Authentication of a survival nomogram for non-invasive micropapillary breast cancer

Authentication of a survival nomogram for non-invasive micropapillary 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 Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Authentication of a survival nomogram for non-invasive micropapillary breast cancer Mingkun Zhang, Yuan Qin, Niuniu Hou, Fuqing Ji, Zhihao Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2595093/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose We aimed at establishing a nomogram to accurately forecast the overall survival (OS) of non-metastatic invasive micropapillary breast carcinoma (IMPC). Methods In the training cohort, data from 429 patients with non-metastatic IMPC were obtained through the Surveillance, Epidemiology, and End Results (SEER) database. Other 102 patients were enrolled at the Xijing Hospital as validation cohort. Independent risk factors affecting OS were ascertained using univariate and multivariate Cox regression. A nomogram was established to forecast OS at 3, 5 and 8 years. The concordance index (C-index), the area under a receiver operating characteristic (ROC) curve and calibration curves were utilized to assess calibration, discrimination and predictive accuracy. Finally, the nomogram was utilized to stratify the risk. The OS between groups was compared through Kaplan-Meier survival curves. Results The multivariate analyses revealed that race ( p = 0.047), surgery ( p = 0.003), positive lymph nodes ( p = 0.027), T stage ( p = 0.045) and estrogen receptors ( p = 0.019) were independent prognostic risk factors. The C-index was 0.766 (95% CI, 0.682–0.850) in the training cohort and 0.694 (95% CI, 0.527–0.861) in the validation cohort. Furthermore, the predicted OS was consistent with actual observation. The AUCs for OS at 3, 5 and and 8 years were 0.786 (95% CI: 0.656–0.916), 0.791 (95% CI: 0.669–0.912), and 0.774 (95% CI: 0.688–0.860) in the training cohort, respectively. The area under the curves (AUCs) for OS at 3, 5 and 8 years were 0.653 (95% CI: 0.498–0.808), 0.683 (95% CI: 0.546–0.820), and 0.716 (95% CI: 0.595–0.836) in the validation cohort, respectively. The Kaplan-Meier survival curves revealed a significant different OS between groups in both cohorts ( p ༜0.001). Conclusion Our novel prognostic nomogram for non-metastatic IMPC patients achieved a good level of accuracy in both cohorts and could be used to optimize the treatment based on the individual risk factors. invasive micropapillary breast carcinoma overall survival SEER prognosis nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction In 2019, about 271,270 patients in the United States (US) were diagnosed with breast cancer, explaining about 30% of new diagnoses in women. Approximately 42,260 patients died from breast cancer 1 . Invasive micropapillary carcinoma (IMPC) is a ductal lesion found in up to 8% of breast cancers 2–9 . This histological subtype was first described in a randomized controlled trial by Fisher et al. in 1980, whereby 35 patients out of 1603 women were diagnosed with this subtype 10 . Subsequently, Siriaunkgul and Tavassoli proposed a new official classification for IMPC in 1993 11 . Until 2003, IMPC was listed as a distinct pathological type in the classification of the World Health Organization, owing to its distinctive clinicopathology characteristics 12 . IMPC is likely spreading via lymphatic vessels and lymph node metastasis than other subtypes. Guan et al. found that IMPC is more aggressive and has inferior disease-free survival and overall survival (OS) respect to invasive ductal carcinoma and ductal carcinoma in situ 14 . However, recent studies similar long-term survival outcomes 15,16 . As a result, there is a need to evaluate factors affecting survival in IMPC. Nomograms are mathematical models used in medicine to describe how clinical variables are related to each other. The advantages of these models are that they are easy to use, intuitive, accurate, and reliable 17 . As a result, nomograms are increasingly being used to predict survival in cancer and facilitate clinical decision-making 18,19 . However, there are currently only a few nomograms predicting the prognosis of non-metastatic IMPC. Therefore this study aimed at developing a prognostic nomogram for non-metastatic IMPC. The final nomogram was further verified on an external cohort of patients obtained from a Chinese hospital. 2. Materials And Methods 2.1. Patients´ cohort The data of patients diagnosed with non-metastatic IMPC were retrieved from the the Surveillance, Epidemiology, and End Results (SEER) database. The program SEER * Stat (version 8.3.5) identified relevant patients from 2000 to 2014. We included female patients with a diagnosis of primary breast cancer according to the third edition of the International Classification of Diseases (ICD-O-3), with no distant metastasis (M0) and/or with a confirmed histological diagnosis of IMPC according to the histological/behavior code (ICD-O-3 Hist/behav, malignant) as training cohort. Patients aged below 20 or above 70 years, with bilateral breast cancer or unclear unilateral breast cancer, and/or those with incomplete follow-up (survival months = 0) were excluded. In addition, we excluded patients with missing clinical data, including information about race, grade, treatment (surgery, radiation therapy, and chemotherapy), TNM stage, estrogen receptor (ER) and progesterone receptor (PR) status. According to previous inclusion and exclusion criteria, patients diagnosed with non-metastatic IMPC at the Xijing Hospital (Xian, China) between March 2006 and December 2016 were enrolled as part of the external validation cohort. 2.2. Ethics and statement This study was approved by the Ethics Committee review of the Xijing Hospital, The Fourth Military Medical University, Xian, China and was in accordance with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical stabdards. We collected a written informed consent for all patients enrolled at the hospital. This study was conducted using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement, which was the reoorting guideline of prediction models 20 . 2.3. Covariates and endpoints The demographic data (ethnicity, age at the diagnosis and marital status), clinical data (grading, staging, ER and PR status and regional nodes), treatments (surgery, chemotherapy and radiotherapy) and follow-up information were retrieved from the SEER database. Unmarried people were defined as divorced, separated, widowed or single (having a domestic partner or never married). OS was the main primary endpoint, designated as the survival in months to all-cause mortality. 2.4. Construction of the nomogram The categorical variables are expressed as proportions and frequencies. The Chi-square and Fisher's exact tests compared the baseline categorical variables between training and external validation cohorts. The continuous variables are described as median and interquartile range (IQR). The Student's t and non-parametric Mann-Whitney U tests made comparisons between groups. Univariate Cox regression analysis screened out variables predictive of prognosis. Subsequently, a multivariate Cox regression analysis determined the independent prognostic indicators. A nomogram model was established to predict OS at 3, 5 and 8 years by integrating all independent prognostic factors. 2.5. Nomogram´s discrimination and calibration The nomogram's ability to discriminate between different survival groups was estimated through the Concordance index (C-index). Model fitting was done for 1000 bootstraps. The C-index fluctuates from 0.5 to 1, with 1 representing the highest discrimination ability. The time-dependent (tROC) and ROC curves were implemented to verify the prediction accuracy. A calibration curved line reflecting the relevance between the forecasted and the observed survival probability was used to assess the calibration of the model. A straight calibration line threaded through the axis's starting point with a rake rate of 1 indicates excellent calibration. The predictive capacity of the model correlated with the closeness of the forecasted calibration curved line to the ideal curve. A decision curve analysis (DCA) verified the net benefit and latent clinical effectiveness for different predictive percentage thresholds. 2.6. Classification of the risk groups by the model In the training cohort, the total nomogram score was calculated for each patient. According to a cut-off nomogram score calculated through the X-tile software, patients were stratified into high- and low-risk categories. The Kaplan-Meier survival analysis and log-rank test analyzed the different survival between groups. 2.7. Statistical analysis We deployed SPSS program (version 26.0), R software (version 3.5.3) and X-tile software to analyze data. We judged a p -value below 0.05 as statistically significant. 3. Results 3.1. Comparison of baseline characteristics A total of 429 IMPC cases were retrieved from the SEER database, while 102 IMPC patients were recruited from the Xijing Hospital. The demographic and clinicopathological information are summarized in Table 1 . The age, ethnicity, surgery, staging, marital status and ER status differed between cohorts ( p < 0.05). Compared to the training cohort, N3 and ER-positive patients with a lower age characterized the validation cohort. On the other hand, the external cohort was characterized by a higher number of Asian patients, mastectomies, T2 tumors and married patients. The median follow-up was 61 months (IQR, 39–91 months) for the training cohort and 60.5 months (IQR, 53–98 months) for the external cohort. TABLE 1 Demographic and clinicopathologic features of the training cohort and external validation cohort Characteristics Training cohort (n=429), n (%) External validation cohort (n=102), n (%) P value Age 55（47-63） 49（42-55） ＜0.001 Race ＜0.001 White 321(74.83) 1（0.98） Black 69(16.08) 2（1.96） Asian and other 39(9.09) 99(97.06) Laterality 0.110 Left 227(52.91) 45(44.12) Right 202(47.09) 57(55.88) Grade 0.173 1 28（6.53） 12（11.76） 2 226（52.68） 48（47.06） 3 175（40.79） 42（41.18） Examined lymph nodes 6(2-14) 9(4-19) 0.091 Positive lymph nodes 1(0-3) 1(0-4) 0.544 Surgery ＜0.001 No 11（2.56） 1（0.98） BCS 209（48.72） 29（28.43） Mastectomy 209（48.72） 72（70.59） Radiation therapy 0.704 No 194（45.22） 44（43.14） Yes 235（54.78） 58（56.86） Chemotherapy 0.731 No 155（36.13） 35（34.31） Yes 274（63.87） 67（65.69） Marital status ＜0.001 Unmarried 171（39.86） 20（19.61） Married 258（60.14） 82（80.39） T stage 0.009 T1 229（53.38） 40（39.22） T2 141（32.87） 52（50.98） T3 47（10.96） 8（7.84） T4 12（2.79） 2（1.96） N stage 0.027 N0 186（43.36） 49（48.04） N1 141（32.87） 42（41.18） N2 58（13.52） 8（7.84） N3 44（10.25） 3（2.94） ER 0.044 Negative 55（12.82） 21（20.59） Positive 374（87.18） 81，（79.41） PR 0.536 Negative 97（22.61） 26（25.49） Positive 332（77.39） 76（74.51） 3.2. Independent prognostic variables in the training cohort Marital status, ethnicity, surgery, staging, positive lymph nodes, ER and PR were prognostic factors for OS in non-metastatic IMPC (all p < 0.05). Five independent risk factors were identified, including race, surgery, positive lymph nodes, T stage, and ER status. The results are summarized in Table 2 . Table 2 Univariable and multivariable Cox analysis for predicting overall survival in non-metastatic IMPC in training cohort Characteristics Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Age 1.527 (0.896–2.602) 0.120 Race White Black 2.220 (1.149–4.289) 0.018 1.866 (1.010–3.779) 0.047 Asian and other 0.457 (0.109–1.921) 0.285 0.357 (0.079–1.626) 0.183 Laterality Left Right 0.869 (0.471–1.605) 0.655 Grade 1 2 2.350 (0.011–3.870) 0.594 3 4.595 (2.478–9.805) 0.572 Surgery No BCS 0.137 (0.039–0.480) 0.002 0.164 (0.037–0.722) 0.017 Mastectomy 0.247 (0.074–0.827) 0.023 0.124 (0.031–0.498) 0.003 Radiation therapy No Yes 0.824 (0.453-1.500) 0.527 Chemotherapy No Yes 1.240 (0.655–2.349) 0.509 Examined lymph nodes 1.021 (0.991–1.053) 0.172 Positive lymph nodes 1.207 (1.105–1.319) < 0.001 1.128 (1.009–1.282) 0.027 Marital status Unmarried Married 0.497 (0.271–0.910) 0.023 0.743 (0.385–1.432) 0.375 T stage T1 T2 1.370 (0.627–2.990) 0.430 0.949 (0.399–2.256) 0.906 T3 4.859 (2.210-10.683) < 0.001 2.149 (0.754–6.124) 0.152 T4 12.314 (4.739–31.998) < 0.001 3.988 (1.032–15.410) 0.045 N stage N0 N1 2.025 (0.864–4.747) 0.104 1.935 (0.769–4.868) 0.161 N2 2.487 (0.883–7.004) 0.085 1.951 (0.554–6.864) 0.298 N3 7.412 (3.242–16.947) < 0.001 2.636 (0.507–13.707) 0.249 ER Negative Positive 0.316 (0.167–0.599) < 0.001 0.241 (0.073–0.790) 0.019 PR Negative Positive 0.481 (0.261–0.888) 0.019 1.421 (0.466–4.337) 0.537 3.3. Establishment of the nomogram of prognosis The nomogram forecasting survival time based on the 5 independent risk factors, including race (White, Black, Asian or other), surgery (none, breast conservative surgery (BCS) or mastectomy), number of positive lymph nodes, T stage (T1, T2, T3 or T4) and ER status (negative or positive) is illustrated in Fig. 1 . The risk score for each independent prognostic indicator was calculated by sketching a vertical line from the independent variable. The total risk was then calculated by considering the scores obtained for each variable. The OS at 3, 5 and 8 years was calculated by sketching a line from the total points axis to the corresponding survival axis. 3.4. Verification of the model's performance Regarding OS, the C-index showed a good prognostic discrimination for both training (0.766, 95% CI, 0.682–0.850) and validation cohorts (0.694, 95% CI, 0.527–0.861). The AUC values were 0.786 (95%CI: 0.656–0.916) at 3 years, 0.791 (95%CI: 0.669–0.912) at 5 years and 0.774 (95%CI: 0.688–0.860) at 8 years for the training cohort. The AUC values were 0.653 (95%CI: 0.498–0.808) at 3 years, 0.683 (95%CI: 0.546–0.820) at 5 years and 0.716 (95%CI: 0.595–0.836) at 8 years for the external cohort (Figs. 2 A and B). The time-dependent AUC values showed excellent performance and discrimination (Figs. 2 C and D). The calibration curves indicated a satisfactory agreement between predictions and observed OS in both cohorts (Figs. 3 A and B). The 3-, 5- and 8-year decision curve analysis showed that compared to single-factor models, the nomogram had a favorable net benefit in predicting survival for all threshold probabilities (Fig. 4 ). 3.5. Risk stratification Considering the total sum score, the optimal cut-off value was 139.3. Patients with a total sum score below this cut-off were identified as low risk patients, whereas those with total sum scores above or equal to the cut-off were identified as high risk patients (Figs. 5 A and B). In the training cohort, we classified 43 high-risk patients and 386 low-risk patients. In the validation cohort, we classified 27 high-risk patients and 75 low-risk patients. The Kaplan-Meier survival curves revealed a different survival between groups in both cohorts ( p < 0.001 and p = 0.0074) (Figs. 5 C and D). 4. Discussion IMPC is an heterogeneous form of breast cancer. Respect to IDC, IMPC is associated with a worse prognosis 21,22,23 . IMPC has different characteristics from other common breast cancer histological subtypes. As a result, several other factors may affect the prognosis of IMPC besides the tumor stage. We developed a novel risk stratification nomogram to forecast the OS in non-metastatic IMPC. Clinical information of IMPC individuals were retrieved from the SEER database, which covers about 30% of all cancers in the US. However, since the US and Chinese populations differ genetically and demographically, we also validated the model on a cohort of IMPC cases obtained from the Xijing Hospital, one of the biggest hospitals in northwest China, to confirm the applicability and accuracy of the stratification nomogram. After performing univariate and multivariate analyses, we identified 5 independent risk factors for OS, including race, surgery, positive lymph nodes, T stage and ER status. These factors were used to develop a predictive nomogram. Consistent with previous studies, we identified a strong association between positive lymph nodes and worse survival outcomes. Therefore, positive lymph nodes was given the highest weighting and incorporated as a continuous variable to improve the prediction accuracy of the nomogram. Compared with IDC, IMPC has a higher incidence of lymph involvement ranging from 60–90% 4,24,25,26 . We described that the incidence of lymph node involvement was higher in the training cohort (56.64%) than in the validation cohort (51.96%), possibly due to variations in the T-stage, ER status, age, and race between the 2 groups. Meanwhile, our analysis indicated that Asian patients with non-metastatic IMPC had a better prognosis than black patients, possibly due to the lower rate of lymphatic metastasis. Though there are no reports of such findings in non-metastatic IMPC, some studies on IDC also showed that Asian breast cancer patients tend to have a better prognosis 27,28 . The individuals pertaining to the training cohort reported a lower T stage compared to those in the external cohort. Due to a small tumor size, more patients in the training cohort received BCS respect to the other cohort (48.72% versus 28.43%, p ༜0.001). Interestingly, although previous studies showed that patients treated with BCS and mastectomy had the same prognosis 29,30 , in our study, breast mastectomy was an independent high-risk indicator for OS more than BCS, possibly since patients who underwent BCS tended to have a lower T-stage. Furthermore, various studies also demonstrated that ER expression is an important predictor for OS, recurrence-free survival and breast cancer-specific survival in IMPC patients 31,32 . Our study also identified ER status as an important prognostic indicator. Inconsistent with previously published work, age was not an independent risk factor for OS. Younger patients often tend to have more aggressive advanced breast cancer than older patients 33,34 . However, the proportion of ER-positive patients with breast cancer was relatively high in our cohort. Since ER status greatly impacts OS, age was not identified as an independent risk factor. Individuals from the training were significantly older than those in the validation cohort (55 years versus 49 years, p < 0.001). In the SEER database, the median age of women with breast cancer is 61 years 35 . However, in China, the age of women with breast carcinoma is frequently reported from 45 to 55 years. The younger age in Chinese patients could be due to the birth cohort effect, variations in menstrual and reproductive patterns or other environmental factors 36 . Since Chinese patients were younger than those in US, they were likely to present with an advanced breast cancer. Notably, our analysis showed no difference regarding 5-year OS between cohorts. In low-risk groups, the 5-year OS was higher in the training cohort. In high-risk groups, the 5-year OS was lower in the training cohort. The AUC, C-index and calibration results showed that the proposed nomogram achieved good discrimination and accuracy in both cohorts. The DCA confirmed that our nomogram could significantly benefit decision-making compared to single-factor models. Additionally, the nomogram could accurately stratify the IMPC patients in high-risk and low-risk groups. According to this stratification, clinicians can more accurately predict the survival time and therefore optimize the follow-up and treatment according to the patient's needs. Our study has some limitations. The retrospective design and the small sample size in our study could have introduced selection bias, limiting the generalizability of the research findings. Moreover, the SEER database lacked detailed information about important predictive factors for OS, such as chemotherapy treatment, lymphovascular invasion, genetic mutations, and the proportion of the IMPC subtype within the breast sample. Larger prospective studies are recommended to identify the impact of other variables on OS and validate the nomogram's predictive accuracy. 5. Conclusions We developed a nomogram predicting OS specifically for the IMPC breast cancer subtype. Our novel prognostic nomogram for non-metastatic IMPC patients achieved satisfactory discrimination and predictive accuracy in both cohorts. Clinicians could use the nomogram to optimize the follow-up and treatment in accordance with patient's specific risk factors. Declarations Acknowledgments We recognize the support from TopEdit (www.topeditsci.com) for the language editing of the manuscript. Funding: None. Disclosure The authors have declared no conflicts of interest. Data availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7-34. doi:10.3322/caac.21551. Sinn H-P, Kreipe H. A brief overview of the WHO classification of breast tumors. Breast Care 2013; 8:149–54. Paterakos M, Watkin WG, Edgerton SM, et al. 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Clinico-pathological features and prognosis of invasive micropapillary carcinoma compared to invasive ductal carcinoma: a population-based study from China. PLoS One . 2014;9(6):e101390. Published 2014 Jun 30. Lewis GD, Xing Y, Haque W, et al. The impact of molecular status on survival outcomes for invasive micropapillary carcinoma of the breast. Breast J . 2019;25(6):1171-1176. doi:10.1111/tbj.13432 Sabiani L, Houvenaeghel G, Heinemann M, Reyal F, Classe JM, Cohen M, et al. Breast cancer in young women: pathologic features and molecular phenotype. Breast. 2016; 29:109–116. doi: 10.1016/j.breast.2016.07.007. Liedtke C, Rody A, Gluz O, Baumann K, Beyer D, Kohls E-B, et al. The prognostic impact of age in different molecular subtypes of breast cancer. Breast Cancer Res Treat. 2015;152(3):667–673. doi: 10.1007/s10549-015-3491-3. Howlader N, Noone AM, Krapcho M, et al. SEER Cancer Statistics Review, 1975-2012, based on Nov 2014 SEER data submission. Bethesda, MD: National Cancer Institute; 2015. Fan L, Strasser-Weippl K, Li JJ, et al. Breast cancer in China. Lancet Oncol. 2014;15(7): e279-e289. doi:10.1016/S1470-2045(13)70567-9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-2595093","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":179178252,"identity":"acce08ef-4851-4cda-ba37-7f96b1af5fd5","order_by":0,"name":"Mingkun Zhang","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingkun","middleName":"","lastName":"Zhang","suffix":""},{"id":179178255,"identity":"6fb05c2e-b37c-42f2-a86e-d6949e8fcac3","order_by":1,"name":"Yuan Qin","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Qin","suffix":""},{"id":179178256,"identity":"c81d51c0-041c-4391-ba71-8f5065bf9c97","order_by":2,"name":"Niuniu Hou","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Niuniu","middleName":"","lastName":"Hou","suffix":""},{"id":179178257,"identity":"f3d5be5c-0fc8-47cf-9a8e-c445bd9ccb31","order_by":3,"name":"Fuqing Ji","email":"","orcid":"","institution":"Xi’an NO.3 Hospital, the Affiliated Hospital of Northwest University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fuqing","middleName":"","lastName":"Ji","suffix":""},{"id":179178258,"identity":"35b0a716-c0f3-4299-8a01-4916439fcb23","order_by":4,"name":"Zhihao Zhang","email":"","orcid":"","institution":"Xi’an NO.3 Hospital, the Affiliated Hospital of Northwest University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhihao","middleName":"","lastName":"Zhang","suffix":""},{"id":179178259,"identity":"472f8ee2-c5f6-47c9-bde5-b0597b91e5ee","order_by":5,"name":"Juliang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYNCCAiCWADEqJOTkidNiANNyxsLYsIEkLYxtFYkMBwgpPn784WceA7t8+dnNzx5+nSeRwNjA/PDRDXxazuQYS/MYJFs2zjlmbiy7TSKPnYHN2DgHjxbJhhw2Zh4DZgNmiQQzacltEsWMDTxs0ni19D9/BtRSb8Amkf5NWnKORGLDAQJa+IGGA7UcNuCRyDGT/NhAlJY3xpJzDI4bSEjklEkzHJMwNmwm4Bc2/vSHH95UVBvIz0jfJvmjpk5Onr354WN8WlAAMw+YJFY5CDD+IEX1KBgFo2AUjBgAAOufQCHTmpF+AAAAAElFTkSuQmCC","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Juliang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-02-16 13:44:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2595093/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2595093/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":33645571,"identity":"6ff0056f-137e-4bc3-9868-326ce1972655","added_by":"auto","created_at":"2023-03-01 19:36:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":699590,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram for predicting 3-, 5- and 8-year overall survival (OS) of patients with non-metastatic IMPC.\u003c/p\u003e","description":"","filename":"Figure.1.png","url":"https://assets-eu.researchsquare.com/files/rs-2595093/v1/2e6d1ebfcff2d4e115383153.png"},{"id":33646083,"identity":"b2def746-2575-41cc-910e-6911981f0ba4","added_by":"auto","created_at":"2023-03-01 19:44:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":576305,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves and AUCs of nomogram for predicting 3-year, 5-year and 8-year OS in the training cohort (A) and external validation cohort(B), and time-dependent AUC values of nomogram in the training cohort (C) and external validation cohort (D).\u003c/p\u003e","description":"","filename":"Figure.2.png","url":"https://assets-eu.researchsquare.com/files/rs-2595093/v1/8f955304f94b9a3fcf439d30.png"},{"id":33645573,"identity":"98e6c471-1a82-4bec-8933-6f3d67ca3c33","added_by":"auto","created_at":"2023-03-01 19:36:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":430080,"visible":true,"origin":"","legend":"\u003cp\u003ecalibration curves of nomogram for predicting 3-year, 5-year and 8-year OS in the training cohort (A) and external validation cohort (B). The x-axis indicates the predicted survival probability, and the y-axis indicates the actual survival probability. The 45-degree line (gray line) indicates that the prediction agrees with actuality.\u003c/p\u003e","description":"","filename":"figure.3.png","url":"https://assets-eu.researchsquare.com/files/rs-2595093/v1/375b1a3f1ac11f880a8325be.png"},{"id":33645574,"identity":"71fce7a6-7185-486c-8e9f-44ccbcf99c1b","added_by":"auto","created_at":"2023-03-01 19:36:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":606535,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the nomogram and single independent predictors for predicting the 3-year OS (A, D), 5-year OS (B, E) and 8-year OS (C, F) in the training cohort and external validation cohort, respectively. Light green line: net benefit of a strategy of treating all IMPC patients. Gray line: net benefit of treating no IMPC patients. Colored lines: net benefit of a strategy of treating patients according to the nomogram, T stage and ER.\u003c/p\u003e","description":"","filename":"Figure.4.png","url":"https://assets-eu.researchsquare.com/files/rs-2595093/v1/420f326b6e2fd31889d25383.png"},{"id":33646084,"identity":"45b4a1d9-60ce-4690-b42f-c8cfd09b6f2e","added_by":"auto","created_at":"2023-03-01 19:44:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":485478,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of best cut-off value of total sum score by the X-tile software (A and B). Kaplan–Meier curves of OS for risk stratification in the training cohort (C) and the external validation cohort (D).\u003c/p\u003e","description":"","filename":"Figure.5.png","url":"https://assets-eu.researchsquare.com/files/rs-2595093/v1/368fe565c02680af817e0711.png"},{"id":42414173,"identity":"8b8067e8-d9d2-4fe7-9095-8fb4d10b20c2","added_by":"auto","created_at":"2023-08-31 09:22:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1527102,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2595093/v1/be7895db-90db-4dd0-b575-4bec53e4b14e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Authentication of a survival nomogram for non-invasive micropapillary breast cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn 2019, about 271,270 patients in the United States (US) were diagnosed with breast cancer, explaining about 30% of new diagnoses in women. Approximately 42,260 patients died from breast cancer\u003csup\u003e1\u003c/sup\u003e. Invasive micropapillary carcinoma (IMPC) is a ductal lesion found in up to 8% of breast cancers\u003csup\u003e2\u0026ndash;9\u003c/sup\u003e. This histological subtype was first described in a randomized controlled trial by Fisher et al. in 1980, whereby 35 patients out of 1603 women were diagnosed with this subtype\u003csup\u003e10\u003c/sup\u003e. Subsequently, Siriaunkgul and Tavassoli proposed a new official classification for IMPC in 1993\u003csup\u003e11\u003c/sup\u003e. Until 2003, IMPC was listed as a distinct pathological type in the classification of the World Health Organization, owing to its distinctive clinicopathology characteristics\u003csup\u003e12\u003c/sup\u003e. IMPC is likely spreading via lymphatic vessels and lymph node metastasis than other subtypes. \u003cem\u003eGuan et al.\u003c/em\u003e found that IMPC is more aggressive and has inferior disease-free survival and overall survival (OS) respect to invasive ductal carcinoma and ductal carcinoma in situ\u003csup\u003e14\u003c/sup\u003e. However, recent studies similar long-term survival outcomes\u003csup\u003e15,16\u003c/sup\u003e. As a result, there is a need to evaluate factors affecting survival in IMPC.\u003c/p\u003e \u003cp\u003eNomograms are mathematical models used in medicine to describe how clinical variables are related to each other. The advantages of these models are that they are easy to use, intuitive, accurate, and reliable\u003csup\u003e17\u003c/sup\u003e. As a result, nomograms are increasingly being used to predict survival in cancer and facilitate clinical decision-making\u003csup\u003e18,19\u003c/sup\u003e. However, there are currently only a few nomograms predicting the prognosis of non-metastatic IMPC. Therefore this study aimed at developing a prognostic nomogram for non-metastatic IMPC. The final nomogram was further verified on an external cohort of patients obtained from a Chinese hospital.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Patients\u0026acute; cohort\u003c/h2\u003e \u003cp\u003eThe data of patients diagnosed with non-metastatic IMPC were retrieved from the the Surveillance, Epidemiology, and End Results (SEER) database. The program SEER * Stat (version 8.3.5) identified relevant patients from 2000 to 2014. We included female patients with a diagnosis of primary breast cancer according to the third edition of the International Classification of Diseases (ICD-O-3), with no distant metastasis (M0) and/or with a confirmed histological diagnosis of IMPC according to the histological/behavior code (ICD-O-3 Hist/behav, malignant) as training cohort. Patients aged below 20 or above 70 years, with bilateral breast cancer or unclear unilateral breast cancer, and/or those with incomplete follow-up (survival months\u0026thinsp;=\u0026thinsp;0) were excluded. In addition, we excluded patients with missing clinical data, including information about race, grade, treatment (surgery, radiation therapy, and chemotherapy), TNM stage, estrogen receptor (ER) and progesterone receptor (PR) status.\u003c/p\u003e \u003cp\u003eAccording to previous inclusion and exclusion criteria, patients diagnosed with non-metastatic IMPC at the Xijing Hospital (Xian, China) between March 2006 and December 2016 were enrolled as part of the external validation cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Ethics and statement\u003c/h2\u003e \u003cp\u003e This study was approved by the Ethics Committee review of the Xijing Hospital, The Fourth Military Medical University, Xian, China and was in accordance with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical stabdards. We collected a written informed consent for all patients enrolled at the hospital. This study was conducted using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement, which was the reoorting guideline of prediction models\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Covariates and endpoints\u003c/h2\u003e \u003cp\u003e The demographic data (ethnicity, age at the diagnosis and marital status), clinical data (grading, staging, ER and PR status and regional nodes), treatments (surgery, chemotherapy and radiotherapy) and follow-up information were retrieved from the SEER database. Unmarried people were defined as divorced, separated, widowed or single (having a domestic partner or never married). OS was the main primary endpoint, designated as the survival in months to all-cause mortality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Construction of the nomogram\u003c/h2\u003e \u003cp\u003eThe categorical variables are expressed as proportions and frequencies. The Chi-square and Fisher's exact tests compared the baseline categorical variables between training and external validation cohorts. The continuous variables are described as median and interquartile range (IQR). The Student's t and non-parametric Mann-Whitney U tests made comparisons between groups. Univariate Cox regression analysis screened out variables predictive of prognosis. Subsequently, a multivariate Cox regression analysis determined the independent prognostic indicators. A nomogram model was established to predict OS at 3, 5 and 8 years by integrating all independent prognostic factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Nomogram\u0026acute;s discrimination and calibration\u003c/h2\u003e \u003cp\u003eThe nomogram's ability to discriminate between different survival groups was estimated through the Concordance index (C-index). Model fitting was done for 1000 bootstraps. The C-index fluctuates from 0.5 to 1, with 1 representing the highest discrimination ability. The time-dependent (tROC) and ROC curves were implemented to verify the prediction accuracy.\u003c/p\u003e \u003cp\u003eA calibration curved line reflecting the relevance between the forecasted and the observed survival probability was used to assess the calibration of the model. A straight calibration line threaded through the axis's starting point with a rake rate of 1 indicates excellent calibration. The predictive capacity of the model correlated with the closeness of the forecasted calibration curved line to the ideal curve. A decision curve analysis (DCA) verified the net benefit and latent clinical effectiveness for different predictive percentage thresholds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Classification of the risk groups by the model\u003c/h2\u003e \u003cp\u003eIn the training cohort, the total nomogram score was calculated for each patient. According to a cut-off nomogram score calculated through the X-tile software, patients were stratified into high- and low-risk categories. The Kaplan-Meier survival analysis and log-rank test analyzed the different survival between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e \u003cp\u003eWe deployed SPSS program (version 26.0), R software (version 3.5.3) and X-tile software to analyze data. We judged a \u003cem\u003ep\u003c/em\u003e-value below 0.05 as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003e3.1. Comparison of baseline characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 429 IMPC cases were retrieved from the SEER database, while 102 IMPC patients were recruited from the Xijing Hospital. The demographic and clinicopathological information are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The age, ethnicity, surgery, staging, marital status and ER status differed between cohorts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared to the training cohort, N3 and ER-positive patients with a lower age characterized the validation cohort. On the other hand, the external cohort was characterized by a higher number of Asian patients, mastectomies, T2 tumors and married patients. The median follow-up was 61 months (IQR, 39\u0026ndash;91 months) for the training cohort and 60.5 months (IQR, 53\u0026ndash;98 months) for the external cohort.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTABLE 1 Demographic and clinicopathologic features of the training cohort and external validation cohort\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"548\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003eTraining cohort\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=429), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78688524590164%\"\u003e\n \u003cp\u003eExternal validation cohort\u003c/p\u003e\n \u003cp\u003e(n=102), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e55（47-63）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e49（42-55）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e321(74.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e1（0.98）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e69(16.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e2（1.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eAsian and other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e39(9.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e99(97.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eLaterality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e227(52.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e45(44.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e202(47.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e57(55.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e28（6.53）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e12（11.76）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e226（52.68）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e48（47.06）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e175（40.79）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e42（41.18）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eExamined lymph nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e6(2-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e9(4-19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003ePositive lymph nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e1(0-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e1(0-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e11（2.56）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e1（0.98）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eBCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e209（48.72）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e29（28.43）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Mastectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e209（48.72）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e72（70.59）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eRadiation therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e194（45.22）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e44（43.14）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e235（54.78）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e58（56.86）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e155（36.13）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e35（34.31）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e274（63.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e67（65.69）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e171（39.86）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e20（19.61）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e258（60.14）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e82（80.39）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e229（53.38）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e40（39.22）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e141（32.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e52（50.98）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e47（10.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e8（7.84）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e12（2.79）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e2（1.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e186（43.36）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e49（48.04）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e141（32.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e42（41.18）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e58（13.52）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e8（7.84）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e44（10.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e3（2.94）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e55（12.82）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e21（20.59）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e374（87.18）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e81，（79.41）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e97（22.61）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e26（25.49）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.50455373406193%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.404371584699454%\"\u003e\n \u003cp\u003e332（77.39）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e76（74.51）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.304189435336976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\n \u003ch2\u003e3.2. Independent prognostic variables in the training cohort\u003c/h2\u003e\n \u003cp\u003eMarital status, ethnicity, surgery, staging, positive lymph nodes, ER and PR were prognostic factors for OS in non-metastatic IMPC (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Five independent risk factors were identified, including race, surgery, positive lymph nodes, T stage, and ER status. The results are summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariable and multivariable Cox analysis for predicting overall survival in non-metastatic IMPC in training cohort\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.527 (0.896\u0026ndash;2.602)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.220 (1.149\u0026ndash;4.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.866 (1.010\u0026ndash;3.779)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian and other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.457 (0.109\u0026ndash;1.921)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.357 (0.079\u0026ndash;1.626)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaterality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.869 (0.471\u0026ndash;1.605)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.350 (0.011\u0026ndash;3.870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.595 (2.478\u0026ndash;9.805)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137 (0.039\u0026ndash;0.480)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.164 (0.037\u0026ndash;0.722)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMastectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.247 (0.074\u0026ndash;0.827)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.124 (0.031\u0026ndash;0.498)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRadiation therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.824 (0.453-1.500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.240 (0.655\u0026ndash;2.349)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExamined lymph nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.021 (0.991\u0026ndash;1.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive lymph nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.207 (1.105\u0026ndash;1.319)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.128 (1.009\u0026ndash;1.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.497 (0.271\u0026ndash;0.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.743 (0.385\u0026ndash;1.432)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.370 (0.627\u0026ndash;2.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.949 (0.399\u0026ndash;2.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.859 (2.210-10.683)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.149 (0.754\u0026ndash;6.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.314 (4.739\u0026ndash;31.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.988 (1.032\u0026ndash;15.410)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.025 (0.864\u0026ndash;4.747)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.935 (0.769\u0026ndash;4.868)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.487 (0.883\u0026ndash;7.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.951 (0.554\u0026ndash;6.864)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.412 (3.242\u0026ndash;16.947)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.636 (0.507\u0026ndash;13.707)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.316 (0.167\u0026ndash;0.599)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.241 (0.073\u0026ndash;0.790)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.481 (0.261\u0026ndash;0.888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.421 (0.466\u0026ndash;4.337)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003e3.3. Establishment of the nomogram of prognosis\u003c/h2\u003e\n \u003cp\u003eThe nomogram forecasting survival time based on the 5 independent risk factors, including race (White, Black, Asian or other), surgery (none, breast conservative surgery (BCS) or mastectomy), number of positive lymph nodes, T stage (T1, T2, T3 or T4) and ER status (negative or positive) is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The risk score for each independent prognostic indicator was calculated by sketching a vertical line from the independent variable. The total risk was then calculated by considering the scores obtained for each variable. The OS at 3, 5 and 8 years was calculated by sketching a line from the total points axis to the corresponding survival axis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003e3.4. Verification of the model\u0026apos;s performance\u003c/h2\u003e\n \u003cp\u003eRegarding OS, the C-index showed a good prognostic discrimination for both training (0.766, 95% CI, 0.682\u0026ndash;0.850) and validation cohorts (0.694, 95% CI, 0.527\u0026ndash;0.861). The AUC values were 0.786 (95%CI: 0.656\u0026ndash;0.916) at 3 years, 0.791 (95%CI: 0.669\u0026ndash;0.912) at 5 years and 0.774 (95%CI: 0.688\u0026ndash;0.860) at 8 years for the training cohort. The AUC values were 0.653 (95%CI: 0.498\u0026ndash;0.808) at 3 years, 0.683 (95%CI: 0.546\u0026ndash;0.820) at 5 years and 0.716 (95%CI: 0.595\u0026ndash;0.836) at 8 years for the external cohort (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). The time-dependent AUC values showed excellent performance and discrimination (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC and D).\u003c/p\u003e\n \u003cp\u003eThe calibration curves indicated a satisfactory agreement between predictions and observed OS in both cohorts (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). The 3-, 5- and 8-year decision curve analysis showed that compared to single-factor models, the nomogram had a favorable net benefit in predicting survival for all threshold probabilities (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec15\"\u003e\n \u003ch2\u003e3.5. Risk stratification\u003c/h2\u003e\n \u003cp\u003eConsidering the total sum score, the optimal cut-off value was 139.3. Patients with a total sum score below this cut-off were identified as low risk patients, whereas those with total sum scores above or equal to the cut-off were identified as high risk patients (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA and B). In the training cohort, we classified 43 high-risk patients and 386 low-risk patients. In the validation cohort, we classified 27 high-risk patients and 75 low-risk patients. The Kaplan-Meier survival curves revealed a different survival between groups in both cohorts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0074) (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC and D).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIMPC is an heterogeneous form of breast cancer. Respect to IDC, IMPC is associated with a worse prognosis\u003csup\u003e21,22,23\u003c/sup\u003e. IMPC has different characteristics from other common breast cancer histological subtypes. As a result, several other factors may affect the prognosis of IMPC besides the tumor stage. We developed a novel risk stratification nomogram to forecast the OS in non-metastatic IMPC. Clinical information of IMPC individuals were retrieved from the SEER database, which covers about 30% of all cancers in the US. However, since the US and Chinese populations differ genetically and demographically, we also validated the model on a cohort of IMPC cases obtained from the Xijing Hospital, one of the biggest hospitals in northwest China, to confirm the applicability and accuracy of the stratification nomogram.\u003c/p\u003e \u003cp\u003eAfter performing univariate and multivariate analyses, we identified 5 independent risk factors for OS, including race, surgery, positive lymph nodes, T stage and ER status. These factors were used to develop a predictive nomogram. Consistent with previous studies, we identified a strong association between positive lymph nodes and worse survival outcomes. Therefore, positive lymph nodes was given the highest weighting and incorporated as a continuous variable to improve the prediction accuracy of the nomogram.\u003c/p\u003e \u003cp\u003eCompared with IDC, IMPC has a higher incidence of lymph involvement ranging from 60\u0026ndash;90% \u003csup\u003e4,24,25,26\u003c/sup\u003e. We described that the incidence of lymph node involvement was higher in the training cohort (56.64%) than in the validation cohort (51.96%), possibly due to variations in the T-stage, ER status, age, and race between the 2 groups. Meanwhile, our analysis indicated that Asian patients with non-metastatic IMPC had a better prognosis than black patients, possibly due to the lower rate of lymphatic metastasis. Though there are no reports of such findings in non-metastatic IMPC, some studies on IDC also showed that Asian breast cancer patients tend to have a better prognosis\u003csup\u003e27,28\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe individuals pertaining to the training cohort reported a lower T stage compared to those in the external cohort. Due to a small tumor size, more patients in the training cohort received BCS respect to the other cohort (48.72% versus 28.43%, \u003cem\u003ep\u003c/em\u003e༜0.001). Interestingly, although previous studies showed that patients treated with BCS and mastectomy had the same prognosis\u003csup\u003e29,30\u003c/sup\u003e, in our study, breast mastectomy was an independent high-risk indicator for OS more than BCS, possibly since patients who underwent BCS tended to have a lower T-stage. Furthermore, various studies also demonstrated that ER expression is an important predictor for OS, recurrence-free survival and breast cancer-specific survival in IMPC patients \u003csup\u003e31,32\u003c/sup\u003e. Our study also identified ER status as an important prognostic indicator.\u003c/p\u003e \u003cp\u003eInconsistent with previously published work, age was not an independent risk factor for OS. Younger patients often tend to have more aggressive advanced breast cancer than older patients\u003csup\u003e33,34\u003c/sup\u003e. However, the proportion of ER-positive patients with breast cancer was relatively high in our cohort. Since ER status greatly impacts OS, age was not identified as an independent risk factor.\u003c/p\u003e \u003cp\u003eIndividuals from the training were significantly older than those in the validation cohort (55 years versus 49 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the SEER database, the median age of women with breast cancer is 61 years\u003csup\u003e35\u003c/sup\u003e. However, in China, the age of women with breast carcinoma is frequently reported from 45 to 55 years. The younger age in Chinese patients could be due to the birth cohort effect, variations in menstrual and reproductive patterns or other environmental factors \u003csup\u003e36\u003c/sup\u003e. Since Chinese patients were younger than those in US, they were likely to present with an advanced breast cancer. Notably, our analysis showed no difference regarding 5-year OS between cohorts. In low-risk groups, the 5-year OS was higher in the training cohort. In high-risk groups, the 5-year OS was lower in the training cohort.\u003c/p\u003e \u003cp\u003eThe AUC, C-index and calibration results showed that the proposed nomogram achieved good discrimination and accuracy in both cohorts. The DCA confirmed that our nomogram could significantly benefit decision-making compared to single-factor models. Additionally, the nomogram could accurately stratify the IMPC patients in high-risk and low-risk groups. According to this stratification, clinicians can more accurately predict the survival time and therefore optimize the follow-up and treatment according to the patient's needs.\u003c/p\u003e \u003cp\u003eOur study has some limitations. The retrospective design and the small sample size in our study could have introduced selection bias, limiting the generalizability of the research findings. Moreover, the SEER database lacked detailed information about important predictive factors for OS, such as chemotherapy treatment, lymphovascular invasion, genetic mutations, and the proportion of the IMPC subtype within the breast sample. Larger prospective studies are recommended to identify the impact of other variables on OS and validate the nomogram's predictive accuracy.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eWe developed a nomogram predicting OS specifically for the IMPC breast cancer subtype. Our novel prognostic nomogram for non-metastatic IMPC patients achieved satisfactory discrimination and predictive accuracy in both cohorts. Clinicians could use the nomogram to optimize the follow-up and treatment in accordance with patient's specific risk factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe recognize the support from TopEdit (www.topeditsci.com) for the language editing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding: None.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Disclosure\u003c/p\u003e\n\u003cp\u003eThe authors have declared no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data availability\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7-34. doi:10.3322/caac.21551.\u003c/li\u003e\n\u003cli\u003eSinn H-P, Kreipe H. 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Sci Rep. 2019;9(1):11921.\u003c/li\u003e\n\u003cli\u003eCollins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. Published 2015 Jan 7. \u003c/li\u003e\n\u003cli\u003eWalsh, M. M., and Bleiweiss I. J. 2001. Invasive micropapillary carcinoma of the breast: eighty cases of an underrecognized entity. Hum. Pathol. 32:583\u0026ndash;589. \u003c/li\u003e\n\u003cli\u003eChen, L., Fan Y., Lang R. G., Guo X. J., Sun Y. L., Cui L. F., et al. 2008. Breast carcinoma with micropapillary features: clinicopathologic study and long‐term follow‐up of 100 cases. Int. J. Surg. Pathol. 16:155\u0026ndash;163.\u003c/li\u003e\n\u003cli\u003eLi, Y. S., Kaneko M., Sakamoto D. G., Takeshima Y., and Inai K.. 2006. The reversed apical pattern of MUC1 expression is characteristics of invasive micropapillary carcinoma of the breast. 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Oncol. 40:781\u0026ndash;787.\u003c/li\u003e\n\u003cli\u003eKo NY, Hong S, Winn RA, Calip GS. Association of Insurance Status and Racial Disparities With the Detection of Early-Stage Breast Cancer. JAMA Oncol. 2020;6(3):385-392.\u003c/li\u003e\n\u003cli\u003eIqbal J, Ginsburg O, Rochon PA, Sun P, Narod SA. Differences in breast cancer stage at diagnosis and cancer-specific survival by race and ethnicity in the United States [published correction appears in JAMA. 2015 Jun 9;313(22):2287]. JAMA. 2015;313(2):165-173. \u003c/li\u003e\n\u003cli\u003eSinnadurai S, Kwong A, Hartman M, et al. Breast-conserving surgery versus mastectomy in young women with breast cancer in Asian settings. BJS Open. 2018;3(1):48-55. Published 2018 Oct 18. \u003c/li\u003e\n\u003cli\u003eVila J, Gandini S, Gentilini O. Overall survival according to type of surgery in young (\u0026le;40 years) early breast cancer patients: A systematic meta-analysis comparing breast-conserving surgery versus mastectomy. 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The prognostic impact of age in different molecular subtypes of breast cancer. Breast Cancer Res Treat. 2015;152(3):667\u0026ndash;673. doi: 10.1007/s10549-015-3491-3.\u003c/li\u003e\n\u003cli\u003eHowlader N, Noone AM, Krapcho M, et al. SEER Cancer Statistics Review, 1975-2012, based on Nov 2014 SEER data submission. Bethesda, MD: National Cancer Institute; 2015.\u003c/li\u003e\n\u003cli\u003eFan L, Strasser-Weippl K, Li JJ, et al. Breast cancer in China. Lancet Oncol. 2014;15(7): e279-e289. doi:10.1016/S1470-2045(13)70567-9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"invasive micropapillary breast carcinoma, overall survival, SEER, prognosis, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-2595093/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2595093/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eWe aimed at establishing a nomogram to accurately forecast the overall survival (OS) of non-metastatic invasive micropapillary breast carcinoma (IMPC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn the training cohort, data from 429 patients with non-metastatic IMPC were obtained through the Surveillance, Epidemiology, and End Results (SEER) database. Other 102 patients were enrolled at the Xijing Hospital as validation cohort. Independent risk factors affecting OS were ascertained using univariate and multivariate Cox regression. A nomogram was established to forecast OS at 3, 5 and 8 years. The concordance index (C-index), the area under a receiver operating characteristic (ROC) curve and calibration curves were utilized to assess calibration, discrimination and predictive accuracy. Finally, the nomogram was utilized to stratify the risk. The OS between groups was compared through Kaplan-Meier survival curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe multivariate analyses revealed that race (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047), surgery (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), positive lymph nodes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), T stage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045) and estrogen receptors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) were independent prognostic risk factors. The C-index was 0.766 (95% CI, 0.682\u0026ndash;0.850) in the training cohort and 0.694 (95% CI, 0.527\u0026ndash;0.861) in the validation cohort. Furthermore, the predicted OS was consistent with actual observation. The AUCs for OS at 3, 5 and and 8 years were 0.786 (95% CI: 0.656\u0026ndash;0.916), 0.791 (95% CI: 0.669\u0026ndash;0.912), and 0.774 (95% CI: 0.688\u0026ndash;0.860) in the training cohort, respectively. The area under the curves (AUCs) for OS at 3, 5 and 8 years were 0.653 (95% CI: 0.498\u0026ndash;0.808), 0.683 (95% CI: 0.546\u0026ndash;0.820), and 0.716 (95% CI: 0.595\u0026ndash;0.836) in the validation cohort, respectively. The Kaplan-Meier survival curves revealed a significant different OS between groups in both cohorts (\u003cem\u003ep\u003c/em\u003e༜0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur novel prognostic nomogram for non-metastatic IMPC patients achieved a good level of accuracy in both cohorts and could be used to optimize the treatment based on the individual risk factors.\u003c/p\u003e","manuscriptTitle":"Authentication of a survival nomogram for non-invasive micropapillary breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-01 19:36:04","doi":"10.21203/rs.3.rs-2595093/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":"2757a4b1-7b5a-4432-ac1f-1bc0f0865b69","owner":[],"postedDate":"March 1st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-31T09:14:25+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-01 19:36:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2595093","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2595093","identity":"rs-2595093","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}