of breast cancer is not only about studying the survival time of untreated patients but also involves exploring the developmental changes of the disease itself. Tumor staging is one of the main factors affecting the prognosis of breast cancer patients and serves as an objective quantitative indicator of the severity of the patient's condition[20]. Breast cancer typically begins with genetic mutations in cells, gradually developing into early-stage breast cancer. Without treatment intervention, it will further progress to mid-stage and late-stage breast cancer[14]. Therefore, studying the time it takes for breast cancer patients to progress from one stage to the next without treatment can objectively and accurately reflect the changes in the condition of breast cancer, providing an objective and accurate method for researching the natural history of breast cancer[11]. In our study, we estimated the progression time of untreated breast cancer patients by comparing the median survival times of patients at different stages, thus illustrating the natural history of breast cancer. The study found that, in the absence of treatment, the progression time from stage I to II, from II to III, and from III to IV were 43 months, 18 months, and 9 months, respectively, with the disease's progression speed accelerating as the stage increases. This indicates that without treatment intervention, the progression speed of breast cancer is not constant; the progression of breast cancer exacerbates the tumor burden, and an increased tumor burden further accelerates the progression of breast cancer, leading to a vicious cycle of worsening conditions. This also reflects from another aspect that early treatment is key to improving the prognosis of breast cancer patients. Every breast cancer patient exhibits unique clinical and pathological characteristics, and descriptions of survival times for groups cannot accurately represent individual survival expectations[ 21 ]. To gain a deeper understanding of the natural history of breast cancer, we conducted further analysis on factors that could affect the prognosis of breast cancer. The results indicated that age, marital status, histological grade, tumor stage, ER status, and PR status are independent risk factors affecting the prognosis of breast cancer in the absence of treatment. Based on these risk factors, we constructed a nomogram model to predict the prognosis of untreated breast cancer. Validation within an internal group showed that the model has good predictive power and accuracy, demonstrating better accuracy and practicality compared to the currently widely used TNM tumor staging system. Additionally, the factors used to build the model are easily obtainable clinical and pathological factors, which can be applied in clinical practice for prognosis assessment and guiding the formulation of individualized treatment plans. Tumor staging is one of the primary indicators currently used to evaluate the prognosis of breast cancer patients. The American Joint Committee on Cancer's 8th edition of the Breast Cancer TNM Staging System is widely used for staging and prognostic assessment of breast cancer[ 20 , 22 ]. Consistent with many previous studies, the prognosis of breast cancer patients worsens as the tumor stage advances[ 21 , 23 , 24 ]. Our research has yielded similar results; the prognosis of breast cancer patients deteriorates with the progression of tumor stages, regardless of whether the patients have undergone anti-tumor treatment. This objective pattern serves as a crucial theoretical basis for our study on the natural history of breast cancer, allowing us to estimate the progression time of breast cancer and thus study its natural course. Age is a significant factor affecting the prognosis of breast cancer patients[ 25 , 26 ]. A retrospective study examining the relationship between the age of breast cancer patients and their prognosis found a unique U-shaped association between diagnosis age and breast cancer prognosis[ 27 ]. That is, both young and elderly breast cancer patients tend to have relatively poorer prognoses. Among middle-aged and older breast cancer patients, both the overall survival rate and the cancer-specific survival rate decrease with age. However, it is noteworthy that young breast cancer patients, particularly those under 35, tend to have poorer outcomes, often presenting with larger breast tumors and a higher rate of axillary lymph node metastasis[ 27 ]. The incidence of breast cancer also correlates with age, being more common in middle-aged and older women, with women over 50 accounting for 82% of new breast cancer diagnoses[ 28 , 29 ]. Based on the relationship between breast cancer incidence and age, our study divided patients into three age groups: ≤50 years, 51–70 years, and > 70 years, to minimize significant differences in numbers across age groups. The results revealed that the prognosis of untreated breast cancer patients worsened with increasing age, consistent with the general trend reported in previous studies. On one hand, older patients inherently have a shorter life expectancy and are more likely to have other fatal diseases, such as cardiovascular and respiratory diseases; on the other hand, they are also more prone to delayed diagnosis and treatment, which adversely affects their prognosis[ 30 ]. Steroid hormone receptors (ER, PR) and HER2 are currently essential indicators for molecular typing and prognostic assessment of breast cancer based on immunohistochemistry[ 31 ]. Assessing ER and PR status is crucial for identifying patient groups that benefit from endocrine therapy and for predicting prognosis. Patients with hormone receptor-positive breast cancer often exhibit milder clinical and pathological characteristics, such as later occurrence of lymph node metastasis and vascular invasion, and usually benefit from endocrine therapy, often having a better prognosis[ 32 , 33 ]. Our study found that even in untreated breast cancer patients, ER and PR status remain significant indicators affecting prognosis, with hormone receptor-positive patients having a better prognosis, possibly due to relatively milder clinical and pathological characteristics. Assessing HER2 status is important for identifying patients suitable for anti-HER2 targeted therapy and for predicting prognosis. HER2-positive breast cancer typically exhibits more aggressive clinical and pathological features and generally has a relatively poorer prognosis. However, anti-HER2 targeted therapy has significantly improved the prognosis of patients with HER2-positive breast cancer[ 31 ]. In our study, univariate analysis of factors affecting the prognosis of untreated breast cancer revealed that HER2-positive patients have a relatively poorer prognosis, but multivariate analysis showed that HER2 is not an independent risk factor for the prognosis of untreated breast cancer. Histological grading is an important factor affecting breast cancer prognosis. Multiple studies have shown that in invasive breast cancer, histological grade is definitively related to prognosis, with higher histological grades usually indicating more aggressive characteristics and leading to poorer outcomes[ 34 , 35 ]. In our study, we found that histological grade is also a crucial factor affecting the prognosis of untreated breast cancer, with prognosis worsening as histological grade increases. Social psychological factors are closely related to the prognosis of malignant tumors, and marital status is one of the most important social psychological factors influencing the occurrence and development of malignant tumors[ 36 , 37 ]. Studies have found that marital status is an independent risk factor affecting the prognosis of breast cancer patients. In subgroup analyses based on different stages, gender, and molecular subtypes, unmarried patients consistently shown a significantly higher risk of death compared to their married counterparts[ 37 – 39 ]. Married individuals often lead healthier lifestyles, including healthy diets, regular physical exercise, and routine health check-ups, which may be intermediary factors in cancer prevention[ 40 ]. Moreover, married individuals typically have better emotional well-being and receive more social support, including emotional and financial support, allowing them to focus on treatment and more easily recover from a solitary malignancy. Our study found similar results; marital status remained an independent risk factor affecting the prognosis of untreated breast cancer patients, with married patients having a significantly better prognosis than unmarried ones. In summary, the factors affecting the prognosis of untreated breast cancer patients are similar to the non-therapeutic factors reported in previous studies. In our study, the most significant factors impacting the prognosis of untreated breast cancer patients were the tumor stage at diagnosis and the age at diagnosis, followed by ER/PR status, histological grade, and marital status. Based on these risk factors, we have constructed a prognostic model that is more practical than the AJCC staging system, intended to guide clinical applications and practices. Limitations This study has certain limitations that should be taken into consideration. As a retrospective study, it may be subject to selection bias, as some cases might have been excluded due to incomplete data. Secondly, certain factors potentially related to breast cancer prognosis, such as comorbidities of the patients, the degree of local invasion of the tumor, and Ki-67 levels, could not be analyzed due to the lack of these factors in the SEER database. Additionally, the absence of information on endocrine treatment for breast cancer in the SEER database might lead to inaccuracies in the definition of the untreated group in this study. Finally, since both the training and validation sets of the model are derived from the SEER database, there is a possibility of overfitting. External validation on other datasets is necessary to ensure the reproducibility of the model. Conclusion Treatment is a key factor affecting the prognosis of breast cancer patients, with treated patients showing significantly better outcomes than untreated ones. In the absence of treatment, the progression of breast cancer accelerates with increasing tumor stages, and prognosis progressively worsens. Age, marital status, histological grade, tumor stage, ER status, and PR status are independent risk factors affecting the prognosis of untreated breast cancer patients. The predictive model constructed based on these factors demonstrates good predictive ability for the prognosis of untreated breast cancer patients. Abbreviations SEER:Surveillance, Epidemiology, and End Results database; ER:estrogen receptor; PR: progesterone receptor; HER2: human epidermal growth factor receptor-2; MST: median survival time; C-index: concordance index; ROC: receiver operating characteristic curve; DCA: decision curve analysis; PSM: propensity score matching. Declarations Data availability statement The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Ethics statement The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All information from the SEER program is available and free for public, so the agreement of the medical ethics committee board was not necessary. Moreover, due to the strict register-based nature of the study, informed consent was waived. Author contributions Dasong Wang contributed to the study conception and design. Dasong Wang, Yan Yang and Yu He participate in writing proposal, analyzed the data, wrote the result and discussion. Dasong Wang, Hongwei Yang, Maoshan Chen, Li Fan and Lei Yang participate in analyzing the data, writing result and prepared manuscript. All authors commented on previous versions of the manuscript, and all authors read and approved the final manuscript. Funding This work was supported by Scientific research project of Suining Central Hospital (No. 2016y17). Acknowledgments The authors would like to thank SEER for the open access to the database. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Siegel RL, Miller KD, Wagle NS et al (2023) Cancer statistics, 2023. CA Cancer J Clin 73:17–48. https://doi.org/10.3322/caac.21763 Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660 Houghton SC, Hankinson SE (2021) Cancer progress and priorities: breast cancer. Cancer Epidemiol Biomarkers Prev 30:822–844. https://doi.org/10.1158/1055-9965.EPI-20-1193 Giaquinto AN, Sung H, Miller KD et al (2022) Breast cancer statistics, 2022. CA Cancer J Clin 72:524–541. https://doi.org/10.3322/caac.21754 Torre LA, Islami F, Siegel RL et al (2017) Global cancer in women: burden and trends. Cancer Epidemiol Biomarkers Prev 26:444–457. https://doi.org/10.1158/1055-9965.EPI-16-0858 Berry DA, Cronin KA, Plevritis SK et al (2005) Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med 353:1784–1792. https://doi.org/10.1056/NEJMoa050518 Kristeleit H, Parton M, Beresford M et al (2016) Long-term follow-up data from pivotal studies of adjuvant trastuzumab in early breast cancer. Target Oncol 11:579–591. https://doi.org/10.1007/s11523-016-0438-5 EBCTCG (2015) Aromatase inhibitors versus tamoxifen in early breast cancer: patient-level meta-analysis of the randomised trials. Lancet 386:1341–1352. https://doi.org/10.1016/S0140-6736(15)61074-1 Johnstone PA, Norton MS, Riffenburgh RH (2000) Survival of patients with untreated breast cancer. J Surg Oncol 73:273–277. https://doi.org/10.1002/(sici)1096-9098(200004)73:43.0.co;2-h Galmarini CM, Tredan O, Galmarini FC (2015) Survivorship in untreated breast cancer patients. Med Oncol 32:466. https://doi.org/10.1007/s12032-014-0466-x Wu J, Liu H, Hu T et al (2022) The natural history of breast cancer: a chronological analysis of breast cancer progression using data from the seer database. Ann Transl Med 10:365. https://doi.org/10.21037/atm-22-918 Loibl S, Andre F, Bachelot T et al (2023) Early breast cancer: esmo clinical practice guideline for diagnosis, treatment and follow-up(dagger). Ann Oncol. https://doi.org/10.1016/j.annonc.2023.11.016 Liu R, Xiao Z, Hu D et al (2021) Cancer-specific survival outcome in early-stage young breast cancer: evidence from the seer database analysis. Front Endocrinol (Lausanne) 12:811878. https://doi.org/10.3389/fendo.2021.811878 Strandberg R, Czene K, Eriksson M et al (2022) Estimating distributions of breast cancer onset and growth in a swedish mammography screening cohort. Cancer Epidemiol Biomarkers Prev 31:569–577. https://doi.org/10.1158/1055-9965.EPI-21-1011 Rojas KE, Manasseh DM, Rojas M et al (2020) The natural history of untreated estrogen receptor-positive, her2-negative invasive breast cancer. Breast Cancer Res Treat 182:79–83. https://doi.org/10.1007/s10549-020-05666-7 Han E, Johnson N, DelaMelena T et al (2011) Alternative therapy used as primary treatment for breast cancer negatively impacts outcomes. Ann Surg Oncol 18:912–916. https://doi.org/10.1245/s10434-010-1487-0 Joseph K, Vrouwe S, Kamruzzaman A et al (2012) Outcome analysis of breast cancer patients who declined evidence-based treatment. World J Surg Oncol 10:118. https://doi.org/10.1186/1477-7819-10-118 Espie M (2014) The management of breast cancer. Diagn Interv Imaging 95:753–757. https://doi.org/10.1016/j.diii.2014.04.003 Grimm LJ, Avery CS, Hendrick E et al (2022) Benefits and risks of mammography screening in women ages 40 to 49 years. J Prim Care Community Health 13:627223406. https://doi.org/10.1177/21501327211058322 Shao N, Xie C, Shi Y et al (2019) Comparison of the 7th and 8th edition of american joint committee on cancer (ajcc) staging systems for breast cancer patients: a surveillance, epidemiology and end results (seer) analysis. Cancer Manag Res 11:1433–1442. https://doi.org/10.2147/CMAR.S185212 Zhou J, Lei J, Wang J et al (2020) Validation of the 8(th) edition of the american joint committee on cancer pathological prognostic staging for young breast cancer patients. Aging 12:7549–7560. https://doi.org/10.18632/aging.103111 Zhu H, Dogan BE (2021) American joint committee on cancer's staging system for breast cancer, eighth edition: summary for clinicians. Eur J Breast Health 17:234–238. https://doi.org/10.4274/ejbh.galenos.2021.2021-4-3 Tang L, Ishikawa Y, Matsushita H et al (2020) Prognostic value of the ajcc 8th edition staging system for japanese patients treated with surgery followed by radiotherapy for breast cancer. Int J Clin Oncol 25:1499–1505. https://doi.org/10.1007/s10147-020-01686-x Kim EJ, Park HS, Kim JY et al (2020) Assessment of the prognostic staging system of american joint committee on cancer 8th edition for breast cancer: comparisons with the conventional anatomic staging system. J Breast Cancer 23:59–68. https://doi.org/10.4048/jbc.2020.23.e9 Wang Z, Xing L, Luo X et al (2022) A nomogram for survival prediction in 275,812 u.s. Patients with breast cancer: a population-based cohort study based on the seer database. Gland Surg 11:1166–1179. https://doi.org/10.21037/gs-22-321 Fleurier C, De Wit A, Pilloy J et al (2020) Outcome of patients with breast cancer in the oldest old (>/=80 years). Eur J Obstet Gynecol Reprod Biol 244:66–70. https://doi.org/10.1016/j.ejogrb.2019.11.009 Xie Y, Deng Y, Wei S et al (2023) Age has a u-shaped relationship with breast cancer outcomes in women: a cohort study. Front Oncol 13:1265304. https://doi.org/10.3389/fonc.2023.1265304 DeSantis CE, Ma J, Gaudet MM et al (2019) Breast cancer statistics, 2019. CA Cancer J Clin 69:438–451. https://doi.org/10.3322/caac.21583 Chen W, Zheng R, Baade PD et al (2016) Cancer statistics in china, 2015. CA Cancer J Clin 66:115–132. https://doi.org/10.3322/caac.21338 Houterman S, Janssen-Heijnen ML, Verheij CD et al (2004) Comorbidity has negligible impact on treatment and complications but influences survival in breast cancer patients. Br J Cancer 90:2332–2337. https://doi.org/10.1038/sj.bjc.6601844 Zhao X, Yang X, Fu L et al (2021) Associations of estrogen receptor, progesterone receptor, human epidemic growth factor receptor-2 and ki-67 with ultrasound signs and prognosis of breast cancer patients. Cancer Manag Res 13:4579–4586. https://doi.org/10.2147/CMAR.S276422 Kwak Y, Jang SY, Choi JY et al (2023) Progesterone receptor expression level predicts prognosis of estrogen receptor-positive/her2-negative young breast cancer: a single-center prospective cohort study. Cancers (Basel) 15. https://doi.org/10.3390/cancers15133435 Yamashita H, Iwase H, Toyama T et al (2011) Estrogen receptor-positive breast cancer in japanese women: trends in incidence, characteristics, and prognosis. Ann Oncol 22:1318–1325. https://doi.org/10.1093/annonc/mdq596 Volpi A, Bacci F, Paradiso A et al (2004) Prognostic relevance of histological grade and its components in node-negative breast cancer patients. Mod Pathol 17:1038–1044. https://doi.org/10.1038/modpathol.3800161 Rakha EA, Reis-Filho JS, Baehner F et al (2010) Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res 12:207. https://doi.org/10.1186/bcr2607 Soler-Vila H, Kasl SV, Jones BA (2003) Prognostic significance of psychosocial factors in african-american and white breast cancer patients: a population-based study. Cancer-Am Cancer Soc 98:1299–1308. https://doi.org/10.1002/cncr.11670 Jiao D, Ma Y, Zhu J et al (2022) Impact of marital status on prognosis of patients with invasive breast cancer: a population-based study using seer database. Front Oncol 12:913929. https://doi.org/10.3389/fonc.2022.913929 Martinez ME, Unkart JT, Tao L et al (2017) Prognostic significance of marital status in breast cancer survival: a population-based study. PLoS ONE 12:e175515. https://doi.org/10.1371/journal.pone.0175515 Zhai Z, Zhang F, Zheng Y et al (2019) Effects of marital status on breast cancer survival by age, race, and hormone receptor status: a population-based study. Cancer Med 8:4906–4917. https://doi.org/10.1002/cam4.2352 Williams K, Umberson D (2004) Marital status, marital transitions, and health: a gendered life course perspective. J Health Soc Behav 45:81–98. https://doi.org/10.1177/002214650404500106 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4108112","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280216915,"identity":"b21aa04b-086c-41f1-997f-0980f91f3dcd","order_by":0,"name":"Dasong Wang","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dasong","middleName":"","lastName":"Wang","suffix":""},{"id":280216916,"identity":"e7a70aca-f121-45f4-ba76-325e6d324d06","order_by":1,"name":"Yan Yang","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Yang","suffix":""},{"id":280216917,"identity":"44f3a46c-7bab-4470-a58f-c484ad8f43b7","order_by":2,"name":"Hongwei Yang","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Yang","suffix":""},{"id":280216918,"identity":"e79e24ec-897f-4362-822e-6976563b5939","order_by":3,"name":"Lei Yang","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Yang","suffix":""},{"id":280216919,"identity":"3a899e43-8add-478c-bd6b-1eb43be460ae","order_by":4,"name":"Maoshan Chen","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Maoshan","middleName":"","lastName":"Chen","suffix":""},{"id":280216920,"identity":"c4d1ebbc-41af-4146-9ba5-9e4bb20f848f","order_by":5,"name":"Li Fan","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Fan","suffix":""},{"id":280216921,"identity":"0eed501c-f4d2-4815-b18a-1858b4f9058f","order_by":6,"name":"Yu He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBCDBAb2xsaHH0jTwnO42ViCNC0S6W0CPMQolZ/d/vgzb9vhPIObD9sYJBjs5HQbCGgxuHPGwHBm2+Fig9uJbQ8KGJKNzQ4Q0iKRw5Dwse1w4obbie0GEgwHErcR0iI/I/3BgUSQlpsH2yR4iNHCcCPBsAFsyw1GIrUY3MgxZpxxLj1x5plEYCAbEOEXoMMef+Yps07sO3784cMPFXZyBLWAASNbM4MCWKUBMcrB4E8dg3wD0apHwSgYBaNgpAEAtDlLp+WJvNgAAAAASUVORK5CYII=","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2024-03-15 13:12:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4108112/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4108112/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53015076,"identity":"250f14e3-62ba-4d82-a57b-a5acb6eb074a","added_by":"auto","created_at":"2024-03-19 15:55:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":320554,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart detailing the selection of the patients in this study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4108112/v1/22d58b64cfd466ef5d01bf5f.png"},{"id":53015079,"identity":"4f6c9657-6bc3-4ed6-bee8-e5cf7cfd3c2b","added_by":"auto","created_at":"2024-03-19 15:55:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":206218,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curves for breast cancer patients in treated and untreated groups. A: overall survial before PSM, B: overall survial after PSM, C: cancer specific survival before PSM, D: cancer specific survival after PSM.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4108112/v1/43cc52f5e1b953e5379447af.png"},{"id":53016026,"identity":"88001eb0-e9b8-45b1-b0c4-3e340d8a96d8","added_by":"auto","created_at":"2024-03-19 16:03:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":333467,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curves of untreated breast cancer patients at different stages (A: overall survival, B: cancer specific survival).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4108112/v1/39b71dca98e105b6d0be5ed8.png"},{"id":53015077,"identity":"18f55fd5-e5c4-4175-ab13-e359b1102ce0","added_by":"auto","created_at":"2024-03-19 15:55:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61823,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram predicting the 5- and 10-year overall survival of untreated group of patients with breast cancer.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4108112/v1/7d2543de7f14e6b90d400c73.png"},{"id":53015080,"identity":"c508119b-5aab-4ff4-9c26-ddd3d676fb56","added_by":"auto","created_at":"2024-03-19 15:55:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":281353,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of the nomogram for predicting overall survival in training (A, 5-year; C, 10-year) and validation (B, 5-year; D, 10-year) sets.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4108112/v1/af5033ab6c0e9164ac7513fc.png"},{"id":53016027,"identity":"f330f0cd-d8d8-4fac-9538-20a2ed25de55","added_by":"auto","created_at":"2024-03-19 16:03:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":214614,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots of the nomogram for predicting overall survival in training (A, 5-year; C, 10-year) and validation (B, 5-year; D, 10-year) sets.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4108112/v1/f82428a8e3ff9a79b722fbee.png"},{"id":53015082,"identity":"5956ef9b-9595-466d-960b-d81ff2277923","added_by":"auto","created_at":"2024-03-19 15:55:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":213026,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for the Nomogram and AJCC TNM stage system in prediction of overall survival in training (A, 5-year; C, 10-year) and validation (B, 5-year; D, 10-year) sets.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4108112/v1/83e877361a501d9e7c2b5cdd.png"},{"id":53265726,"identity":"3bb6177c-eef2-49a6-bd80-c36c1c75dacb","added_by":"auto","created_at":"2024-03-22 15:37:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1241341,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4108112/v1/3a6be052-a6e3-4001-aaa9-cc6a2d0b9097.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Natural history and prognostic model of untreated breast cancer: a study based on the SEER database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most common malignant tumor among women worldwide, accounting for 25\u0026ndash;30% of all new cancer cases in women globally. It is also a leading cause of cancer-related deaths among women, contributing to about 15% of all cancer deaths[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In 2020, approximately 2.3\u0026nbsp;million women were newly diagnosed with breast cancer worldwide, and about 685,000 deaths were attributed to the disease[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The incidence of breast cancer varies by region but is increasing in almost all areas of the world[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Breast cancer generally has a favorable prognosis and a relatively long survival period. In recent years, the survival rate for breast cancer has significantly improved due to advancements in treatment techniques and the widespread availability of screening[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the 1970s, the 5-year cancer-specific survival rate for breast cancer patients was about 75%, but by 2015, this figure had risen to approximately 90%[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, even with the continuous improvement in breast cancer prognoses, a minority of patients remain untreated due to lack of medical access or cultural biases[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The prognosis for untreated breast cancer patients is significantly worse. In most studies reported, breast cancer patients who receive no treatment only survive for 3\u0026ndash;4 years, and only 5\u0026ndash;10% of untreated patients live longer than 10 years[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe natural history of a disease refers to the natural behavior of the disease when left untreated[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For breast cancer, studying its natural history is beneficial for enhancing understanding of the disease and for learning about its natural progression in the absence of treatment intervention. It also helps to understand the impact of the disease on the lifespan of the affected population[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, knowledge of the natural history of the disease can inform the benefits of certain treatment options, serving as a foundation for further clinical research to evaluate the effectiveness of these treatments[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Currently, research on the natural history of breast cancer is limited, with most conclusions drawn from small-sample, retrospective studies, which offer limited guidance for clinical practice and lack effective tools for predicting survival times in untreated patients[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Under the current diagnostic and treatment system, standardized treatment is generally recommended for breast cancer patients without significant contraindications upon diagnosis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, studying the natural history of breast cancer requires a large number of patients diagnosed with the disease who have not undergone any treatment, along with a sufficiently long follow-up period. This is not in line with current standards for breast cancer diagnosis and treatment, making it almost impossible to conduct large-scale clinical trials to study its natural history in clinical practice. The National Cancer Institute\u0026rsquo;s Surveillance, Epidemiology, and End Results (SEER) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://seer.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://seer.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a large public database that covers about 28% of the U.S. population, providing clinical and pathological information about patient demographics, tumor characteristics, diagnosis, treatment, and prognosis[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The SEER database includes a significant number of untreated breast cancer patients and possesses relatively complete follow-up data and sufficiently long follow-up periods. Therefore, in this study, we utilize the SEER database to statistically analyze the natural histroy of untreated breast cancer, study the factors affecting breast cancer prognosis, and construct a prognostic model to guide clinical diagnosis and treatment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eData source and study population\u003c/p\u003e\n\u003cp\u003eWe obtained data from the National Cancer Institute\u0026rsquo;s SEER program. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All information from the SEER program is available and free for public, so the agreement of the medical ethics committee board was not necessary. Moreover, due to the strict register-based nature of the study, informed consent was waived. Patient data from the SEER database (Version:8.4.1, https://seer.cancer.gov/data-software/), SEER Research Data, 17 registries, Nov 2022 Sub (2000-2020) were screened. Patients with newly diagnosed breast cancer from 2010 to 2020 were initially enrolled in this study. The including and excluding criteria of patients with breast cancer were as follows. A schematic representation of the patient screening process can be observed in Figure 1.\u003c/p\u003e\n\u003cp\u003eInclusion criteria:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePathologically diagnosed with invasive breast cancer.\u003c/li\u003e\n \u003cli\u003eDiagnosis year between 2010 and 2020.\u003c/li\u003e\n \u003cli\u003eAge at diagnosis over 18 years.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExclusion criteria:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eNot the only primary malignant tumor.\u003c/li\u003e\n \u003cli\u003eFollow-up time less than one month or follow-up incomplete.\u003c/li\u003e\n \u003cli\u003eIncomplete follow-up data.\u003c/li\u003e\n \u003cli\u003eSpecial pathological types of malignant breast tumors.\u003c/li\u003e\n \u003cli\u003eMale patients.\u003c/li\u003e\n \u003cli\u003eUnknown tumor size or tumor stage.\u003c/li\u003e\n \u003cli\u003eUnknown treatment information.\u003c/li\u003e\n \u003cli\u003eUnknown histologic grade/ estrogen receptor (ER)/ progesterone receptor (PR)/ human epidermal growth factor receptor-2 (HER2) status.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eVariable assessment\u003c/p\u003e\n\u003cp\u003eThe treatment information of breast cancer contained in the SEER database includes surgery, chemotherapy and radiotherapy. Considering these treatment modalities are the most important and common for breast cancer, patients who did not receive surgery, radiotherapy, or chemotherapy were defined as the untreated group in this study. Based on the inclusion and exclusion criteria, a total of 367,175 breast cancer patients were included in the analysis. Of these, 357,768 patients received treatment, while 9,407 patients did not undergo any treatment. Demographic parameters included age at diagnosis (categorized as \u0026le;50 years, 51-70 years, \u0026gt;70 years), race (including White, Black, Other), and marital status (including married, unmarried). Clinical and pathological parameters included tumor size (categorized as 0-20 mm, 21-50 mm, \u0026gt;50 mm), tumor location (categorized as central, medial, lateral, other), laterality (including left, right), histological type (including invasive ductal carcinoma, invasive lobular carcinoma, mixed ductal and lobular carcinoma), histological grade (including I, II, III), tumor stage (adjusted to the 7th edition American Joint Committee on Cancer (AJCC) staging system, including I, II, III, IV), T stage (including T1, T2, T3, T4), N stage (including N0, N1, N2, N3), M stage (including M0, M1), ER status (including positive, negative), PR status (including positive, negative), HER2 status (including positive, negative), and subtype (including HR+/HER2-, HR+/HER2+, HR-/HER2+, and HR-/HER2-).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSurvival analysis\u003c/p\u003e\n\u003cp\u003eThe primary endpoint of this study was the overall survival of breast cancer patients, defined as the time from the diagnosis date of breast cancer to the patient\u0026apos;s death. Median survival time (MST) was defined as the time at which 50% of patients reach the survival endpoint. The progression time of the disease could be estimated by subtracting the MST of a more severe stage from its preceding stage. In this study, we used the estimated disease progression time to assess the natural history of breast cancer. Additionally, kaplan-Meier survival curves and survival rates are also used to compare the survival differences among breast cancers with different characteristics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConstruction and validation of the nomogram\u003c/p\u003e\n\u003cp\u003eTo construct and validate the nomogram, the untreated patients were randomly divided into training and validation sets in a ratio of 7:3. Univariate cox regression was used to identify risk factors associated with the prognosis of the untreated patients, and statistically significant variables were included in multivariate cox regression analysis. Finally, variables with p\u0026lt;0.05 in multivariate cox regression were determined as independent risk factors and used to construct a nomogram for predicting the prognosis of the untreated patients with breast cancer. The concordance index (C-index) and the receiver operating characteristic curve (ROC) were used to evaluate the discriminative ability of the nomogram. The calibration curves were used to compare the association between the actual outcomes and predicted probabilities. The clinical usefulness and benefits of the predictive model were estimated through decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eThe chi-square test or Fisher exact test was performed for comparison of categorical data, and t test was used for comparison of continuous variables. Cumulative survival time was calculated using the Kaplan-Meier method, and the differences in survival curves were analyzed using the log-rank test. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify variables that significantly affected the prognosis of the untreated patients with breast cancer in the training set. All statistical analyses were performed using Stata15.0 Software. P values \u0026lt;0.05 denoted statistically significant differences.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eClinical and pathological characteristics\u003c/p\u003e\n\u003cp\u003eThis study included a total of 367,175 patients diagnosed with breast cancer between 2010 and 2020. Among them, 357,768 patients received treatment, while 9,407 patients did not receive any treatment. In the entire patient cohort, the median age was 60 years, with the majority being white (78.01%) and married (56.90%). In terms of clinical and pathological characteristics, the majority of patients (58.78%) had tumor sizes less than 20mm. The most common tumor location was the outer quadrant of the breast, accounting for 43.32% of all patients. Invasive ductal carcinoma was the most common histological type, representing 84.67% of all patients. Histological grade II was observed in 45.60% of all patients. The distribution of tumors in the left and right breasts was nearly equal. Most patients were diagnosed at stage I (57.86%) or stage II (28.62%). The untreated group had later-stage tumors compared to the treated group. During the follow-up period of the study, a total of 44,190 (12.04%) patients died, including 39,727 (11.10%) in the treated group and 4,463 (47.44%) in the untreated group. There was a significant difference in survival rates between the treated and untreated groups. The patients in the treated and untreated groups exhibited significant differences in the distribution of demographic and clinical-pathological characteristics. Therefore, propensity score matching (PSM) was performed to compare the impact of treatment factors on the prognosis of breast cancer patients. The demographic and clinical distributions for pre- and post-PSM groups can be found in Tables 1 and 2, respectively.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eTable1 \u0026nbsp; Demographic and clinicopathological characteristics of breast cancer patients before PSM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003eAll patients(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003eTreated group(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003eUntreated group(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e367175(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e357768(97.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e9407(2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e93877(25.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e92567(25.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1310(13.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e51-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e191465(52.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e188092(52.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3373(35.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e81833(22.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e77109(21.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e4724(50.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e286436(78.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e279454(78.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e6982(74.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e38563(10.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e37243(10.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1320(14.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e42176(11.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e41071(11.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1105(11.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e208916(56.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e205714(57.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3202(34.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e158259(43.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e152054(42.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e6205(65.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eTumorsize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e215837(58.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e212528(59.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3309(35.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e21-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e122607(33.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e118303(33.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e4304(45.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e28731(7.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e26937(7.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1794(19.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ecentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e17177(4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e16543(4.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e634(6.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003einner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e68144(18.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e66687(18.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1427(15.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eouter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e159074(43.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e155614(43.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3460(36.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eOther*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e122810(33.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e118924(33.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3886(41.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003elaterality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eleft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e186291(50.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e181400(50.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e4891(51.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eright\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e180884(49.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e176368(49.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e4516(48.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHistology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e310889(84.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e303045(84.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e7844(83.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eILC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e35435(9.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e35258(9.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1177(12.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIDLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e19851(5.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e19465(5.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e386(4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e80699(21.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e78870(22.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1829(19.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e167433(45.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e162624(45.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e4809(51.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e119043(32.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e116274(32.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2769(29.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAJCC stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e212461(57.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e209318(58.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3143(33.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e105097(28.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e102233(28.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2864(30.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e35884(9.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e34882(9.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1002(10.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e13733(3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e11335(3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2398(25.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e214727(58.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e211557(59.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3170(33.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e117894(32.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e114114(31.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3780(40.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e23197(6.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e22030(6.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1167(12.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e11357(3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e10067(2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1290(13.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e249187(67.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e243499(68.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e5688(60.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e89403(24.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e86340(24.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3063(32.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e17347(4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e16990(4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e357(3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e11238(3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e10939(3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e299(3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eM stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e353442(96.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e346433(96.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e7009(74.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e13733(3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e11335(3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2398(25.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eER status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e307949(83.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e299812(83.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e8137(86.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e59226(16.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e57956(16.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1270(13.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePR status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e270291(73.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e263267(73.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e7024(74.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e96884(26.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e94501(26.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2383(25.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHer2 status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e56507(15.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e55071(15.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1436(15.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e310668(84.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e302697(84.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e7971(84.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eSubtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHR+/HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e271192(73.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e264009(73.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e7183(76.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHR+/HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e40464(11.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e39402(11.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1062(11.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHR-/HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e16043(4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e15669(4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e374(3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHR-/HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e39476(10.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e38688(10.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e788(8.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e346017(94.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e346017(96.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e21158(5.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e11751(3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e9407(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eRadiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e221660(60.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e221660(61.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e145515(39.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e136108(38.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e9407(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e155745(42.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e155745(43.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e211430(57.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e202023(56.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e9407(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e322985(87.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e318041(88.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e4944(52.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e44190(12.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e39727(11.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e4463(47.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eCSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAlive/dead of other cause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e341396(92.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e334961(93.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e6435(68.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eDead of breast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e25779(7.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e22807(6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2972(31.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eOther*: axillary and overlapping of the breast; IDC = invasive ductal cacinoma; ILC = invasive lobular carcinoma; IDLC =invasive duct and lobular carcinoma; AJCC, American Joint Committee on Cancer; ER = estrogen receptor; PR = progesterone receptor; HER2 = human epidermal growth factor receptor type 2; OS, overall survival; CSS, cancer specific survival\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eTable2 \u0026nbsp; Demographic and clinicopathological characteristics of breast cancer patients after PSM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003eAll patients(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003eTreated group(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003eUntreated group(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e14968(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e7484(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e7484(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e2130(14.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e1065(14.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1065(14.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e51-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e5554(37.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e2777(37.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2777(37.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e7284(48.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e3642(48.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3642(48.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e11600(77.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e5800(77.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e5800(77.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e1810(12.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e905(12.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e905(12.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e1558(10.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e779(10.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e779(10.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e5166(34.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e2583(34.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2583(34.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e9802(65.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e4901(65.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e4901(65.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eTumorsize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e5912(39.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e2956(39.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2956(39.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e21-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e6882(45.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e3441(45.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3441(45.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e2174(14.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e1087(14.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1087(14.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ecentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e732(4.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e366(4.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e366(4.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003einner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e2208(14.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e1104(14.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1104(14.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eouter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e5806(38.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e2903(38.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2903(38.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eOther*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e6222(41.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e3111(41.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3111(41.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003elaterality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eleft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e7756(51.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e3878(51.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3878(51.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eright\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e7212(48.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e3606(48.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3606(48.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHistology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e12882(86.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e6441(86.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e6441(86.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eILC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e1624(10.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e812(10.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e812(10.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIDLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e462(3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e231(3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e231(3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e2190(19.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e1455(19.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1455(19.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e7810(52.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e3905(52.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3905(52.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e4248(28.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e2124(28.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2124(28.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAJCC stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e6178(41.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e3089(41.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3089(41.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e5236(34.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e2618(34.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2618(34.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e1238(8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e619(8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e619(8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e2316(15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e1158(15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1158(15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e5848(39.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e2924(39.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2924(39.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e6434(42.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e3217(42.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3217(42.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e1502(10.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e751(10.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e751(10.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e1184(7.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e592(7.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e592(7.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e9964(66.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e4982(66.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e4982(66.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e4408(29.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e2204(29.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e2204(29.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e306(2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e153(2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e153(2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e290(1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e145(1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e145(1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eM stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e12652(84.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e6326(84.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e6326(84.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e2316(15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e1158(15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1158(15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eER status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e13132(87.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e6566(87.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e6566(87.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e1836(12,27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e918(12.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e918(12.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePR status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e11616(77.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e5808(77.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e5808(77.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e3352(22.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e1676(22.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1676(22.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHer2 status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e1902(12.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e951(12.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e951(12.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e13066(87.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e6533(87.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e6533(87.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eSubtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHR+/HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e11824(79.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e5912(79.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e5912(79.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHR+/HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e1384(9.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e692(9.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e692(9.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHR-/HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e518(3.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e259(3.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e259(3.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHR-/HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e1242(8.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e621(8.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e621(8.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e6688(44.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e6688(89.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e8280(55.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e796(10.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e7484(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eRadiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e4087(27.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e4087(54.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e10881(72.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e3397(45.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e7484(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e2792(18.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e2792(37.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e12176(81.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e4692(62.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e7484(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e9505(63.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e5206(69.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e4299(57.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e5463(36.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e2278(30.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e3185(43.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eCSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAlive/dead of other cause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e11832(79.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e6287(84.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e5545(74.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eDead of breast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.280701754385966%\" valign=\"top\"\u003e\n \u003cp\u003e3136(20.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e1197(15.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e1939(25.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eOther*: axillary and overlapping of the breast; IDC = invasive ductal cacinoma; ILC = invasive lobular carcinoma; IDLC =invasive duct and lobular carcinoma; AJCC, American Joint Committee on Cancer; ER = estrogen receptor; PR = progesterone receptor; HER2 = human epidermal growth factor receptor type 2; OS, overall survival; CSS, cancer specific survival\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eImpact of treatment on the prognosis of breast cancer patients\u003c/p\u003e\n\u003cp\u003eIn this study, 9,407 (2.56%) patients did not receive any treatment, 346,017 (94.24%) patients underwent surgical treatment, 221,660 (60.37%) patients received radiation therapy, and 155,745 (42.42%) patients underwent chemotherapy. Regardless of before or after PSM, treatment was a crucial factor affecting the prognosis of breast cancer patients. As shown in Figure 2, the treated group had higher overall survival rates and cancer-specific survival rates compared to the untreated group. As shown in Table 3, in the subgroup analysis after PSM, the treated group had better prognosis in different tumor stages compared to the untreated group.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTable 3 Ten-year overall survival rate after PSM in treated and untreated groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTreated(N%, 95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eUntreated(N%, 95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e61.73(60.34-63.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e27.40(25.14-29.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eAJCC stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e78.22(76.27-80.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e41.75(37.29-46.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e62.48(60.15-64.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e25.83(22.41-29.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e46.62(41.44-51.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e15.43(9.88-22.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e14.00(10.65-17.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e7.82(4.94-11.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e76.96(75.08-78.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e39.64(35.62-43.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e54.55(52.21-56.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e23.78(20.52-27.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e47.31(42.24-52.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e14.11(9.00-20.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e22.28(17.40-27.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e4.77(1.34-11.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e68.94(67.32-70.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e32.44(29.41-35.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e48.70(45.92-51.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e18.53(15.28-22.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e30.48(20.69-40.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNA*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e22.90(15.24-31.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNA*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eM stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e68.60(67.15-70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e31.48(28.87-34.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e14.00(10.65-17.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e7.82(4.94-11.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eNA* There was not enough sample size for analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eNatural progression of untreated breast cancer patients\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival analysis was conducted in the untreated group of patients. As shown in Figure 3, the prognosis of untreated patients worsened with increasing stages. In all untreated patients, the median overall survival time was 47 months (95% CI: 46-49), with median survival times of 94 months (95% CI: 85-104) for stage I, 51 months (95% CI: 47-55) for stage II, 33 months (95% CI: 29-37) for stage III, and 24 months (95% CI: 22-26) for stage IV. The development of breast cancer typically starts with genetic mutations in cells, gradually progressing to early-stage breast cancer and further advancing to intermediate and late-stage breast cancer without treatment intervention[14]. In this study, we estimated the progression time of breast cancer by calculating the difference in median survival time between patients with lower-stage breast cancer and those with higher-stage breast cancer. As shown in Table 4, the progression times from stage I to II, from II to III, and from III to IV were 43 months, 18 months, and 9 months, respectively.\u003c/p\u003e\n\u003cp\u003eFactors related to the prognosis of untreated breast cancer patients\u003c/p\u003e\n\u003cp\u003eIn the case of untreated breast cancer patients, 6,583 cases were randomly assigned to the training set, and 2,824 cases to the validation set. There was no significant difference in the distribution of demographic and clinicopathologic characteristics between the training and validation sets, indicating comparability between the two groups. In the training set, variables related to the prognosis of breast cancer patients were assessed using the log-rank test and univariate Cox regression analysis. The results showed that age, race, marital status, tumor size, tumor location, histological grade, tumor stage, T stage, N stage, M stage, ER status, PR status, HER2 status, and molecular subtype were associated with the overall survival of breast cancer. All variables with statistical significance in the univariate analysis were included in a multivariate Cox regression model, which revealed that age, marital status, histological grade, tumor stage, ER status, and PR status were independent predictive factors affecting the overall survival of breast cancer patients. The results are presented in Table 5.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTable 4\u0026nbsp;Median survival time and disease progression time in untreated patients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.992970123022847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.101933216168717%\" valign=\"top\"\u003e\n \u003cp\u003ePatients at risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.579964850615113%\" valign=\"top\"\u003e\n \u003cp\u003eMedian survival time(months,95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.32513181019332%\" valign=\"top\"\u003e\n \u003cp\u003eProgression time(months)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.992970123022847%\" valign=\"top\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.101933216168717%\" valign=\"top\"\u003e\n \u003cp\u003e8842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.579964850615113%\" valign=\"top\"\u003e\n \u003cp\u003e47(46-49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.32513181019332%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.992970123022847%\" valign=\"top\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.101933216168717%\" valign=\"top\"\u003e\n \u003cp\u003e3008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.579964850615113%\" valign=\"top\"\u003e\n \u003cp\u003e94(85-104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.32513181019332%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.992970123022847%\" valign=\"top\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.101933216168717%\" valign=\"top\"\u003e\n \u003cp\u003e2761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.579964850615113%\" valign=\"top\"\u003e\n \u003cp\u003e51(47-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.32513181019332%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.992970123022847%\" valign=\"top\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.101933216168717%\" valign=\"top\"\u003e\n \u003cp\u003e961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.579964850615113%\" valign=\"top\"\u003e\n \u003cp\u003e33(29-37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.32513181019332%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.992970123022847%\" valign=\"top\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.101933216168717%\" valign=\"top\"\u003e\n \u003cp\u003e2112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.579964850615113%\" valign=\"top\"\u003e\n \u003cp\u003e24(22-26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.32513181019332%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eTable5\u0026nbsp;Univariate and multivariate cox regression analysis (Training Cohort).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e51-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.37(1.21-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.43(1.23-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e2.75(2.46-3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e3.10(2.70-3.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.10(1.01-1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.07(0.97-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.65(0.58-0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.75(0.48-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003ereference\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.51(1.41-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e1.33(1.22-1.44)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eTumorsize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e21-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.78(1.66-1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.78(0.60-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e2.46(2.26-2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.85(0.65-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ecentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003einner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.63(0.55-0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.76(0.56-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eouter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.72(0.64-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.87(0.76-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eOther*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.80(0.71-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.87(0.76-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003elaterality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eleft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eright\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.95(0.89-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHistology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eILC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.06(0.97-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIDLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.88(0.76-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.46(1.33-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e1.14(1.02-1.27)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e0.019\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e2.01(1.83-2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e1.36(1.20-1.53)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAJCC stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003ereference\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.82(1.67-1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e1.58(1.42-1.77)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e2.86(2.56-3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e2.40(2.10-2.74)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e3.87(3.55-4.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e3.89(3.49-4.33)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.78(1.65-1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.12(0.98-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e2.33(2.11-2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.13(0.95-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e3.03(2.76-3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.30(0.79-2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.70(1.59-1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.02(0.93-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e2.20(1.91-2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.03(0.86-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.99(1.70-2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.89(0.73-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eM stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e2.46(2.31-2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.44(0.93-2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eER status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003ereference\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1.76(1.62-1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e1.58(1.38-1.80)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePR status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003ereference\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.62(1.51-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e1.32(1.19-1.46)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(84, 172, 210);\"\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHer2 status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.80(0.74-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.04(0.93-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSubtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHR+/HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHR+/HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21(1.10-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97(0.57-1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHR-/HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.67(1.44-1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.10(0.75-1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHR-/HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.84(1.66-2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.30(0.77-2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eOther*: axillary and overlapping of the breast; IDC = invasive ductal cacinoma; ILC = invasive lobular carcinoma; IDLC =invasive duct and lobular carcinoma; AJCC, American Joint Committee on Cancer; ER = estrogen receptor; PR = progesterone receptor; HER2 = human epidermal growth factor receptor type 2; OS, overall survival; CSS, cancer specific survival.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eConstruction and validation of a nomogram\u003c/p\u003e\n\u003cp\u003eBy utilizing the independent risk factors affecting the prognosis of breast cancer patients identified through Cox regression, a Nomogram model was developed to predict the 5-year and 10-year overall survival rates for untreated breast cancer patients (Figure 4). The Nomogram model indicates that tumor stage has the greatest impact on the prognosis of breast cancer patients, followed by age. ER status has a more significant influence compared to PR status, while histological grade and marital status have relatively smaller effects. By adding up the scores of all prognostic indicators to calculate a total score, the 5-year and 10-year overall survival rates for each untreated breast cancer patient can be estimated.\u003c/p\u003e\n\u003cp\u003eTo assess the predictive accuracy of the nomogram, both the C-index and ROC curves were employed. The C-index of the nomogram was calculated as 0.7763(95% CI: 0.7655-0.7872) in the training set and 0.7857(95% CI: 0.7702-0.8012) in the validation set. The ROC curves of the nomogram (Figure 5) were consistent with the C-index, indicating excellent predictive ability of the models. As shown in Figure 6, the 95% CI of calibration belt in both training and validation sets hardly cross the diagonal bisector line. This observation indicated that the calibration plot demonstrates favorable concordance between the predicted probabilities and the actual observed outcomes. The DCA was applied to make comparisons of the availability and advantages between the nomogram model and AJCC TNM stage system (Figure 7). The findings indicate that the nomogram demonstrates greater practicality in predicting the prognosis of patients with breast cancer when compared to the TNM stage system.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe natural history of a disease refers to the disease's natural behavior in the absence of treatment. Studying the natural history of untreated cases is foundational for understanding any disease and serves as the real backdrop for evaluating the effectiveness of treatment modalities[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For breast cancer, researching its natural history means investigating the disease's changes without treatment intervention, which can be approached through various methods[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Describing and recording the clinical manifestation changes in breast cancer patients without treatment may be the most direct method of study. Most breast cancer patients present with a lump in the breast as the main clinical manifestation, typically appearing 3\u0026ndash;12 months before diagnosis. Usually, without treatment, the tumor will infiltrate the skin, leading to skin ulcers and fixation to the chest wall. If the patient survives, the tumor often invades the other breast and may lead to distant organ metastasis, with death typically occurring after evident metastasis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Describing the changes in clinical manifestations of breast cancer patients is an intuitive study method, but subjective descriptions can hardly reflect objective changes in the condition. Using imaging to document changes in the size of breast cancer lesions is a more objective method of study. In another study, researchers investigated the progression speed of breast cancer in untreated cases by comparing X-ray images of patients with delayed diagnosis[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The study showed that tumors in 94% of breast cancer patients gradually grew over time, with a median tumor volume doubling time of 385 days. The growth rate of tumors varied greatly, with histological grade and tumor size being positively correlated with tumor growth speed[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, this method of documenting changes in lesion size does not directly reflect the impact of disease progression on breast cancer patients. Some studies have recorded the survival time of breast cancer patients without treatment. In most reports, the majority of patients survived nearly 3\u0026ndash;4 years without any treatment, and a very few patients exhibited impressively good prognoses even without any treatment, with about 5\u0026ndash;10% of untreated breast cancer patients living for more than 10 years[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In our study, among all untreated breast cancer patients, the median survival time was 47 months (approximately 3.9 years), which is consistent with most previous reports; the estimated 10-year survival rate for all untreated patients was 22.68%, which is better than the previously reported 5\u0026ndash;10%. The difference might be partly due to improved screening methods leading to relatively earlier staging at diagnosis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Another consideration is that even if patients did not receive any anti-tumor treatment, the use of some nonspecific modern therapeutic drugs (such as antibiotics) could also prolong the survival time of some patients[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDescribing the survival time of untreated patients does not fully capture the natural history of breast cancer. Researching the natural history of breast cancer is not only about studying the survival time of untreated patients but also involves exploring the developmental changes of the disease itself. Tumor staging is one of the main factors affecting the prognosis of breast cancer patients and serves as an objective quantitative indicator of the severity of the patient's condition[20]. Breast cancer typically begins with genetic mutations in cells, gradually developing into early-stage breast cancer. Without treatment intervention, it will further progress to mid-stage and late-stage breast cancer[14]. Therefore, studying the time it takes for breast cancer patients to progress from one stage to the next without treatment can objectively and accurately reflect the changes in the condition of breast cancer, providing an objective and accurate method for researching the natural history of breast cancer[11]. In our study, we estimated the progression time of untreated breast cancer patients by comparing the median survival times of patients at different stages, thus illustrating the natural history of breast cancer. The study found that, in the absence of treatment, the progression time from stage I to II, from II to III, and from III to IV were 43 months, 18 months, and 9 months, respectively, with the disease's progression speed accelerating as the stage increases. This indicates that without treatment intervention, the progression speed of breast cancer is not constant; the progression of breast cancer exacerbates the tumor burden, and an increased tumor burden further accelerates the progression of breast cancer, leading to a vicious cycle of worsening conditions. This also reflects from another aspect that early treatment is key to improving the prognosis of breast cancer patients.\u003c/p\u003e \u003cp\u003eEvery breast cancer patient exhibits unique clinical and pathological characteristics, and descriptions of survival times for groups cannot accurately represent individual survival expectations[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To gain a deeper understanding of the natural history of breast cancer, we conducted further analysis on factors that could affect the prognosis of breast cancer. The results indicated that age, marital status, histological grade, tumor stage, ER status, and PR status are independent risk factors affecting the prognosis of breast cancer in the absence of treatment. Based on these risk factors, we constructed a nomogram model to predict the prognosis of untreated breast cancer. Validation within an internal group showed that the model has good predictive power and accuracy, demonstrating better accuracy and practicality compared to the currently widely used TNM tumor staging system. Additionally, the factors used to build the model are easily obtainable clinical and pathological factors, which can be applied in clinical practice for prognosis assessment and guiding the formulation of individualized treatment plans.\u003c/p\u003e \u003cp\u003eTumor staging is one of the primary indicators currently used to evaluate the prognosis of breast cancer patients. The American Joint Committee on Cancer's 8th edition of the Breast Cancer TNM Staging System is widely used for staging and prognostic assessment of breast cancer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Consistent with many previous studies, the prognosis of breast cancer patients worsens as the tumor stage advances[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our research has yielded similar results; the prognosis of breast cancer patients deteriorates with the progression of tumor stages, regardless of whether the patients have undergone anti-tumor treatment. This objective pattern serves as a crucial theoretical basis for our study on the natural history of breast cancer, allowing us to estimate the progression time of breast cancer and thus study its natural course.\u003c/p\u003e \u003cp\u003eAge is a significant factor affecting the prognosis of breast cancer patients[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A retrospective study examining the relationship between the age of breast cancer patients and their prognosis found a unique U-shaped association between diagnosis age and breast cancer prognosis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. That is, both young and elderly breast cancer patients tend to have relatively poorer prognoses. Among middle-aged and older breast cancer patients, both the overall survival rate and the cancer-specific survival rate decrease with age. However, it is noteworthy that young breast cancer patients, particularly those under 35, tend to have poorer outcomes, often presenting with larger breast tumors and a higher rate of axillary lymph node metastasis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The incidence of breast cancer also correlates with age, being more common in middle-aged and older women, with women over 50 accounting for 82% of new breast cancer diagnoses[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Based on the relationship between breast cancer incidence and age, our study divided patients into three age groups: \u0026le;50 years, 51\u0026ndash;70 years, and \u0026gt;\u0026thinsp;70 years, to minimize significant differences in numbers across age groups. The results revealed that the prognosis of untreated breast cancer patients worsened with increasing age, consistent with the general trend reported in previous studies. On one hand, older patients inherently have a shorter life expectancy and are more likely to have other fatal diseases, such as cardiovascular and respiratory diseases; on the other hand, they are also more prone to delayed diagnosis and treatment, which adversely affects their prognosis[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSteroid hormone receptors (ER, PR) and HER2 are currently essential indicators for molecular typing and prognostic assessment of breast cancer based on immunohistochemistry[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Assessing ER and PR status is crucial for identifying patient groups that benefit from endocrine therapy and for predicting prognosis. Patients with hormone receptor-positive breast cancer often exhibit milder clinical and pathological characteristics, such as later occurrence of lymph node metastasis and vascular invasion, and usually benefit from endocrine therapy, often having a better prognosis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our study found that even in untreated breast cancer patients, ER and PR status remain significant indicators affecting prognosis, with hormone receptor-positive patients having a better prognosis, possibly due to relatively milder clinical and pathological characteristics. Assessing HER2 status is important for identifying patients suitable for anti-HER2 targeted therapy and for predicting prognosis. HER2-positive breast cancer typically exhibits more aggressive clinical and pathological features and generally has a relatively poorer prognosis. However, anti-HER2 targeted therapy has significantly improved the prognosis of patients with HER2-positive breast cancer[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In our study, univariate analysis of factors affecting the prognosis of untreated breast cancer revealed that HER2-positive patients have a relatively poorer prognosis, but multivariate analysis showed that HER2 is not an independent risk factor for the prognosis of untreated breast cancer. Histological grading is an important factor affecting breast cancer prognosis. Multiple studies have shown that in invasive breast cancer, histological grade is definitively related to prognosis, with higher histological grades usually indicating more aggressive characteristics and leading to poorer outcomes[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In our study, we found that histological grade is also a crucial factor affecting the prognosis of untreated breast cancer, with prognosis worsening as histological grade increases.\u003c/p\u003e \u003cp\u003eSocial psychological factors are closely related to the prognosis of malignant tumors, and marital status is one of the most important social psychological factors influencing the occurrence and development of malignant tumors[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Studies have found that marital status is an independent risk factor affecting the prognosis of breast cancer patients. In subgroup analyses based on different stages, gender, and molecular subtypes, unmarried patients consistently shown a significantly higher risk of death compared to their married counterparts[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Married individuals often lead healthier lifestyles, including healthy diets, regular physical exercise, and routine health check-ups, which may be intermediary factors in cancer prevention[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, married individuals typically have better emotional well-being and receive more social support, including emotional and financial support, allowing them to focus on treatment and more easily recover from a solitary malignancy. Our study found similar results; marital status remained an independent risk factor affecting the prognosis of untreated breast cancer patients, with married patients having a significantly better prognosis than unmarried ones.\u003c/p\u003e \u003cp\u003eIn summary, the factors affecting the prognosis of untreated breast cancer patients are similar to the non-therapeutic factors reported in previous studies. In our study, the most significant factors impacting the prognosis of untreated breast cancer patients were the tumor stage at diagnosis and the age at diagnosis, followed by ER/PR status, histological grade, and marital status. Based on these risk factors, we have constructed a prognostic model that is more practical than the AJCC staging system, intended to guide clinical applications and practices.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has certain limitations that should be taken into consideration. As a retrospective study, it may be subject to selection bias, as some cases might have been excluded due to incomplete data. Secondly, certain factors potentially related to breast cancer prognosis, such as comorbidities of the patients, the degree of local invasion of the tumor, and Ki-67 levels, could not be analyzed due to the lack of these factors in the SEER database. Additionally, the absence of information on endocrine treatment for breast cancer in the SEER database might lead to inaccuracies in the definition of the untreated group in this study. Finally, since both the training and validation sets of the model are derived from the SEER database, there is a possibility of overfitting. External validation on other datasets is necessary to ensure the reproducibility of the model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTreatment is a key factor affecting the prognosis of breast cancer patients, with treated patients showing significantly better outcomes than untreated ones. In the absence of treatment, the progression of breast cancer accelerates with increasing tumor stages, and prognosis progressively worsens. Age, marital status, histological grade, tumor stage, ER status, and PR status are independent risk factors affecting the prognosis of untreated breast cancer patients. The predictive model constructed based on these factors demonstrates good predictive ability for the prognosis of untreated breast cancer patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSEER:Surveillance, Epidemiology, and End Results database; ER:estrogen receptor; PR: progesterone receptor; HER2: human epidermal growth factor receptor-2; MST: median survival time; C-index: concordance index; ROC: receiver operating characteristic curve; DCA: decision curve analysis; PSM: propensity score matching.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All information from the SEER program is available and free for public, so the agreement of the medical ethics committee board was not necessary. Moreover, due to the strict register-based nature of the study, informed consent was waived.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDasong Wang contributed to the study conception and design. Dasong Wang, Yan Yang and Yu He participate in writing proposal, analyzed the data, wrote the result and discussion. Dasong Wang, Hongwei Yang, Maoshan Chen, Li Fan and Lei Yang participate in analyzing the data, writing result and prepared manuscript. All authors commented on previous versions of the manuscript, and all authors read and approved the final manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Scientific research project of Suining Central Hospital (No. 2016y17).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank SEER for the open access to the database.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS et al (2023) Cancer statistics, 2023. CA Cancer J Clin 73:17\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21763\u003c/span\u003e\u003cspan address=\"10.3322/caac.21763\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209\u0026ndash;249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21660\u003c/span\u003e\u003cspan address=\"10.3322/caac.21660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoughton SC, Hankinson SE (2021) Cancer progress and priorities: breast cancer. Cancer Epidemiol Biomarkers Prev 30:822\u0026ndash;844. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1055-9965.EPI-20-1193\u003c/span\u003e\u003cspan address=\"10.1158/1055-9965.EPI-20-1193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiaquinto AN, Sung H, Miller KD et al (2022) Breast cancer statistics, 2022. CA Cancer J Clin 72:524\u0026ndash;541. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21754\u003c/span\u003e\u003cspan address=\"10.3322/caac.21754\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorre LA, Islami F, Siegel RL et al (2017) Global cancer in women: burden and trends. Cancer Epidemiol Biomarkers Prev 26:444\u0026ndash;457. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1055-9965.EPI-16-0858\u003c/span\u003e\u003cspan address=\"10.1158/1055-9965.EPI-16-0858\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerry DA, Cronin KA, Plevritis SK et al (2005) Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med 353:1784\u0026ndash;1792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa050518\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa050518\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKristeleit H, Parton M, Beresford M et al (2016) Long-term follow-up data from pivotal studies of adjuvant trastuzumab in early breast cancer. Target Oncol 11:579\u0026ndash;591. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11523-016-0438-5\u003c/span\u003e\u003cspan address=\"10.1007/s11523-016-0438-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEBCTCG (2015) Aromatase inhibitors versus tamoxifen in early breast cancer: patient-level meta-analysis of the randomised trials. Lancet 386:1341\u0026ndash;1352. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(15)61074-1\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(15)61074-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnstone PA, Norton MS, Riffenburgh RH (2000) Survival of patients with untreated breast cancer. J Surg Oncol 73:273\u0026ndash;277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/(sici)1096-9098(200004)73:4\u0026lt;273::aid-jso15\u0026gt;3.0.co;2-h\u003c/span\u003e\u003cspan address=\"10.1002/(sici)1096-9098(200004)73:4%3C273::aid-jso15%3E3.0.co;2-h\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalmarini CM, Tredan O, Galmarini FC (2015) Survivorship in untreated breast cancer patients. Med Oncol 32:466. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12032-014-0466-x\u003c/span\u003e\u003cspan address=\"10.1007/s12032-014-0466-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Liu H, Hu T et al (2022) The natural history of breast cancer: a chronological analysis of breast cancer progression using data from the seer database. Ann Transl Med 10:365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21037/atm-22-918\u003c/span\u003e\u003cspan address=\"10.21037/atm-22-918\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoibl S, Andre F, Bachelot T et al (2023) Early breast cancer: esmo clinical practice guideline for diagnosis, treatment and follow-up(dagger). Ann Oncol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.annonc.2023.11.016\u003c/span\u003e\u003cspan address=\"10.1016/j.annonc.2023.11.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu R, Xiao Z, Hu D et al (2021) Cancer-specific survival outcome in early-stage young breast cancer: evidence from the seer database analysis. Front Endocrinol (Lausanne) 12:811878. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fendo.2021.811878\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2021.811878\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrandberg R, Czene K, Eriksson M et al (2022) Estimating distributions of breast cancer onset and growth in a swedish mammography screening cohort. Cancer Epidemiol Biomarkers Prev 31:569\u0026ndash;577. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1055-9965.EPI-21-1011\u003c/span\u003e\u003cspan address=\"10.1158/1055-9965.EPI-21-1011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRojas KE, Manasseh DM, Rojas M et al (2020) The natural history of untreated estrogen receptor-positive, her2-negative invasive breast cancer. Breast Cancer Res Treat 182:79\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10549-020-05666-7\u003c/span\u003e\u003cspan address=\"10.1007/s10549-020-05666-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan E, Johnson N, DelaMelena T et al (2011) Alternative therapy used as primary treatment for breast cancer negatively impacts outcomes. Ann Surg Oncol 18:912\u0026ndash;916. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1245/s10434-010-1487-0\u003c/span\u003e\u003cspan address=\"10.1245/s10434-010-1487-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoseph K, Vrouwe S, Kamruzzaman A et al (2012) Outcome analysis of breast cancer patients who declined evidence-based treatment. World J Surg Oncol 10:118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1477-7819-10-118\u003c/span\u003e\u003cspan address=\"10.1186/1477-7819-10-118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEspie M (2014) The management of breast cancer. Diagn Interv Imaging 95:753\u0026ndash;757. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.diii.2014.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.diii.2014.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrimm LJ, Avery CS, Hendrick E et al (2022) Benefits and risks of mammography screening in women ages 40 to 49 years. J Prim Care Community Health 13:627223406. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/21501327211058322\u003c/span\u003e\u003cspan address=\"10.1177/21501327211058322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao N, Xie C, Shi Y et al (2019) Comparison of the 7th and 8th edition of american joint committee on cancer (ajcc) staging systems for breast cancer patients: a surveillance, epidemiology and end results (seer) analysis. Cancer Manag Res 11:1433\u0026ndash;1442. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/CMAR.S185212\u003c/span\u003e\u003cspan address=\"10.2147/CMAR.S185212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou J, Lei J, Wang J et al (2020) Validation of the 8(th) edition of the american joint committee on cancer pathological prognostic staging for young breast cancer patients. Aging 12:7549\u0026ndash;7560. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/aging.103111\u003c/span\u003e\u003cspan address=\"10.18632/aging.103111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu H, Dogan BE (2021) American joint committee on cancer's staging system for breast cancer, eighth edition: summary for clinicians. Eur J Breast Health 17:234\u0026ndash;238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4274/ejbh.galenos.2021.2021-4-3\u003c/span\u003e\u003cspan address=\"10.4274/ejbh.galenos.2021.2021-4-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang L, Ishikawa Y, Matsushita H et al (2020) Prognostic value of the ajcc 8th edition staging system for japanese patients treated with surgery followed by radiotherapy for breast cancer. Int J Clin Oncol 25:1499\u0026ndash;1505. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10147-020-01686-x\u003c/span\u003e\u003cspan address=\"10.1007/s10147-020-01686-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim EJ, Park HS, Kim JY et al (2020) Assessment of the prognostic staging system of american joint committee on cancer 8th edition for breast cancer: comparisons with the conventional anatomic staging system. J Breast Cancer 23:59\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4048/jbc.2020.23.e9\u003c/span\u003e\u003cspan address=\"10.4048/jbc.2020.23.e9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Xing L, Luo X et al (2022) A nomogram for survival prediction in 275,812 u.s. Patients with breast cancer: a population-based cohort study based on the seer database. Gland Surg 11:1166\u0026ndash;1179. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21037/gs-22-321\u003c/span\u003e\u003cspan address=\"10.21037/gs-22-321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleurier C, De Wit A, Pilloy J et al (2020) Outcome of patients with breast cancer in the oldest old (\u0026gt;/=80 years). Eur J Obstet Gynecol Reprod Biol 244:66\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejogrb.2019.11.009\u003c/span\u003e\u003cspan address=\"10.1016/j.ejogrb.2019.11.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Y, Deng Y, Wei S et al (2023) Age has a u-shaped relationship with breast cancer outcomes in women: a cohort study. Front Oncol 13:1265304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2023.1265304\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2023.1265304\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeSantis CE, Ma J, Gaudet MM et al (2019) Breast cancer statistics, 2019. CA Cancer J Clin 69:438\u0026ndash;451. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21583\u003c/span\u003e\u003cspan address=\"10.3322/caac.21583\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, Zheng R, Baade PD et al (2016) Cancer statistics in china, 2015. CA Cancer J Clin 66:115\u0026ndash;132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21338\u003c/span\u003e\u003cspan address=\"10.3322/caac.21338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHouterman S, Janssen-Heijnen ML, Verheij CD et al (2004) Comorbidity has negligible impact on treatment and complications but influences survival in breast cancer patients. Br J Cancer 90:2332\u0026ndash;2337. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/sj.bjc.6601844\u003c/span\u003e\u003cspan address=\"10.1038/sj.bjc.6601844\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao X, Yang X, Fu L et al (2021) Associations of estrogen receptor, progesterone receptor, human epidemic growth factor receptor-2 and ki-67 with ultrasound signs and prognosis of breast cancer patients. Cancer Manag Res 13:4579\u0026ndash;4586. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/CMAR.S276422\u003c/span\u003e\u003cspan address=\"10.2147/CMAR.S276422\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwak Y, Jang SY, Choi JY et al (2023) Progesterone receptor expression level predicts prognosis of estrogen receptor-positive/her2-negative young breast cancer: a single-center prospective cohort study. Cancers (Basel) 15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers15133435\u003c/span\u003e\u003cspan address=\"10.3390/cancers15133435\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamashita H, Iwase H, Toyama T et al (2011) Estrogen receptor-positive breast cancer in japanese women: trends in incidence, characteristics, and prognosis. Ann Oncol 22:1318\u0026ndash;1325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/annonc/mdq596\u003c/span\u003e\u003cspan address=\"10.1093/annonc/mdq596\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolpi A, Bacci F, Paradiso A et al (2004) Prognostic relevance of histological grade and its components in node-negative breast cancer patients. Mod Pathol 17:1038\u0026ndash;1044. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/modpathol.3800161\u003c/span\u003e\u003cspan address=\"10.1038/modpathol.3800161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRakha EA, Reis-Filho JS, Baehner F et al (2010) Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res 12:207. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/bcr2607\u003c/span\u003e\u003cspan address=\"10.1186/bcr2607\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoler-Vila H, Kasl SV, Jones BA (2003) Prognostic significance of psychosocial factors in african-american and white breast cancer patients: a population-based study. Cancer-Am Cancer Soc 98:1299\u0026ndash;1308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cncr.11670\u003c/span\u003e\u003cspan address=\"10.1002/cncr.11670\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiao D, Ma Y, Zhu J et al (2022) Impact of marital status on prognosis of patients with invasive breast cancer: a population-based study using seer database. Front Oncol 12:913929. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2022.913929\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2022.913929\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez ME, Unkart JT, Tao L et al (2017) Prognostic significance of marital status in breast cancer survival: a population-based study. PLoS ONE 12:e175515. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0175515\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0175515\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhai Z, Zhang F, Zheng Y et al (2019) Effects of marital status on breast cancer survival by age, race, and hormone receptor status: a population-based study. Cancer Med 8:4906\u0026ndash;4917. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cam4.2352\u003c/span\u003e\u003cspan address=\"10.1002/cam4.2352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams K, Umberson D (2004) Marital status, marital transitions, and health: a gendered life course perspective. J Health Soc Behav 45:81\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/002214650404500106\u003c/span\u003e\u003cspan address=\"10.1177/002214650404500106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, natural history, nomogram, SEER database","lastPublishedDoi":"10.21203/rs.3.rs-4108112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4108112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: The primary aim of this study was to explore the natural history of breast cancer in the absence of treatment, aiming to identify the main factors affecting the prognosis of untreated breast cancer patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Data for patients diagnosed with invasive breast cancer between 2010 and 2020 were obtained from the SEER database. The disease progression time was estimated by subtracting the median survival time of higher stages from that of lower stages. Untreated patients were divided into a training set and a validation set in a 7:3 ratio. In the training set, independent risk factors affecting prognosis were identified through univariate and multivariate cox proportional hazards regression analysis. A nomogram model was constructed using these risk factors to predict the prognosis of untreated patients, which was then validated in the validation set.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: In untreated breast cancer patients, the progression time from stage I to stage II was 43 months, from stage II to stage III was 18 months, and from stage III to stage IV was 9 months. Age, tumor stage, ER status, PR status, histological grade, and marital status were identified as independent predictive factors for the overall survival of untreated breast cancer patients, and a nomogram model was constructed with these factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The disease progression speed of breast cancer patients accelerates with the increase in tumor stage in the absence of treatment, and prognosis progressively worsens. Age, tumor stage, ER status, PR status, histological grade, and marital status are independent risk factors affecting the prognosis of untreated patients.\u003c/p\u003e","manuscriptTitle":"Natural history and prognostic model of untreated breast cancer: a study based on the SEER database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 15:55:31","doi":"10.21203/rs.3.rs-4108112/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc89c11a-0ba0-40d2-b60f-c6a287cb48cc","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-02T23:29:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-19 15:55:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4108112","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4108112","identity":"rs-4108112","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}