Novel Nomograms to Predict of Overall Survival and Cancer-specific Survival of Patients of Metaplastic Breast Cancer

Novel Nomograms to Predict of Overall Survival and Cancer-specific Survival of Patients of Metaplastic Breast Cancer | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Novel Nomograms to Predict of Overall Survival and Cancer-specific Survival of Patients of Metaplastic Breast Cancer Yongfeng Li, Daobao Chen, Haojun Xuan, Mihnea P. Dragomir, George A. Calin, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-96114/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 Background Metaplastic breast cancer (MBC) is a rare type of breast cancer with an increasing incidence, we aim to develop clinical nomograms to predict the overall survival and cancer-specific survival for patients with MBC. Methods Patients data were collected from the SEER database between 1973 and 2015. All included patients were randomly assigned into the training and validation sets. Univariate and multivariate Cox analysis were performed to identify independent prognostic factors of MBC. These essential prognostic variables were combined to construct nomogram models to predict overall survival (OS) and cancer-specific survival (CSS) in patients with MBC. Model performance was evaluated by concordance index (C-index) and calibration plots. Results A total of 1835 patients were collected and divided into the training (1223) and validation (612) groups. The multivariate Cox model identified age, TNM stage, T stage, and N stage, chemotherapy and radiotherapy as independent covariates associated with OS, while these variables except for age and chemotherapy were independent prognostic factors of CSS. The nomogram constructed based on these covariates demonstrated excellent accuracy in estimating 3-, and 5-year OS and CSS, with a C-index of 0.759 (95% CI, 0.746-0.772) for OS and 0.766 (95% CI, 0.751-0.781) for CSS in the training cohort. In the validation cohort, the nomogram-predicted C-index was 0.754 for OS (95%CI, 0.734-0.774) and 0.752 (95%CI, 0.728-0.776) for CSS. All calibration curves exhibited good consistency between predicted and actual survival. Conclusions These nomogram models established in this study can help to enhance the accuracy of prognostic prediction, which may thereby improve individualized assessment of survival risks and facilitate to provide constructive therapeutic suggestions. Oncology metaplastic breast cancer nomogram overall survival cancer-specific survival Figures Figure 1 Figure 2 Figure 3 Background Metaplastic breast carcinoma (MBC) is a relatively rare form of breast cancer with worse clinical outcomes and resistance to neoadjuvant systemic chemotherapies[ 1 ], accounting for 0.2-5% of all breast cancers [ 2 ]. The incidence of MBC is increasing since it was recognized as a distinct pathological diagnosis in 2000[ 3 ].. Histologically, MBC is classified into several subtypes, including spindle, squamous, chondroid, osseous and/or rhabdomyoid MBC[ 4 ]. MBC commonly shows a triple negative breast cancer (TNBC) phenotype, due to the lack of expression of the ER, PR and HER2 [ 5 ], and is managed with surgical resection in combination with radiotherapy and chemotherapy [ 1 ]. However, only radiotherapy shows improvement on overall survival (OS) in MBC patients[ 6 ]. Compared with invasive ductal carcinoma, the 5-year survival rate for MBC remains poor owing to its rapid tumor growth rate and chemoresistance [ 7 , 8 ]. The Surveillance, Epidemiology, and End Results database (SEER database) is an annually updated and population-based database, covering about 30% of the US population. It has become a distinctive resource to investigate special malignancies, such as MBC, by taking advantages of its wide range of data on cancer. Nomograms have been proposed as a novel and dependable tool to incorporate demographic and clinicopathologic factors for accurate prognostic prediction of many cancers. They were generated from regression analysis and showed to compare favorably to the standard TNM staging systems. Currently, to our knowledge, there was no available nomograms for individual MBC patients derived from population-based data. Herein, we aim to establish a novel nomogram to forecast individualized survival of MBC depended on the personalized demographic, pathologic and therapeutic information from the SEER database. Materials And Methods Patient population Date of patients with MBC diagnosed between 1973 and 2015 was obtained from the SEER program of the National Cancer Institute. Demographic variables of interest for each case included age at diagnosis, race, marital status, grade, status of ER and PR, American Joint Committee on Cancer (AJCC) tumor stage, T stage, N stage, and treatment information including (surgery for the primary site, record of chemotherapy and adjuvant radiotherapy). The following SEER ICD-0-3 codes, including 8052, 8070-8072, 8074, 8560, 8571, 8572, 8575, and 8980 were adopted to identify cases of MBC. Patients with unknown race and marital status, unavailable pathological or survival data were excluded. Figure 1 illustrates the detailed flow diagram for patients screening. Nomogram construction and conformation Patients were randomly divided into the training set and validation set with a ratio of 2:1. Univariate and multivariate analyses were carried out by employing the Cox proportional hazard regression models to determine the hazard ratio (HR) along with corresponding 95% confidence interval (CI) for all possible risk factors. All independent risk factors were identified by the forward stepwise selection method using the multivariate Cox proportional hazards models. The nomogram was established by combing all independent risk factors prognostic factors for the prediction of the 3-year and 5-year OS and CSS using the “rms” R package (cran.rproject.org/web/packages/rms). The Harrell’s concordance index (C-index) was used to access the discrimination, and . calibration curves were applied to estimate the consistency between the actual prognosis and the nomogram-predicted survival probability of the model. Statistical analysis IBM SPSS statistics 22 software (SPSS Inc., Chicago, IL, USA) was used to conduct statistical analysis. R software v 3.6.1 (http://www.r-project.org) was adopted to construct nomograms based on the multivariate results and the “RMS” package was used to develop survival models. The two-tailed P -value <0.05 was assumed statistically significant. Results Patients characteristics We obtained a total 1835 patients with MBC in the study, and the demographic and clinical characteristics of included patients were presented in Table 1. All included patients were allocated randomly into two datasets, including 1223 patients randomly assigned to the training set and 612 patients to the validation set. Of these patients, 908 (50.5%) patients were diagnosed at the age more than 60 years, most patients (77.1%) were white, and 85.4% were married. For the degree of cancer differentiation, poorly differentiated (Grade III) was the most type1425 (77.7%). 1504 (82.0%) and 1591 (86.7%) patients were observed to negatively express ER and PR, respectively. According to the AJCC7 system, stage III was the most type (61.1%), follow by stage I (23.3%) and stage III (15.5%). Most patients (58.0%) received mastectomy and were categorized as T2 stage (52.3%). Meanwhile, most patients (75.9%) were categorized as N0 stage. More than half of patients (67.6%) had undergone chemotherapy, while 48.8% patients had undergone radiotherapy. Prognostic factors of OS and CSS According to univariate analysis performed among the training cohort, eight variables including age, grade, TNM stage, T stage, N stage, surgery for the primary site, chemotherapy and radiotherapy were significantly associated with OS in patients with MBC. Meanwhile, those variables, except for age and chemotherapy, were also found to be significantly associated with CSS of MBC patients. Further multivariate analysis indicated that age, TNM stage, T stage, N stage, chemotherapy and radiotherapy were identified as independent prognostic factors of OS of patients with MBC. Moreover, TNM stage, T stage, N stage and radiotherapy were also identified as independent prognostic factors of CSS of MBC patients, as shown in Table 2 and Table 3. Construction and validation of OS and CSS According to the results of multivariate analysis, all independent prognostic factors in the training set were incorporated to create the nomograms for estimating 3- and 5- year OS and CSS of patients with MBC. Figure 2(a) and Figure 2(b) showed the prediction of the 3- and 5-year OS and CSS in the nomogram, respectively. Each factor was allocated a score on the points scale in the nomogram, and we can estimate the 3- and 5-year survival probability of patients with MBC by calculating the total score via adding up all points on the basis of personal patients features. Nomograms were validated using C-index in the training set and validation set, respectively. The results showed sufficient accuracy in forecasting the prognosis of MBC in training set and validation set. The C-index of the nomogram for OS and CSS is 0.759 (95% CI, 0.746-0.772) and 0.766 (95% CI,0.751-0.781) in the training set, respectively (Table 4). The C-index calculated from the validation set is 0.754 (95% CI, 0.734-0.774) in OS and 0.752 (95% CI, 0.728-0.776) in CSS, respectively. The calibration plots showed good coordination between prediction by nomogram models and observed outcomes in the probability of 3- and 5-year OS and CSS of patients with MBC in both training cohort and validation set (Figure 3). Discussion Metaplastic breast cancer is a kind of heterogenous breast cancer, which is relatively rare in clinical practice. Despite several studies have found risk factors related to the clinical outcomes of MBC patients[9-11], there is no recognized prognostic factors to predict the prognosis of MBC. Paul Wright et al.[12] found that for patients with positive or negative hormone receptors, there was no significant difference in the 5-year survival rate of MBC, which indicated that the status of hormone receptors could not be considered as a prognostic factor of MBC. Additionally, previous study demonstrated that the subtype of MBC could be an independent predictor of its prognosis[3]. Several studies revealed that the prognosis of MBC patients with larger tumor and lymph node metastasis are generally poor[9, 13]. In recent years, some studies have also focused on the relationship between gene signatures and prognosis of MBC patients, such as the high expression of RPL39[11] and the mutation of the colony stimulating factor 1 receptor (CSF1R)[14], all of which can indicate poor prognosis. However, single prognostic factors play a limited role in predicting individual survival probability. Nomograms are graphical display of mathematical models for predicting cancer risk, prevention and therapeutic outcomes, which becomes increasingly popular clinical decision aids thanks to their ability to deal with complex problems in a systematic and unbiased manner[15-17]. It has been revealed that nomograms exhibited more excellent prediction precision and prognostic value in diverse malignancies than the existing tumor system[18, 19]To construct a prognostic nomogram, we conducted univariate and multivariate analyses to find clinical characteristics that correlated with the OS and CSS of MBC patients on the basis of a large data set from the SEER database. We demonstrated that several clinicopathological characteristics were independent prognostic factors for OS, including age, grade, TNM stage, T stage, N stage, surg_prim_site, chemotherapy and radiotherapy. Furthermore, age, TNM stage, T stage, N stage, chemotherapy and radiotherapy were identified as independent prognostic factors for OS via multivariate analysis. In addition, grade, TNM stage, T stage, N stage, surg_prim_site and radiotherapy were found to be associated with CSS via univariate analysis, and further multivariate analysis confirmed that TNM stage, T stage, N stage and radiotherapy were independent prognostic factors for CSS of MBC patients. The nomograms established in this study showed favorable discrimination and calibration for 3-year and 5-year OS and CSS of MBC patients and offered a more accurate and personalized clinical tool for prognosis evaluation of MBC patients. Prognostic studies have given conflicting results regarding factors associated with prognosis and survival [20-30]. In the present study, we critically evaluated the prognostic value of various factors based on a large cases of MBC recorded on the SEER. The clinical significance of age, TNM stage, T stage, N stage, chemotherapy, radiotherapy in MBC patients were highlighted in nomogram models. The result demonstrated that half of patients were older then 60 years, who suffered worst survival and poor OS. Of note, age showed no significant influence on CSS. Patients with older age generally accompanied a higher-risk histological phenotype[31], which has been considered as an independent risk factor and may eventually result in lower survival [31]. In these patients, surgical resection of the primary site is the mainstay of therapy with mastectomy more than lumpectomy, which was consistent with previous study[35]In addition, we found that chemotherapy is an independent prognostic factor for OS in MBC patients. Although it is correlated with CSS in univariate analysis, it is not an independent prognostic factor for CSS. It may result from the worse response to chemotherapy regimens in MBC[21, 25, 27, 36, 37]. Previous studies have concluded that radiotherapy was able to improve the survival of patients with MBC [8, 28, 38], and our data also demonstrated that radiation was independently prognostic factors associated with survival probability of patients with MBC. Moreover, radiotherapy was revealed to be able to reduce the risk of local recurrence[39]. T stage represents the tumor size and extrathyroidal extension, and our results demonstrated that T4 has an impact on OS and CSS in MBC patients, which was in line with previous population-based study of MBC [8]. LNM has been identified as a key prognostic indicator for a variety of malignancies, and the number of LNM has been included into the N-staging. Previous studies reported that lymph node status was significantly correlated with survival endpoints in patients with MBC [8] There were several potential shortcomings in this study. First, retrospective data retrieved from the same database was used in the generation and validation of the nomogram models, which may lead to the risk of potential selection bias. Therefore, it would be more reliable to validate the nomograms in another dataset. Second, in this study, we only included two endpoints: 3- and 5-year survival. However, the assessment of recurrence risk is considered as a more meaningful endpoint than OS or CSS because of the rare specific mortality of MBC, which was not performed in this study owing to the lack of data with respect to recurrence in SEER database. Moreover, several other crucial prognostic factors, such as RET mutation status and calcitonin doubling times, were also unavailable in the SEER database Conclusion In the present study, the nomograms reliably predicted 3- and 5-year OS and CSS of MBC patients were successfully established and well-validated on the basis of large population from SEER. These nomograms could assist clinicians to estimate the risk of tumor surgery and other prognostic factors and make individualized decisions, although the models require more powerful evidence from prospective trials. Abbreviations MBC: Metaplastic breast cancer; CSS: cancer-specific survival; C-index: concordance index; SEER: the Surveillance, Epidemiology, and End Results ; CSF1R: colony stimulating factor 1 receptor Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data analyzed during this study are included in this published article. Competing interests The authors declare that they have no competing interes ts. Funding The study was supported by the Zhejiang Provincial Health Department Foundation (Grant No. 2018ky284), the Natural Science Foundation of Zhejiang Province (Grant No. LQ17H160013). The funding body was not involved in the design of the study, collection, analysis, and interpretation of data, nor the writing the manuscript. The content is solely the responsibility of the authors. Authors’ contributions Conceptualization: YFL and DBC. Data curation: YFL, HJX, MPD and GAC Formal analysis : MPD and GAC. Funding acquisition: YFL. Project administration: HCJ, MC Methodology: YFL, MPD, GAC and HJY. Writing-original draft: YFL, MC . Writing-review&editing: MC and HCJ. All authors have read and approved the final manuscript. Acknowledgements We are grateful to all researchers of enrolled studies. References Rayson D, Adjei AA, Suman VJ, Wold LE, Ingle JN: Metaplastic breast cancer: prognosis and response to systemic therapy. Ann Oncol 1999, 10: 413-419. Moreno AC, Lin YH, Bedrosian I, Shen Y, Babiera GV, Shaitelman SF: Outcomes after Treatment of Metaplastic Versus Other Breast Cancer Subtypes. J Cancer 2020, 11: 1341-1350. 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Tables Table 1. Characteristics of the training and validation cohorts. Variables All patients (N=1835) Training set (n=1223) Validation set (n=612) N % N % N % Age <60 908 49.5 617 50.4 291 47.5 ≥60 927 50.5 606 49.6 321 52.5 Race White 1414 77.1 945 77.3 469 76.6 Black 295 16.1 202 16.5 93 15.2 Others 126 6.9 76 6.2 50 8.2 Marital Single 268 14.6 181 14.8 87 14.2 Married 1567 85.4 1042 85.2 525 85.8 Grade G1 86 4.7 56 4.6 30 4.9 G2 239 13.0 159 13.0 80 13.1 G3 1425 77.7 945 77.3 480 78.4 G4 85 4.6 63 5.2 22 3.6 ER negative 1504 82.0 1014 82.9 490 80.1 positive 331 18.0 209 17.1 122 19.9 PR negative 1591 86.7 1069 87.4 522 85.3 positive 244 13.3 154 12.6 90 14.7 Stage ajcc7 I 428 23.3 298 24.4 130 21.2 II 1122 61.1 732 59.9 390 63.7 III 285 15.5 193 15.8 92 15.0 Stage_T T1 478 26.0 325 26.6 153 25.0 T2 960 52.3 636 52.0 324 52.9 T3 285 15.5 186 15.2 99 16.2 T4 112 6.1 76 6.2 36 5.9 Stage_N N0 1392 75.9 925 75.6 467 76.3 N1 308 16.8 204 16.7 104 17.0 N2 83 4.5 60 4.9 23 3.8 N3 52 2.8 34 2.8 18 2.9 Surg prim site Lumpectomy 771 42.0 514 42.0 257 42.0 Mastectomy 1064 58.0 709 58.0 355 58.0 Chemotherapy No/unknown 594 32.4 389 31.8 205 33.5 Yes 1241 67.6 834 68.2 407 66.5 Radiation No/unknown 940 51.2 624 51.0 316 51.6 Yes 895 48.8 599 49.0 296 48.4 Table 2. Univariate and multivariate analyses of variables associated with OS Variables Univariate analysis Multivariate analysis HR (95%CI) P value HR (95%CI) P value Age <60 Reference ≥60 1.786(1.436-2.221) <0.001 1.653(1.307-2.089) <0.001 Race 0.791 Black Reference White 0.923(0.684-1.246) 0.601 Other 1.088(0.705-1.682) 0.703 Marital Married Reference Single 0.842(0.631-1.124) 0.244 Grade 0.004 G1 Reference G2 2.023(0.904-4.531) 0.087 G3 2.584(1.220-5.474) 0.013 G4 3.819(1.677-8.695) 0.001 ER negative Reference positive 0.933(0.689-1.264) 0.656 PR negative Reference positive 0.861(0.612-1.212) 0.392 Stage ajcc7 <0.001 0.002 I Reference II 2.421(1.686-3.476) <0.001 3.506(1.731-7.101) <0.001 III 8.743(5.987-12.767) <0.01 4.403(1.786-10.855) 0.001 Stage_T <0.001 <0.001 T1 Reference T2 1.776(1.280-2.464) 0.001 0.585(0.316-1.084) 0.088 T3 5.175(3.631-7.376) <0.001 1.595(0.835-3.047) 0.157 T4 9.686(6.491-14.453) <0.001 2.467(1.148-5.299) 0.021 Stage_N <0.001 0.006 N0 Reference N1 1.901(1.463-2.469) <0.001 1.220(0.878-1.695) 0.235 N2 3.767(2.602-5.453) <0.001 2.265(1.286-3.991) 0.005 N3 5.174(3.433-7.798) <0.001 2.409(1.396-4.155) 0.002 Surg prim site Lumpectomy Reference Mastectomy 2.581(2.020-3.298) <0.001 1.202(0.899-1.607) 0.213 Chemotherapy No/unknown Reference Yes 0.657(0.529-0.816) <0.001 0.689(0.538-0.882) 0.003 Radiation No/unknown Reference Yes 0.656(0.528-0.814) <0.001 0.666(0.530-0.836) <0.001 Table 3. Univariate and multivariate analyses of variables associated with CSS Variables Univariate analysis Multivariate analysis HR (95%CI) P value HR (95%CI) P value Age <60 Reference ≥60 1.185(0.925-1.517) 0.179 Race Black Reference White 0.916(0.648-1.297) 0.622 Other 1.054(0.633-1.755) 0.839 Marital Married Reference Single 0.745(0.541-1.026) 0.071 Grade 0.001 0.409 G1 Reference G2 1.495(0.561-3.984) 0.421 1.086(0.405-2.913) 0.870 G3 2.826(1.164-6.863) 0.022 1.438(0.585-3.534) 0.428 G4 3.988(1.510-10.531) 0.005 1.754(0.655-4.698) 0.264 ER negative Reference positive 0.923(0.651-1.309) 0.654 PR negative Reference positive 0.870(0.588-1.289) 0.489 Stage ajcc7 <0.001 0.003 I Reference II 3.306(2.019-5.414) <0.001 4.240(1.809-9.939) 0.001 III 14.131(8.556-23.339) <0.001 5.619(1.959-16.115) 0.001 Stage_T <0.001 <0.001 T1 Reference T2 2.137(1.400-3.262) <0.001 0.582(0.286-1.183) 0.135 T3 7.345(4.727-11.415) <0.001 1.810(0.866-3.781) 0.115 T4 14.008(8.662-22.654) <0.001 2.606(1.119-6.066) 0.026 Stage_N <0.001 <0.001 N0 Reference N1 2.423(1.807-3.250) <0.001 1.299(0.893-1.890) 0.171 N2 4.516(3.000-6.798) <0.001 2.150(1.163-3.973) 0.015 N3 6.595(4.230-10.283) <0.001 2.659(1.467-4.820) 0.001 Surg prim site Lumpectomy Reference Mastectomy 2.720(2.038-3.630) <0.001 1.216(0.871-1.698) 0.251 Chemotherapy No/unknown Reference Yes 1.041(0.796-1.360) 0.772 Radiation No/unknown Reference Yes 0.757(0.591-0.970) 0.028 0.645(0.501-0.830) 0.001 Table 4. The C-index of nomogram for OS and CSS in patients with MBC Survival Training cohort Validation cohort HR 95% CI HR 95% CI OS 0.759 0.746-0.772 0.754 0.734-0.774 CSS 0.766 0.751-0.781 0.752 0.728-0.776 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 In Review Editorial Policies 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-96114","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":3827190,"identity":"0a55c4ef-2976-466c-a2d8-45bda53ba39b","order_by":0,"name":"Yongfeng Li","email":"","orcid":"","institution":"Zhejiang University School of Medicine Sir Run Run Shaw Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongfeng","middleName":"","lastName":"Li","suffix":""},{"id":3827191,"identity":"240f0f3a-ba7f-4b34-a83e-78376e0a6399","order_by":1,"name":"Daobao Chen","email":"","orcid":"","institution":"Institute of cancer research and basic medical sciences of Chinese Academy of sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daobao","middleName":"","lastName":"Chen","suffix":""},{"id":3827192,"identity":"fceff63b-4a26-427b-96d1-308b00e6156a","order_by":2,"name":"Haojun Xuan","email":"","orcid":"","institution":"Institute of cancer research and basic medical science of Chinese Academy of Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haojun","middleName":"","lastName":"Xuan","suffix":""},{"id":3827193,"identity":"a685fc52-0220-4a25-b75a-2efa6036c99b","order_by":3,"name":"Mihnea P. Dragomir","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mihnea","middleName":"P.","lastName":"Dragomir","suffix":""},{"id":3827194,"identity":"020bdc3b-7cb0-4ace-a069-683dc855e2eb","order_by":4,"name":"George A. Calin","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"George","middleName":"A.","lastName":"Calin","suffix":""},{"id":3827195,"identity":"47670014-6a11-4e77-b790-961ed9e99b10","order_by":5,"name":"Hongjian Yang","email":"","orcid":"","institution":"Institute of cancer research and basic medical sciences of Chinese academy of Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongjian","middleName":"","lastName":"Yang","suffix":""},{"id":3827196,"identity":"2e8114cc-7975-4020-936a-2d4c2129f289","order_by":6,"name":"Meng Chen","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Chen","suffix":""},{"id":3827197,"identity":"efd64f67-2029-4e9a-87d7-9a590d6be7f0","order_by":7,"name":"hongchuan Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACAxDB2AAiDzYwfDCw4eFnZj74gGgtjDMq0uQk29mSDYjUwsDAzHPmsLHBeR4zAXxazNl7D7/4ueOwvDnj4bYHvG3MiZsPM5gxMNTYROPSYtlzLs2y98xhw50NB9sNJNvYErcdZkh7wHAsLbcBl8Nu5JgZM7YdZtxw4GCbhGEbD0jLcQPGhsMEtdiDtSQC0eZmxjYJAlqMHwO1JIK1HDhjYGzAzMyGX8uZM2aMvW3pySAtkg0VCXISh9mYDRLw+eV4j/GHn23WthtuHH8m/cfgPw9///mPDz7U2ODUAgRsEmBK4gCSWAJu5SDA/AFM8eMxdRSMglEwCkY2AADwcWcf40YJgAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Breast Surgery, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang 310022, P.R. China ","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"hongchuan","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2020-10-21 14:48:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-96114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-96114/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":3219956,"identity":"e6290c5c-c3d0-4502-b944-b287383caaca","added_by":"auto","created_at":"2020-10-27 13:46:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":428684,"visible":true,"origin":"","legend":"Flow chart of patient selection.","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-96114/v1/e8fef5d73ba68f4baec39a1a.jpg"},{"id":3219957,"identity":"5ca4be3b-b86a-4cf1-8204-a07b99aa6c86","added_by":"auto","created_at":"2020-10-27 13:46:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":577963,"visible":true,"origin":"","legend":"Nomograms for predicting the 3-, and 5-year (a) overall survival and (b) cancer-specifc survival of MBC. ","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-96114/v1/4807194e4c323e1d5265be22.jpg"},{"id":3219958,"identity":"cc3e1c50-2c92-4b98-adbc-0fd83d6e45d7","added_by":"auto","created_at":"2020-10-27 13:46:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":693822,"visible":true,"origin":"","legend":"The calibration curves for predictions survival of MBC patients. The overall survival (A, B) and cancer-specifc survival (C, D) in the training cohort at 3 and 5 years after diagnosis, and the overall survival (E, F) and cancer-specifc survival (G, H) in the validation cohort at 3 and 5 years after diagnosis.","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-96114/v1/0c884d5e0bfd8ab3ab28e1e7.jpg"},{"id":13605955,"identity":"910d1e5f-620f-4351-9a84-360553064869","added_by":"auto","created_at":"2021-09-17 06:05:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1678215,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-96114/v1/f84c20ed-30ce-435a-96c7-76bba943d3ff.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eNovel Nomograms to Predict of Overall Survival and Cancer-specific Survival of Patients of Metaplastic Breast Cancer\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eMetaplastic breast carcinoma (MBC) is a relatively rare form of breast cancer with worse clinical outcomes and resistance to neoadjuvant systemic chemotherapies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], accounting for 0.2-5% of all breast cancers [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The incidence of MBC is increasing since it was recognized as a distinct pathological diagnosis in 2000[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].. Histologically, MBC is classified into several subtypes, including spindle, squamous, chondroid, osseous and/or rhabdomyoid MBC[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. MBC commonly shows a triple negative breast cancer (TNBC) phenotype, due to the lack of expression of the ER, PR and HER2 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and is managed with surgical resection in combination with radiotherapy and chemotherapy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, only radiotherapy shows improvement on overall survival (OS) in MBC patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Compared with invasive ductal carcinoma, the 5-year survival rate for MBC remains poor owing to its rapid tumor growth rate and chemoresistance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The Surveillance, Epidemiology, and End Results database (SEER database) is an annually updated and population-based database, covering about 30% of the US population. It has become a distinctive resource to investigate special malignancies, such as MBC, by taking advantages of its wide range of data on cancer.\u003c/p\u003e \u003cp\u003eNomograms have been proposed as a novel and dependable tool to incorporate demographic and clinicopathologic factors for accurate prognostic prediction of many cancers. They were generated from regression analysis and showed to compare favorably to the standard TNM staging systems. Currently, to our knowledge, there was no available nomograms for individual MBC patients derived from population-based data. Herein, we aim to establish a novel nomogram to forecast individualized survival of MBC depended on the personalized demographic, pathologic and therapeutic information from the SEER database.\u003c/p\u003e "},{"header":" Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDate of patients with MBC diagnosed between 1973 and 2015 was obtained from the SEER program of the National Cancer Institute. Demographic variables of interest for each case included age at diagnosis, race, marital status, grade, status of ER and PR, American Joint Committee on Cancer (AJCC) tumor stage, T stage, N stage, and treatment information including (surgery for the primary site, record of chemotherapy and adjuvant radiotherapy). The following SEER ICD-0-3 codes, including 8052, 8070-8072, 8074, 8560, 8571, 8572, 8575, and 8980 were adopted to identify cases of MBC. Patients with unknown race and marital status, unavailable pathological or survival data were excluded. Figure 1 illustrates the detailed flow diagram for patients screening.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNomogram construction and conformation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were randomly divided into the training set and validation set with a ratio of 2:1. Univariate and multivariate analyses were carried out by employing the Cox proportional hazard regression models to determine the hazard ratio (HR) along with corresponding 95% confidence interval (CI) for all possible risk factors. All independent risk factors were identified by the forward stepwise selection method using the multivariate Cox proportional hazards models. The nomogram was established by combing all independent risk factors prognostic factors for the prediction of the 3-year and 5-year OS and CSS using the \u0026ldquo;rms\u0026rdquo; R package (cran.rproject.org/web/packages/rms). The Harrell\u0026rsquo;s concordance index (C-index) was used to access the discrimination, and . calibration curves were applied to estimate the consistency between the actual prognosis and the nomogram-predicted survival probability of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIBM SPSS statistics 22 software (SPSS Inc., Chicago, IL, USA) was used to conduct statistical analysis. R software v 3.6.1 (http://www.r-project.org) was adopted to construct nomograms based on the multivariate results and the \u0026ldquo;RMS\u0026rdquo; package was used to develop survival models. The two-tailed \u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.05 was assumed statistically significant.\u003c/p\u003e"},{"header":" Results","content":"\u003cp\u003e\u003cstrong\u003ePatients characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained a total 1835 patients with MBC in the study, and the demographic and clinical characteristics of included patients were presented in Table 1. All included patients were allocated randomly into two datasets, including 1223 patients randomly assigned to the training set and 612 patients to the validation set. Of these patients, 908 (50.5%) patients were diagnosed at the age more than 60 years, most patients (77.1%) were white, and 85.4% were married. For the degree of cancer differentiation, poorly differentiated (Grade III) was the most type1425 (77.7%). 1504 (82.0%) and 1591 (86.7%) patients were observed to negatively express ER and PR, respectively. According to the AJCC7\u0026nbsp;system, stage III was the most type (61.1%), follow by stage I (23.3%) and stage III (15.5%). Most patients (58.0%) received mastectomy and were categorized as T2 stage (52.3%). Meanwhile, most patients (75.9%) were categorized as N0 stage. More than half of patients (67.6%) had undergone chemotherapy, while 48.8% patients had undergone radiotherapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic factors of OS and CSS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to univariate analysis performed among the training cohort, eight variables including age, grade, TNM stage, T stage, N stage, surgery for the primary site, chemotherapy and radiotherapy were significantly associated with OS in patients with MBC. Meanwhile, those variables, except for age and chemotherapy, were also found to be significantly associated with CSS of MBC patients. Further multivariate analysis indicated that age, TNM stage, T stage, N stage, chemotherapy and radiotherapy were identified as independent prognostic factors of OS of patients with MBC. Moreover, TNM stage, T stage, N stage and radiotherapy were also identified as independent prognostic factors of CSS of MBC patients, as shown in Table 2 and Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and validation of OS and CSS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the results of multivariate analysis, all independent prognostic factors in the training set were incorporated to create the nomograms for estimating 3- and 5- year OS and CSS of patients with MBC. Figure 2(a) and Figure 2(b) showed the prediction of the 3- and 5-year OS and CSS in the nomogram, respectively. Each factor was allocated a score on the points scale in the nomogram, and we can estimate the 3- and 5-year survival probability of patients with MBC by calculating the total score via adding up all points on the basis of personal patients features.\u003c/p\u003e\n\u003cp\u003eNomograms were validated using C-index in the training set and validation set, respectively. The results showed sufficient accuracy in forecasting the prognosis of MBC in training set and validation set. The C-index of the nomogram for OS and CSS is 0.759 (95% CI, 0.746-0.772) and 0.766 (95% CI,0.751-0.781) in the training set, respectively (Table 4). The C-index calculated from the validation set is 0.754 (95% CI, 0.734-0.774) in OS and 0.752 (95% CI, 0.728-0.776) in CSS, respectively. The calibration plots showed good coordination between prediction by nomogram models and observed outcomes in the probability of 3- and 5-year OS and CSS of patients with MBC in both training cohort and validation set (Figure 3).\u003c/p\u003e"},{"header":" Discussion","content":"\u003cp\u003eMetaplastic breast cancer is a kind of heterogenous breast cancer, which is relatively rare in clinical practice. Despite several studies have found risk factors related to the clinical outcomes of MBC patients[9-11], there is no recognized prognostic factors to predict the prognosis of MBC. Paul Wright et al.[12] found that for patients with positive or negative hormone receptors, there was no significant difference in the 5-year survival rate of MBC, which indicated that the status of hormone receptors could not be considered as a prognostic factor of MBC. Additionally, previous study demonstrated that the subtype of MBC could be an independent predictor of its prognosis[3]. Several studies revealed that the prognosis of MBC patients with larger tumor and lymph node metastasis are generally poor[9, 13]. In recent years, some studies have also focused on the relationship between gene signatures and prognosis of MBC patients, such as the high expression of RPL39[11] and the mutation of the colony stimulating factor 1 receptor (CSF1R)[14], all of which can indicate poor prognosis.\u003c/p\u003e\n\u003cp\u003eHowever, single prognostic factors play a limited role in predicting individual survival probability. Nomograms are graphical display of mathematical models for predicting cancer risk, prevention and therapeutic outcomes, which becomes increasingly popular clinical decision aids thanks to their ability to deal with complex problems in a systematic and unbiased manner[15-17]. It has been revealed that nomograms exhibited more excellent prediction precision and prognostic value in diverse malignancies than the existing tumor system[18, 19]To construct a prognostic nomogram, we conducted univariate and multivariate analyses to find clinical characteristics that correlated with the OS and CSS of MBC patients on the basis of a large data set from the SEER database. We demonstrated that several clinicopathological characteristics were independent prognostic factors for OS, including age, grade, TNM stage, T stage, N stage, surg_prim_site, chemotherapy and radiotherapy. Furthermore, age, TNM stage, T stage, N stage, chemotherapy and radiotherapy were identified as independent prognostic factors for OS via multivariate analysis. In addition, grade, TNM stage, T stage, N stage, surg_prim_site and radiotherapy were found to be associated with CSS via univariate analysis, and further multivariate analysis confirmed that TNM stage, T stage, N stage and radiotherapy were independent prognostic factors for CSS of MBC patients. The nomograms established in this study showed favorable discrimination and calibration for 3-year and 5-year OS and CSS of MBC patients and offered a more accurate and personalized clinical tool for prognosis evaluation of MBC patients.\u003c/p\u003e\n\u003cp\u003ePrognostic studies have given\u0026nbsp;conflicting\u0026nbsp;results\u0026nbsp;regarding factors associated with prognosis and survival [20-30]. In the present study, we critically evaluated the prognostic value of various factors based on a large cases of MBC recorded on the SEER. The clinical significance of age, TNM stage, T stage, N stage, chemotherapy, radiotherapy in MBC patients were highlighted in nomogram models. The result demonstrated that half of patients were older then 60 years, who suffered worst survival and poor OS. Of note, age showed no significant influence on CSS. Patients with older age generally accompanied a higher-risk histological phenotype[31], which has been considered as an independent risk factor and may eventually result in lower survival [31]. In these patients, surgical resection of the primary site is the mainstay of therapy with mastectomy more than lumpectomy, which was consistent with previous study[35]In addition, we found that chemotherapy is an independent prognostic factor for OS in MBC patients. Although it is correlated with CSS in univariate analysis, it is not an independent prognostic factor for CSS. It may result from the worse response to chemotherapy regimens in MBC[21, 25, 27, 36, 37]. Previous studies have concluded that radiotherapy was able to improve the survival of patients with MBC [8, 28, 38], and our data also demonstrated that radiation was independently prognostic factors associated with survival probability of patients with MBC. Moreover, radiotherapy was revealed to be able to reduce the risk of local recurrence[39]. T stage represents the tumor size and extrathyroidal extension, and our results demonstrated that T4 has an impact on OS and CSS in MBC patients, which was in line with previous population-based study of MBC [8]. LNM has been identified as a key prognostic indicator for a variety of malignancies, and the number of LNM has been included into the N-staging. Previous studies reported that lymph node status was significantly correlated with survival endpoints in patients with MBC [8]\u003c/p\u003e\n\u003cp\u003eThere were several potential shortcomings in this study. First, retrospective data retrieved from the same database was used in the generation and validation of the nomogram models, which may lead to the risk of potential selection bias. Therefore, it would be more reliable to validate the nomograms in another dataset. Second, in this study, we only included two endpoints: 3- and 5-year survival. However, the assessment of recurrence risk is considered as a more meaningful endpoint than OS or CSS because of the rare specific mortality of MBC, which was not performed in this study owing to the lack of data with respect to recurrence in SEER database. Moreover, several other crucial prognostic factors, such as RET mutation status and calcitonin doubling times, were also unavailable in the SEER database\u003c/p\u003e"},{"header":" Conclusion","content":"\u003cp\u003eIn the present study, the nomograms reliably predicted 3- and 5-year OS and CSS of MBC patients were successfully established and well-validated on the basis of large population from SEER. These nomograms could assist clinicians to estimate the risk of tumor surgery and other prognostic factors and make individualized decisions, although the models require more powerful evidence from prospective trials.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMBC: Metaplastic breast cancer; CSS: cancer-specific survival; C-index: concordance index; SEER: the Surveillance, Epidemiology, and End Results ; CSF1R: colony stimulating factor 1 receptor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interes\u003cstrong\u003ets.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the Zhejiang Provincial Health Department Foundation (Grant No. 2018ky284), the Natural Science Foundation of Zhejiang Province (Grant No. LQ17H160013). The funding body was not involved in the design of the study, collection, analysis, and interpretation of data, nor the writing the manuscript. The content is solely the responsibility of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u003c/strong\u003e YFL and DBC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData curation:\u003c/strong\u003e YFL, HJX, MPD and GAC\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFormal analysis\u003c/strong\u003e: MPD and GAC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding acquisition:\u003c/strong\u003e YFL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProject administration:\u003c/strong\u003e HCJ, MC\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003eYFL, MPD, GAC and HJY.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting-original draft:\u003c/strong\u003e YFL, MC .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting-review\u0026amp;editing:\u003c/strong\u003e MC and HCJ.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all researchers of enrolled studies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRayson D, Adjei AA, Suman VJ, Wold LE, Ingle JN: \u003cstrong\u003eMetaplastic breast cancer: prognosis and response to systemic therapy.\u003c/strong\u003e\u003cem\u003eAnn Oncol\u003c/em\u003e 1999, \u003cstrong\u003e10:\u003c/strong\u003e413-419.\u003c/li\u003e\n\u003cli\u003eMoreno AC, Lin YH, Bedrosian I, Shen Y, Babiera GV, Shaitelman SF: \u003cstrong\u003eOutcomes after Treatment of Metaplastic Versus Other Breast Cancer Subtypes.\u003c/strong\u003e\u003cem\u003eJ Cancer\u003c/em\u003e 2020, \u003cstrong\u003e11:\u003c/strong\u003e1341-1350.\u003c/li\u003e\n\u003cli\u003eLee H, Jung SY, Ro JY, Kwon Y, Sohn JH, Park IH, Lee KS, Lee S, Kim SW, Kang HS, et al: \u003cstrong\u003eMetaplastic breast cancer: clinicopathological features and its prognosis.\u003c/strong\u003e\u003cem\u003eJ Clin Pathol\u003c/em\u003e 2012, \u003cstrong\u003e65:\u003c/strong\u003e441-446.\u003c/li\u003e\n\u003cli\u003eLakhani S, Ellis I, Schnitt S, Tan P, van de Vijver M: \u003cem\u003eWHO Classification of Tumours of the Breast.\u003c/em\u003e 4 edn. 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Chinese cancer center.\u003c/strong\u003e\u003cem\u003ePlos One\u003c/em\u003e 2015, \u003cstrong\u003e10:\u003c/strong\u003ee131409.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Characteristics of the training and validation cohorts.\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"277\"\u003e\n\u003cp\u003e\u003cstrong\u003eAll patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(N=1835)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003eTraining set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=1223)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"206\"\u003e\n\u003cp\u003e\u003cstrong\u003eValidation set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=612)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u0026lt;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e49.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e617\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e50.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e291\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e47.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u0026ge;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e927\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e50.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e606\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e49.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e52.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e1414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e77.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e77.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e469\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e76.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e16.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e16.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e15.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e126\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e6.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e6.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e8.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarital\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eSingle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e268\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e14.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e14.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e14.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e1567\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e85.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e1042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e85.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e525\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e85.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eG1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e4.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e4.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e4.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eG2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e239\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e13.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e13.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e13.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eG3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e1425\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e77.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e77.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e480\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e78.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eG4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e4.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e5.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e3.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eER\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003enegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e1504\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e82.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e1014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e82.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e80.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003epositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e18.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e209\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e17.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e19.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003enegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e1591\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e86.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e1069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e87.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e522\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e85.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003epositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e244\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e13.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e12.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e14.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eStage ajcc7\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e428\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e23.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e298\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e24.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e21.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e1122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e61.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e732\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e59.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e390\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e63.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e15.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e193\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e15.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e15.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eStage_T\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e26.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e325\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e26.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e25.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e960\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e52.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e52.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e324\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e52.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e15.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e186\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e15.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e16.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e6.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e6.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e5.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eStage_N\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eN0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e1392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e75.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e925\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e75.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e467\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e76.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e308\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e16.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e204\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e16.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e17.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eN2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e4.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e4.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e3.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eN3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e2.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurg prim site\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eLumpectomy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e771\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e42.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e514\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e42.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e257\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e42.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eMastectomy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e1064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e58.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e58.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e355\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e58.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eNo/unknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e32.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e31.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e33.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e1241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e67.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e834\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e68.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e407\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e66.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003eRadiation \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eNo/unknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e940\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e51.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e624\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e51.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e316\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e51.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e895\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e48.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e599\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003e49.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e296\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e48.4\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\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Univariate and multivariate analyses of variables associated with OS\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"275\"\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"275\"\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" width=\"551\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u0026lt;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u0026ge;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e1.786(1.436-2.221)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e1.653(1.307-2.089)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.791\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e0.923(0.684-1.246)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.601\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e1.088(0.705-1.682)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.703\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarital\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eSingle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e0.842(0.631-1.124)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.244\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eG1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eG2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e2.023(0.904-4.531)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.087\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eG3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e2.584(1.220-5.474)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eG4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e3.819(1.677-8.695)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eER\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003enegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003epositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e0.933(0.689-1.264)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.656\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003enegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003epositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e0.861(0.612-1.212)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eStage ajcc7\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e2.421(1.686-3.476)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e3.506(1.731-7.101)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e8.743(5.987-12.767)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e4.403(1.786-10.855)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eStage_T\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e1.776(1.280-2.464)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e0.585(0.316-1.084)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.088\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e5.175(3.631-7.376)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e1.595(0.835-3.047)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.157\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e9.686(6.491-14.453)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e2.467(1.148-5.299)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eStage_N\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eN0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e1.901(1.463-2.469)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e1.220(0.878-1.695)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.235\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eN2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e3.767(2.602-5.453)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e2.265(1.286-3.991)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eN3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e5.174(3.433-7.798)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e2.409(1.396-4.155)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurg prim site\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eLumpectomy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eMastectomy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e2.581(2.020-3.298)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e1.202(0.899-1.607)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.213\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eNo/unknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e0.657(0.529-0.816)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e0.689(0.538-0.882)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eRadiation \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eNo/unknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e0.656(0.528-0.814)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e0.666(0.530-0.836)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026lt;0.001\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\u003cp\u003eTable 3. Univariate and multivariate analyses of variables associated with CSS\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"277\"\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"274\"\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" width=\"551\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u0026lt;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u0026ge;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e1.185(0.925-1.517)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e0.916(0.648-1.297)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.622\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e1.054(0.633-1.755)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.839\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarital\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eSingle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e0.745(0.541-1.026)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.409\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eG1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eG2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e1.495(0.561-3.984)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.421\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.086(0.405-2.913)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.870\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eG3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e2.826(1.164-6.863)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.438(0.585-3.534)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.428\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eG4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e3.988(1.510-10.531)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.754(0.655-4.698)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.264\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eER\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003enegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003epositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e0.923(0.651-1.309)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.654\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003enegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003epositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e0.870(0.588-1.289)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.489\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eStage ajcc7\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e3.306(2.019-5.414)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e4.240(1.809-9.939)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e14.131(8.556-23.339)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e5.619(1.959-16.115)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eStage_T\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e2.137(1.400-3.262)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e0.582(0.286-1.183)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.135\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e7.345(4.727-11.415)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.810(0.866-3.781)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.115\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e14.008(8.662-22.654)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e2.606(1.119-6.066)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eStage_N\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eN0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e2.423(1.807-3.250)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.299(0.893-1.890)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.171\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eN2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e4.516(3.000-6.798)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e2.150(1.163-3.973)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eN3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e6.595(4.230-10.283)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e2.659(1.467-4.820)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurg prim site\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eLumpectomy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eMastectomy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e2.720(2.038-3.630)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.216(0.871-1.698)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.251\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eNo/unknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e1.041(0.796-1.360)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.772\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003e\u003cstrong\u003eRadiation \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eNo/unknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"157\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e0.757(0.591-0.970)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e0.645(0.501-0.830)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e0.001\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\u003cp\u003eTable 4. The C-index of nomogram for OS and CSS in patients with MBC\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"114\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"227\"\u003e\n\u003cp\u003e\u003cstrong\u003eTraining cohort\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"227\"\u003e\n\u003cp\u003e\u003cstrong\u003eValidation cohort\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003eOS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e0.746-0.772\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e0.754\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e0.734-0.774\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003eCSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.766\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e0.751-0.781\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e0.752\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e0.728-0.776\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"}],"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":"metaplastic breast cancer, nomogram, overall survival, cancer-specific survival","lastPublishedDoi":"10.21203/rs.3.rs-96114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-96114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eMetaplastic breast cancer (MBC) is a rare type of breast cancer with an increasing incidence, we aim to develop clinical nomograms to predict the overall survival and cancer-specific survival for patients with MBC.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePatients data were collected from the SEER database between 1973 and 2015. All included patients were randomly assigned into the training and validation sets. Univariate and multivariate Cox analysis were performed to identify independent prognostic factors of MBC. These essential prognostic variables were combined to construct nomogram models to predict overall survival (OS) and cancer-specific survival (CSS) in patients with MBC. Model performance was evaluated by concordance index (C-index) and calibration plots.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA total of 1835 patients were collected and divided into the training (1223) and validation (612) groups. The multivariate Cox model identified age, TNM stage, T stage, and N stage, chemotherapy and radiotherapy as independent covariates associated with OS, while these variables except for age and chemotherapy were independent prognostic factors of CSS. The nomogram constructed based on these covariates demonstrated excellent accuracy in estimating 3-, and 5-year OS and CSS, with a C-index of 0.759 (95% CI, 0.746-0.772) for OS and 0.766 (95% CI, 0.751-0.781) for CSS in the training cohort. In the validation cohort, the nomogram-predicted C-index was 0.754 for OS (95%CI, 0.734-0.774) and 0.752 (95%CI, 0.728-0.776) for CSS. All calibration curves exhibited good consistency between predicted and actual survival.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThese nomogram models established in this study can help to enhance the accuracy of prognostic prediction, which may thereby improve individualized assessment of survival risks and facilitate to provide constructive therapeutic suggestions.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Novel Nomograms to Predict of Overall Survival and Cancer-specific Survival of Patients of Metaplastic Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-10-27 13:43:29","doi":"10.21203/rs.3.rs-96114/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":"24680120-7344-4c26-a4a1-f056197f8f95","owner":[],"postedDate":"October 27th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":897432,"name":"Oncology"}],"tags":[],"updatedAt":"2020-11-22T14:09:07+00:00","versionOfRecord":[],"versionCreatedAt":"2020-10-27 13:43:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-96114","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-96114","identity":"rs-96114","version":["v1"]},"buildId":"pf3fE39SIOqb-0xH_OWvX","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}