Construction of A Clinical Prediction Model for Overall Survival and Cancer-Specific Survival in Malignant Phyllode Tumor of the Breast Based on the SEER Database

Construction of A Clinical Prediction Model for Overall Survival and Cancer-Specific Survival in Malignant Phyllode Tumor of the Breast Based on the SEER Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Construction of A Clinical Prediction Model for Overall Survival and Cancer-Specific Survival in Malignant Phyllode Tumor of the Breast Based on the SEER Database Chenggeng Pan, Ruokuo Han, Senzhe Xia, Dingwei Fan, Daqing Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5691529/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Objective Malignant Phyllodes Tumor of the Breast (MPTB) represents a distinct breast tumor subtype associated with a poor prognosis. The objective of this research was to create and verify a nomogram to predict both overall survival (OS) and breast cancer-specific survival (BCSS) for individuals with a diagnosis of MPTB. Methods From the Surveillance, Epidemiology, and End Results (SEER) database, clinicopathological data of MPTB patients diagnosed between 2000 and 2020 were collected. We performed logistic regression analyses to determine the independent factors that predict both OS and BCSS. Subsequently, a nomogram was developed integrating these significant predictors. Model performance was assessed using metrics such as the calibration curve, area under receiver operating characteristic curve (AUC), and decision curve analysis (DCA). Based on the nomogram scores, patients were categorized into low-risk and high-risk groups, and survival differences were then assessed through the log-rank test and Kaplan-Meier curves. Results The study encompassed 1692 MPTB patients, randomly allocated into a training cohort (N = 1188, 70%,) and a validation cohort (N = 504, 30%). Eight independent predictors for OS were identified through univariate and multivariate analyses: age, marital status, income, stage, tumor stage, nodal stage, surgery, and chemotherapy. Additionally, six independent predictors for BCSS were identified through the same analytical approach: age, stage, tumor stage, nodal stage, surgery, and chemotherapy. Nomograms were constructed based on these variables to forecast OS and BCSS rates for patients with MPTB. Evaluation of the model's discriminative ability using AUC demonstrated satisfactory predictive performance for OS and BCSS in both cohorts. Strong concordance between the probabilities observed and those predicted was indicated by the calibration curve. Furthermore, DCA underscored the clinical utility of the nomogram. Conclusions In this investigation, a nomogram was effectively constructed and internally confirmed to forecast OS and BCSS among individuals with MPTB. This predictive tool provides clinicians with essential prognostic information to guide their clinical decision-making processes. malignant phyllodes tumors of the breast nomogram SEER database overall survival cancer-specific survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Phyllodes tumor of the breast (PTB) is an uncommon occurrence characterized by a combination of epithelial and fibrous connective tissue, representing a small fraction (0.3%-1%) of all primary breast tumors[ 1 ]. The classification of PTB includes benign, borderline, and malignant types, which is determined by five histopathological characteristics outlined by the World Health Organization (WHO): cell division figures, abnormal stromal cell morphology, tumor boundary characteristics, increased stromal cell density, and excessive proliferation [ 2 ]. Approximately 10%-15% of PTB are identified as malignant[ 3 – 5 ]. In instances of MPTB, the tumor typically displays invasive margins into the adjacent breast parenchyma, heightened stromal cellularity, diffuseness, regions of stromal overgrowth, pronounced stromal nuclear pleomorphism, and sporadic regions showing vigorous cell division [ 6 ]. MPTB carry a notable risk of local recurrence (up to 30%) and metastasis[ 3 ]. Despite initial treatment involving surgical excision, studies indicate that all PTB, particularly malignant tumors, harbor the potential for recurrence[ 7 – 10 ]. Radiotherapy has demonstrated effectiveness in local disease management but does not impact overall survival[ 11 – 14 ]. Chemotherapy is employed for recurrent and metastatic disease, although its influence on survival outcomes remains uncertain[ 15 – 17 ]. Nomograms can integrate multiple factors to provide individualized assessments of patient prognosis[ 18 ]. Currently, nomograms are extensively utilized in predicting bone cancer, lung cancer and liver cancer [ 19 – 21 ]. Given the dire prognosis associated with MPTB, understanding prognostic factors can aid clinicians in selecting suitable therapeutic approaches, thereby enhancing patient survival and quality of life. Accurate prognostication of malignant tumors can aid in treatment planning and follow-up strategies. However, as far as we are aware, no nomogram has been developed for patients with MPTB to forecast both OS and BCSS simultaneously. Therefore, it is crucial to develop a nomogram for predicting the survival outcomes of MPTB. In this study, we formulated a nomogram to evaluate the OS and BCSS of patients with MPTB, thereby establishing a framework for tailored diagnostic and therapeutic approaches. Methods Source and Extraction of Data Information regarding patients was obtained from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program. From 2000 to 2020, data on all patients diagnosed with MPTB were gathered. The SEER database, which includes about 28 percent of the American population, is composed of 18 cancer registries[ 22 ]. This database provides insights into patient demographics, clinicopathological features, and survival outcomes. As the clinicopathological data used in our study are publicly available and anonymized, ethical endorsement or patient agreement were not required. Our investigation conformed to the regulations established by the SEER database. Demographic information of patients (including age, gender, and ethnicity), treatment approaches (surgical interventions, chemotherapy, and radiation therapy), tumor attributes (such as size, location, grade, and histology), as well as follow-up records (including duration and status of survival) were all gathered during our data collection process. Inclusion criteria encompassed patients diagnosed with MPTB, while exclusion criteria included: (1) male patients; (2) Stage IV according to the American Joint Committee on Cancer (AJCC) criteria or cases with an undetermined stage; (3) unclear surgical information; (4) survival period shorter than one month. Figure 1 illustrates the patient selection procedure. Based on age, we divide all patients into three groups: younger than 40 years, 40 to 69 years, and older than 70 years. Patient race was categorized as white, black, or other (including Pacific/Asian Islander, AK Indian/American Indian). Based on the AJCC staging guidelines, tumor stage was classified as I, II, or III. Univariate and Multivariate Cox Regression Analysis Patients were divided into a training group (70%) and a validation group (30%) through random assignment. Both univariate and multivariate Cox regression analyses were utilized to identify the autonomous risk factors within the training group, while concurrently documenting the hazard ratio (HR) and the 95% confidence interval (CI). Development and Validation of nomogram A nomogram was developed to evaluate the 1-, 3-, 5-, and 10-year OS and BCSS of MPTB patients by utilizing the autonomous risk factors derived from univariate and multivariate Cox regression analyses. Each variable's weight was used to position it on the nomogram, generating different lines corresponding to the points of each variable. By summing up the points assigned to each variable on the nomogram, a comprehensive score was calculated, facilitating the prediction of survival probabilities at various time points. Validation procedures included assessing the AUC, calibration curve and decision curve analysis (DCA). Nomogram's discrimination ability was evaluated by the AUC. while the calibration curve, analyzed through 1,000 bootstrap samples, compared observed and actual values[ 23 ]. In Fig. 4 , the calibration curve displays the plotted results. Proximity to the diagonal line indicates the model's high level of accuracy. The clinical utility of the model was evaluated using the net benefit across different risk thresholds by DCA, an innovative algorithm [ 24 , 25 ]. Following this, patients were stratified into high-risk and low-risk groups based on each patient's individual risk score. Subsequently, survival disparities among the distinct risk categories were compared using the log-rank test and Kaplan-Meier curve. Statistical Analysis Categorical variables were summarized using frequency distributions and compared via the chi-square test. Subsequently, independent predictors for OS and BCSS were identified through both univariate and multivariate logistic regression analyses, laying the foundation for constructing a nomogram. Statistical significance was set at p < 0.05. Data analysis was carried out using the rms package in R software (version 3.6.2) and SPSS version 22.0 (IBM, USA). Results Clinicopathologic characteristics The research involved the inclusion of 1692 individuals who had been diagnosed with MPTB, comprising 884 married individuals (52.2%) and 1233 Caucasians (72.9%). These patients were divided into two groups, a training cohort (N = 1188) and a validation cohort (N = 504). Elaborate clinicopathologic traits are delineated in Table 1 . Both cohorts exhibited comparable clinical and demographic baseline profiles. Notably, most patients (1,204, 71.2%) fell within the 40–69 age group, representing the peak period for MPTB diagnoses. Furthermore, 1,016 patients (60.0%) reported an income below $ 75,000. Regarding tumor grading, 203 patients (12.0%), 1,397 patients (82.6%), and 92 patients (5.44%) were classified as grades I, II, and III, respectively. In terms of disease staging based on the AJCC T stage, 909 patients (53.7%) were categorized as early-stage (T1/T2) while 783 patients (46.3%) were classified as advanced-stage (T3/T4). The majority of patients presented with positive lymph nodes, accounting for 1,668 cases (98.6%). Surgical interventions included partial mastectomy in 901 cases (53.3%) and total mastectomy in 791 cases (46.7%). Furthermore, a significant proportion of patients had not undergone chemotherapy (1,637, 96.7%) or radiotherapy (1,366, 80.7%). Table 1 Patient demographics and pathological characteristics Term ALL Training Validation P N = 1692, n(%) N = 1188, n(%) N = 504, n(%) Age 0.416 =70 198 (11.7%) 138 (11.6%) 60 (11.9%) Marital Status 0.067 Not married 808 (47.8%) 585 (49.2%) 223 (44.2%) Married 884 (52.2%) 603 (50.8%) 281 (55.8%) Race 0.871 White 1233 (72.9%) 870 (73.2%) 363 (72.0%) Black 181 (10.7%) 126 (10.6%) 55 (10.9%) Others 278 (16.4%) 192 (16.2%) 86 (17.1%) Laterality 0.963 Left 829 (49.0%) 583 (49.1%) 246 (48.8%) Right 863 (51.0%) 605 (50.9%) 258 (51.2%) Income 0.394 = $ 75000 676 (40.0%) 483 (40.7%) 193 (38.3%) Stage 0.942 I 203 (12.0%) 143 (12.0%) 60 (11.9%) II 1397 (82.6%) 979 (82.4%) 418 (82.9%) III 92 (5.44%) 66 (5.56%) 26 (5.16%) AJCC.T 0.175 T1/2 909 (53.7%) 625 (52.6%) 284 (56.3%) T3/4 783 (46.3%) 563 (47.4%) 220 (43.7%) AJCC.N 0.875 Negitive 1668 (98.6%) 1172 (98.7%) 496 (98.4%) Positive 24 (1.42%) 16 (1.35%) 8 (1.59%) Surgery 0.032 Partial mastectomy 901 (53.3%) 612 (51.5%) 289 (57.3%) Total mastectomy 791 (46.7%) 576 (48.5%) 215 (42.7%) Radiation 0.018 No/Unknown 1366 (80.7%) 941 (79.2%) 425 (84.3%) Yes 326 (19.3%) 247 (20.8%) 79 (15.7%) Chemotherapy 0.572 No/Unknown 1637 (96.7%) 1147 (96.5%) 490 (97.2%) Yes 55 (3.25%) 41 (3.45%) 14 (2.78%) Predictors for the OS and BCSS of patients with MPTB Initially, a single-variable Cox regression model was employed to discover potential predictive elements, followed by inclusion in a multiple-variable analysis. Findings from both single-variable and multiple-variable Cox regression assessments are outlined in Table 2 . Marital status (HR = 0.69; 95% CI: 0.53–0.89; p = 0.004), income (= $ 75,000; HR = 0.76; 95% CI: 0.59–0.99; p = 0.041), T stage (T1/T2 vs T3/T4; HR = 1.730; 95% CI: 1.28–2.33; p < 0.001), N stage (HR = 2.220; 95% CI: 1.02–4.82; p = 0.043), surgery (HR = 1.570; 95% CI: 1.18–2.09; p = 0.002), chemotherapy (HR = 2.060; 95% CI: 1.26–3.37; p = 0.004), age, and stage were identified as independent predictors of OS. Similarly, T stage (T1/T2 vs T3/T4; HR = 1.910; 95% CI: 1.20–3.06; p = 0.007), N stage (HR = 2.390; 95% CI: 0.93–6.15; p = 0.071), surgery (HR = 2.080; 95% CI: 1.31–3.32; p = 0.002), chemotherapy (HR = 2.340; 95% CI: 1.23–4.43; p = 0.009), age, and stage were identified as independent predictors of BCSS. These variables played a crucial role in developing a nomogram to forecast OS and BCSS among MPTB individuals. Development and validation of the nomogram for OS and BCSS Utilizing the identified predictive factors, a nomogram was developed (Fig. 2 A, B) to predict OS and BCSS for each patient by calculating a clinical score based on the total points. The 1-, 3-, 5-, and 10-year AUC values for OS were 0.857, 0.796, 0.807, and 0.812 in the training cohort, respectively. For BCSS, the corresponding AUC values were 0.831, 0.780, 0.800, and 0.788. And the AUC values for OS were 0.767, 0.774, 0.792, and 0.790 in the validation cohort, while for BCSS, they were 0.782, 0.770, 0.773, and 0.757 (Figs. 3 A-D). The discriminability and accuracy of the prediction model were confirmed by AUC. Calibration curves demonstrated high consistency between predicted and observed values, further affirming the nomogram's accuracy (Figs. 4 A–H). Practical Implementation of the Nomogram in Clinical Settings In contrast to the tumor stage, the Decision Curve Analysis (DCA) evaluations of both the training and validation cohorts demonstrated a greater clinical utility associated with the nomogram (Figs. 5 A-H). The cutoff values were 85 for OS and 118 for BCSS (Figs. 6 A, B). Patients were stratified into high-risk (risk score ≥ 85/118) and low-risk (risk score < 85/118) groups. The Kaplan-Meier plots depicted increased survival rates in low-risk individuals in contrast to their high-risk counterparts (Figs. 7 A-D). Discussion Malignant phyllodes tumor of the breast (MPTB) poses a significant challenge due to its rarity, leading to uncertainties in treatment strategies stemming from the limited high-quality research available. Accurate assessment of prognostic factors is crucial for enhancing patient outcomes, aiding clinicians in making informed treatment decisions. Although the survival of patients with MPTB is affected by multiple clinical factors, previous investigations have rarely fully combined and analyzed these factors. [ 26 – 29 ]. By integrating various factors, the nomogram enables a personalized evaluation of patient prognosis [ 18 ]. Limited attention has been given in recent times to forecasting the prognosis of MPTB. Using the latest population-based cohort data from the SEER database, we investigated the prognostic factors among patients diagnosed with MPTB. Our findings indicate that despite the potential for aggressive local growth and distant spread, women with MPTB generally have a favorable prognosis in terms of both OS and BCSS, consistent with earlier studies[ 30 , 31 ]. The study revealed a higher incidence of MPTB among white individuals compared to black and other racial groups, with the peak occurrence observed in individuals aged 40–69 years. By employing both univariate and multivariate Cox regression analyses, we pinpointed eight autonomous factors influencing OS and six autonomous factors impacting BCSS, including marital status, income, T stage, N stage, surgery type, age, chemotherapy and disease stage. These factors were incorporated into a nomogram to develop a predictive model for estimating the OS and BCSS in individuals with MPTB. The nomogram demonstrates good accuracy and reliability, offering significant value for clinical decision-making. It is user-friendly and easily applicable to a wide range of individuals. Earlier investigations have emphasized multiple prognostic elements like patient age, tumor site, surgical procedure type, and local relapse, all impacting patient survival results. In our study, the significance of age as a separate risk element in MPTB is highlighted, showing that advanced age correlates with diminished survival rates, aligning with established research findings. underscores age as an independent risk factor for MPTB, with older patients exhibiting lower survival rates, consistent with existing literature[ 31 – 33 ]. Like previous studies[ 10 , 17 ], our examination validates a notable link between increased tumor size and decreased overall survival within the multifactorial framework, potentially indicating the aggressive biological characteristics of larger tumors and their unfavorable prognosis. In our research, lymph node metastasis emerged as a standalone prognostic determinant for MPTB results, despite the infrequent occurrence of axillary lymph node engagement, which typically precludes axillary lymph node dissection as a routine procedure[ 34 ]. Surgical tumor resection remains the standard treatment approach, although the choice between mastectomy and breast-conserving surgery (BCS) remains controversial, particularly in MPTB cases. Consistent with our findings, several authors emphasize the importance of achieving negative surgical margins to control recurrence and distant metastasis in MPTB cases[ 10 , 35 – 40 ]. Negative margins are now acknowledged as an autonomous indicator of local recurrence following surgery. The presence of clear margins is recognized as an independent predictor of local recurrence post-surgery. According to the NCCN Guidelines, a minimum margin of 1 cm is recommended for borderline and MPTB, with total mastectomy proposed as a feasible substitute when necessary[ 41 – 43 ]. Uncertainty persists regarding the efficacy of chemotherapy and radiation therapy in the management of PT, primarily due to the absence of randomized controlled trials, which complicates the decision-making process [ 44 ]. An earlier investigation revealed a disparity in the 5-year disease-free survival rates between individuals with malignant tumors who were administered adjuvant radiotherapy and those who were not, showcasing rates of 61% and 25% respectively [ 37 ]. Nonetheless, the observed contrast did not attain statistical significance in their evaluation, with a p-value of 0.16. A separate study demonstrated that postoperative adjuvant radiotherapy led to a notable decrease in the incidence of local recurrence, but it did not result in enhancements in overall survival or disease-free survival rates[ 14 ]. Within our present investigation, there was no statistically significant variance observed in the OS and BCSS between MPTB patients who underwent radiotherapy and those who did not. Chemotherapy, typically reserved for recurrent or metastatic disease, has not shown clear survival benefits in PT patients. Patients diagnosed with recurrent or metastatic PT frequently adhere to the treatment protocols recommended by the NCCN for metastatic soft tissue sarcomas [ 45 ]. Previous studies on adjuvant chemotherapy have not demonstrated improvements in metastasis-free survival[ 16 , 17 ]. Interestingly, our study indicates that MPTB patients who underwent chemotherapy had a poorer prognosis in contrast to those who did not undergo this form of treatment. Although our research offers valuable insights, it is important to acknowledge its inherent limitations. The retrospective nature of the study introduces potential selection biases that could not be fully addressed, leading to baseline characteristic mismatches that may have impacted the analysis outcomes. Furthermore, it is crucial to highlight that the SEER database has a specific classification for PT, with only cases of malignant PT being documented. The potential misinterpretation of borderline tumors as malignant conditions could have influenced the analysis results, potentially leading to a more optimistic prognosis than anticipated based on findings from other clinical studies. In summary, a nomogram has been created and validated to forecast the risks of OS and BCSS in patients with MPTB. We believe this tool can help identify high-risk subgroups within the MPTB population and offer clinicians valuable information to optimize treatment strategies. Declarations Conflicts of interest: The authors report no conflict of interest. Funding sources: This work was supported by the Wenzhou Science and Technology Bureau Project [number Y20240703]. Acknowledgments This work was supported by the Wenzhou Science and Technology Bureau Project [number Y20240703]. Conflicts of Interest The authors report no conflict of interest. Author Contributions C Pan: study concept and design, drafting of the manuscript. R Han: statistical analysis. S Xia: collection and assembly of data. D Fan: critical revision of the manuscript. D Yang: statistical analysis, data acquisition. H Ma: revision for the manuscript. The final article has been approved by all authors. Ethical Statement The authors bear responsibility for all aspects of the study, ensuring that any questions regarding the accuracy or integrity of the work are duly investigated and addressed. The research was carried out in accordance with the Declaration of Helsinki as revised in 2013. Ethical approval for the research protocol was obtained from the Clinical Research Ethics Committee of Wenzhou Central Hospital, and individual consent for this retrospective analysis was waived. References Tse, G.M., et al., Hormonal receptors expression in epithelial cells of mammary phyllodes tumors correlates with pathologic grade of the tumor: a multicenter study of 143 cases. American journal of clinical pathology, 2002. 118 (4): p. 522-526. 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Gradishar, W.J., et al., NCCN guidelines insights: breast cancer, version 1.2017. Journal of the national comprehensive cancer network, 2017. 15 (4): p. 433-451. Table 2 Table 2 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Mar, 2025 Reviews received at journal 06 Mar, 2025 Reviews received at journal 06 Mar, 2025 Reviewers agreed at journal 05 Mar, 2025 Reviewers agreed at journal 05 Mar, 2025 Reviewers agreed at journal 05 Mar, 2025 Reviews received at journal 31 Jan, 2025 Reviewers agreed at journal 30 Jan, 2025 Reviewers invited by journal 09 Jan, 2025 Editor assigned by journal 02 Jan, 2025 Submission checks completed at journal 30 Dec, 2024 First submitted to journal 21 Dec, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5691529","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":395888138,"identity":"cd78fcc8-bdc0-431e-ad72-40f99932dfc6","order_by":0,"name":"Chenggeng Pan","email":"","orcid":"","institution":"Wenzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chenggeng","middleName":"","lastName":"Pan","suffix":""},{"id":395888139,"identity":"02bf4bde-93e3-4a2a-ad41-f4fa9578500e","order_by":1,"name":"Ruokuo Han","email":"","orcid":"","institution":"Wenzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruokuo","middleName":"","lastName":"Han","suffix":""},{"id":395888140,"identity":"d713a9d8-d8da-4055-b782-7c00a93d9448","order_by":2,"name":"Senzhe Xia","email":"","orcid":"","institution":"Wenzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Senzhe","middleName":"","lastName":"Xia","suffix":""},{"id":395888141,"identity":"9e6b2418-a966-47d2-bc32-f1b9993be8af","order_by":3,"name":"Dingwei Fan","email":"","orcid":"","institution":"Wenzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dingwei","middleName":"","lastName":"Fan","suffix":""},{"id":395888142,"identity":"fb29023b-f41e-4ef3-87e7-ec84cb63c512","order_by":4,"name":"Daqing Yang","email":"","orcid":"","institution":"Wenzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Daqing","middleName":"","lastName":"Yang","suffix":""},{"id":395888143,"identity":"c286aa54-e8db-48b6-be8c-bb0c144df0c1","order_by":5,"name":"Haiguang Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYDACCQbGBx8qbHj42RuI18JsOONMmoxkzwHitbAJc7YctjG44UCkDt3Z7deYGRvO8zDcYGD88DGHCC1md86UPS7ccZuHcXYDs+TMbcRouZGTbjzzzG0eZpkDbMy8RGpJk+ZtO8fDJpFAtJb0Y0AtB3h4SNCSAwrkZB4JnoPNxPol/SEwKu3s7Y83H/zwkRgtDAw8BlAGYwNR6oGA/QGxKkfBKBgFo2CkAgCUXzoHiUUBbgAAAABJRU5ErkJggg==","orcid":"","institution":"Wenzhou Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Haiguang","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-12-21 23:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5691529/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5691529/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72755297,"identity":"845a9c7b-3262-4471-a375-b727403c5918","added_by":"auto","created_at":"2025-01-01 16:53:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe patient selection procedure.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5691529/v1/a01eeb13f47a20ce46b08f03.jpg"},{"id":72755294,"identity":"65eb8615-1042-4aeb-bdfb-6b2a1a17ef5b","added_by":"auto","created_at":"2025-01-01 16:53:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2161986,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe nomogram for predicting OS and BCSS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA）Nomogram for predicting;（B）Nomogram for predicting BCSS.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2Bimageonline.comerged.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5691529/v1/24afdd3c2951e57e36cb4f7b.jpg"},{"id":72756274,"identity":"fb472442-07ba-4260-ba92-0ffadca774a2","added_by":"auto","created_at":"2025-01-01 17:09:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1818029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe AUC values for OS and BCSS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) The 1-, 3-, 5-, and 10-year AUC values for OS in the training cohort; (B) The 1-, 3-, 5-, and 10-year AUC values for BCSS in the training cohort; (C) The 1-, 3-, 5-, and 10-year AUC values for OS in the validation cohort; (D) The 1-, 3-, 5-, and 10-year AUC values for BCSS in the validation cohort.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3Dimageonline.comerged.png","url":"https://assets-eu.researchsquare.com/files/rs-5691529/v1/e535d4c2136e9c698b33c917.png"},{"id":72755299,"identity":"28b5290c-a421-4abf-bba1-54fc965c5816","added_by":"auto","created_at":"2025-01-01 16:53:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2798306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe calibration accuracy at 1‐，3‐, 5‐，10-year OS and BCSS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-D) The calibration accuracy at 1‐，3‐, 5‐，10-year OS; (E-H) The calibration accuracy at 1‐，3‐, 5‐，10-year BCSS.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4Dimageonline.comerged.png","url":"https://assets-eu.researchsquare.com/files/rs-5691529/v1/9755149c16f1e1687b634eee.png"},{"id":72755991,"identity":"e3b31bd0-bd31-4065-9127-a280fc3481b4","added_by":"auto","created_at":"2025-01-01 17:01:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3272204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Decision Curve Analysis (DCA) evaluations of both the training and validation cohorts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-D) The DCA of the training cohorts; (E-H) The DCA of the validation cohorts.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5Dimageonline.comerged.png","url":"https://assets-eu.researchsquare.com/files/rs-5691529/v1/a78ef5a143a0749879dfc0ae.png"},{"id":72756038,"identity":"00b8021f-f1e9-4914-a418-f7c40ccff29b","added_by":"auto","created_at":"2025-01-01 17:01:48","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":598168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe cutoff values for OS and BCSS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) The cutoff values were 85 for OS; (B) The cutoff values were 118 for BCSS.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6Bimageonline.comerged.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5691529/v1/c10e10a4057c2cc92d61775f.jpg"},{"id":72755314,"identity":"bb0a07be-87f2-4333-b074-2cf51682d8fa","added_by":"auto","created_at":"2025-01-01 16:53:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2903643,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier curves of OS and BCSS for in the low- and high-risk groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) Kaplan–Meier curves of OS in the low- and high-risk groups in the training cohort (log‐rank P＜0.001); (B) Kaplan–Meier curves of BCSS in the low- and high-risk groups in the training cohort (log‐rank P＜0.001); (C) Kaplan–Meier curves of OS in the low- and high-risk groups in the validation cohort (log‐rank P＜0.001); (D) Kaplan–Meier curves of BCSS in the low- and high-risk groups in the validation cohort (log‐rank P＜0.001).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7Cimageonline.comerged.png","url":"https://assets-eu.researchsquare.com/files/rs-5691529/v1/4fc69d82c8cf0f6c9da096f7.png"},{"id":72756645,"identity":"43218644-199e-47a5-be3b-c7d20e90da4d","added_by":"auto","created_at":"2025-01-01 17:25:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9677435,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5691529/v1/15de3d5a-1e8d-4fad-93ce-d18b00a3b7aa.pdf"},{"id":72755293,"identity":"370fe813-7f50-46d2-bc7a-44205379ecc6","added_by":"auto","created_at":"2025-01-01 16:53:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":113704,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5691529/v1/0ea6e0024e1de777c756c25a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction of A Clinical Prediction Model for Overall Survival and Cancer-Specific Survival in Malignant Phyllode Tumor of the Breast Based on the SEER Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhyllodes tumor of the breast (PTB) is an uncommon occurrence characterized by a combination of epithelial and fibrous connective tissue, representing a small fraction (0.3%-1%) of all primary breast tumors[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The classification of PTB includes benign, borderline, and malignant types, which is determined by five histopathological characteristics outlined by the World Health Organization (WHO): cell division figures, abnormal stromal cell morphology, tumor boundary characteristics, increased stromal cell density, and excessive proliferation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Approximately 10%-15% of PTB are identified as malignant[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In instances of MPTB, the tumor typically displays invasive margins into the adjacent breast parenchyma, heightened stromal cellularity, diffuseness, regions of stromal overgrowth, pronounced stromal nuclear pleomorphism, and sporadic regions showing vigorous cell division [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. MPTB carry a notable risk of local recurrence (up to 30%) and metastasis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite initial treatment involving surgical excision, studies indicate that all PTB, particularly malignant tumors, harbor the potential for recurrence[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Radiotherapy has demonstrated effectiveness in local disease management but does not impact overall survival[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Chemotherapy is employed for recurrent and metastatic disease, although its influence on survival outcomes remains uncertain[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNomograms can integrate multiple factors to provide individualized assessments of patient prognosis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Currently, nomograms are extensively utilized in predicting bone cancer, lung cancer and liver cancer [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Given the dire prognosis associated with MPTB, understanding prognostic factors can aid clinicians in selecting suitable therapeutic approaches, thereby enhancing patient survival and quality of life. Accurate prognostication of malignant tumors can aid in treatment planning and follow-up strategies. However, as far as we are aware, no nomogram has been developed for patients with MPTB to forecast both OS and BCSS simultaneously. Therefore, it is crucial to develop a nomogram for predicting the survival outcomes of MPTB.\u003c/p\u003e \u003cp\u003eIn this study, we formulated a nomogram to evaluate the OS and BCSS of patients with MPTB, thereby establishing a framework for tailored diagnostic and therapeutic approaches.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSource and Extraction of Data\u003c/h2\u003e \u003cp\u003eInformation regarding patients was obtained from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program. From 2000 to 2020, data on all patients diagnosed with MPTB were gathered. The SEER database, which includes about 28 percent of the American population, is composed of 18 cancer registries[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This database provides insights into patient demographics, clinicopathological features, and survival outcomes. As the clinicopathological data used in our study are publicly available and anonymized, ethical endorsement or patient agreement were not required. Our investigation conformed to the regulations established by the SEER database.\u003c/p\u003e \u003cp\u003eDemographic information of patients (including age, gender, and ethnicity), treatment approaches (surgical interventions, chemotherapy, and radiation therapy), tumor attributes (such as size, location, grade, and histology), as well as follow-up records (including duration and status of survival) were all gathered during our data collection process. Inclusion criteria encompassed patients diagnosed with MPTB, while exclusion criteria included: (1) male patients; (2) Stage IV according to the American Joint Committee on Cancer (AJCC) criteria or cases with an undetermined stage; (3) unclear surgical information; (4) survival period shorter than one month. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the patient selection procedure. Based on age, we divide all patients into three groups: younger than 40 years, 40 to 69 years, and older than 70 years. Patient race was categorized as white, black, or other (including Pacific/Asian Islander, AK Indian/American Indian). Based on the AJCC staging guidelines, tumor stage was classified as I, II, or III.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnivariate and Multivariate Cox Regression Analysis\u003c/h3\u003e\n\u003cp\u003ePatients were divided into a training group (70%) and a validation group (30%) through random assignment. Both univariate and multivariate Cox regression analyses were utilized to identify the autonomous risk factors within the training group, while concurrently documenting the hazard ratio (HR) and the 95% confidence interval (CI).\u003c/p\u003e\n\u003ch3\u003eDevelopment and Validation of nomogram\u003c/h3\u003e\n\u003cp\u003eA nomogram was developed to evaluate the 1-, 3-, 5-, and 10-year OS and BCSS of MPTB patients by utilizing the autonomous risk factors derived from univariate and multivariate Cox regression analyses. Each variable's weight was used to position it on the nomogram, generating different lines corresponding to the points of each variable. By summing up the points assigned to each variable on the nomogram, a comprehensive score was calculated, facilitating the prediction of survival probabilities at various time points.\u003c/p\u003e \u003cp\u003eValidation procedures included assessing the AUC, calibration curve and decision curve analysis (DCA). Nomogram's discrimination ability was evaluated by the AUC. while the calibration curve, analyzed through 1,000 bootstrap samples, compared observed and actual values[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the calibration curve displays the plotted results. Proximity to the diagonal line indicates the model's high level of accuracy. The clinical utility of the model was evaluated using the net benefit across different risk thresholds by DCA, an innovative algorithm [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Following this, patients were stratified into high-risk and low-risk groups based on each patient's individual risk score. Subsequently, survival disparities among the distinct risk categories were compared using the log-rank test and Kaplan-Meier curve.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were summarized using frequency distributions and compared via the chi-square test. Subsequently, independent predictors for OS and BCSS were identified through both univariate and multivariate logistic regression analyses, laying the foundation for constructing a nomogram. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Data analysis was carried out using the rms package in R software (version 3.6.2) and SPSS version 22.0 (IBM, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathologic characteristics\u003c/h2\u003e \u003cp\u003eThe research involved the inclusion of 1692 individuals who had been diagnosed with MPTB, comprising 884 married individuals (52.2%) and 1233 Caucasians (72.9%). These patients were divided into two groups, a training cohort (N\u0026thinsp;=\u0026thinsp;1188) and a validation cohort (N\u0026thinsp;=\u0026thinsp;504). Elaborate clinicopathologic traits are delineated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Both cohorts exhibited comparable clinical and demographic baseline profiles. Notably, most patients (1,204, 71.2%) fell within the 40\u0026ndash;69 age group, representing the peak period for MPTB diagnoses. Furthermore, 1,016 patients (60.0%) reported an income below \u003cspan\u003e$\u003c/span\u003e75,000. Regarding tumor grading, 203 patients (12.0%), 1,397 patients (82.6%), and 92 patients (5.44%) were classified as grades I, II, and III, respectively. In terms of disease staging based on the AJCC T stage, 909 patients (53.7%) were categorized as early-stage (T1/T2) while 783 patients (46.3%) were classified as advanced-stage (T3/T4). The majority of patients presented with positive lymph nodes, accounting for 1,668 cases (98.6%). Surgical interventions included partial mastectomy in 901 cases (53.3%) and total mastectomy in 791 cases (46.7%). Furthermore, a significant proportion of patients had not undergone chemotherapy (1,637, 96.7%) or radiotherapy (1,366, 80.7%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographics and pathological characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1692, n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1188, n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;504, n(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e290 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e213 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026thinsp;~\u0026thinsp;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1204 (71.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e837 (70.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e367 (72.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e808 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e585 (49.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e223 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e884 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e603 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e281 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1233 (72.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e870 (73.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e363 (72.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaterality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e829 (49.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e583 (49.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e246 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e863 (51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e605 (50.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e258 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u003cspan\u003e$\u003c/span\u003e75000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1016 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e705 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e311 (61.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=\u003cspan\u003e$\u003c/span\u003e75000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e676 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e483 (40.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e203 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1397 (82.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e979 (82.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e418 (82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92 (5.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (5.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (5.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAJCC.T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e909 (53.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e625 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e284 (56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e783 (46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e563 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e220 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAJCC.N\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegitive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1668 (98.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1172 (98.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e496 (98.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (1.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (1.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (1.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial mastectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e901 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e612 (51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e289 (57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal mastectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e791 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e576 (48.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e215 (42.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1366 (80.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e941 (79.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e425 (84.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e326 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e247 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1637 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1147 (96.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e490 (97.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55 (3.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (3.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (2.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictors for the OS and BCSS of patients with MPTB\u003c/h3\u003e\n\u003cp\u003eInitially, a single-variable Cox regression model was employed to discover potential predictive elements, followed by inclusion in a multiple-variable analysis. Findings from both single-variable and multiple-variable Cox regression assessments are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Marital status (HR\u0026thinsp;=\u0026thinsp;0.69; 95% CI: 0.53\u0026ndash;0.89; p\u0026thinsp;=\u0026thinsp;0.004), income (\u0026lt;\u003cspan\u003e$\u003c/span\u003e75,000 vs \u0026gt;=\u003cspan\u003e$\u003c/span\u003e75,000; HR\u0026thinsp;=\u0026thinsp;0.76; 95% CI: 0.59\u0026ndash;0.99; p\u0026thinsp;=\u0026thinsp;0.041), T stage (T1/T2 vs T3/T4; HR\u0026thinsp;=\u0026thinsp;1.730; 95% CI: 1.28\u0026ndash;2.33; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), N stage (HR\u0026thinsp;=\u0026thinsp;2.220; 95% CI: 1.02\u0026ndash;4.82; p\u0026thinsp;=\u0026thinsp;0.043), surgery (HR\u0026thinsp;=\u0026thinsp;1.570; 95% CI: 1.18\u0026ndash;2.09; p\u0026thinsp;=\u0026thinsp;0.002), chemotherapy (HR\u0026thinsp;=\u0026thinsp;2.060; 95% CI: 1.26\u0026ndash;3.37; p\u0026thinsp;=\u0026thinsp;0.004), age, and stage were identified as independent predictors of OS. Similarly, T stage (T1/T2 vs T3/T4; HR\u0026thinsp;=\u0026thinsp;1.910; 95% CI: 1.20\u0026ndash;3.06; p\u0026thinsp;=\u0026thinsp;0.007), N stage (HR\u0026thinsp;=\u0026thinsp;2.390; 95% CI: 0.93\u0026ndash;6.15; p\u0026thinsp;=\u0026thinsp;0.071), surgery (HR\u0026thinsp;=\u0026thinsp;2.080; 95% CI: 1.31\u0026ndash;3.32; p\u0026thinsp;=\u0026thinsp;0.002), chemotherapy (HR\u0026thinsp;=\u0026thinsp;2.340; 95% CI: 1.23\u0026ndash;4.43; p\u0026thinsp;=\u0026thinsp;0.009), age, and stage were identified as independent predictors of BCSS. These variables played a crucial role in developing a nomogram to forecast OS and BCSS among MPTB individuals.\u003c/p\u003e\u003cp\u003eDevelopment and validation of the nomogram for OS and BCSS\u003c/p\u003e\n\u003cp\u003eUtilizing the identified predictive factors, a nomogram was developed (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, B) to predict OS and BCSS for each patient by calculating a clinical score based on the total points. The 1-, 3-, 5-, and 10-year AUC values for OS were 0.857, 0.796, 0.807, and 0.812 in the training cohort, respectively. For BCSS, the corresponding AUC values were 0.831, 0.780, 0.800, and 0.788. And the AUC values for OS were 0.767, 0.774, 0.792, and 0.790 in the validation cohort, while for BCSS, they were 0.782, 0.770, 0.773, and 0.757 (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-D). The discriminability and accuracy of the prediction model were confirmed by AUC. Calibration curves demonstrated high consistency between predicted and observed values, further affirming the nomogram\u0026apos;s accuracy (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;H).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003ePractical Implementation of the Nomogram in Clinical Settings\u003c/h2\u003e\n \u003cp\u003eIn contrast to the tumor stage, the Decision Curve Analysis (DCA) evaluations of both the training and validation cohorts demonstrated a greater clinical utility associated with the nomogram (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA-H). The cutoff values were 85 for OS and 118 for BCSS (Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). Patients were stratified into high-risk (risk score\u0026thinsp;\u0026ge;\u0026thinsp;85/118) and low-risk (risk score\u0026thinsp;\u0026lt;\u0026thinsp;85/118) groups. The Kaplan-Meier plots depicted increased survival rates in low-risk individuals in contrast to their high-risk counterparts (Figs. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA-D).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMalignant phyllodes tumor of the breast (MPTB) poses a significant challenge due to its rarity, leading to uncertainties in treatment strategies stemming from the limited high-quality research available. Accurate assessment of prognostic factors is crucial for enhancing patient outcomes, aiding clinicians in making informed treatment decisions.\u003c/p\u003e \u003cp\u003eAlthough the survival of patients with MPTB is affected by multiple clinical factors, previous investigations have rarely fully combined and analyzed these factors. [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. By integrating various factors, the nomogram enables a personalized evaluation of patient prognosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Limited attention has been given in recent times to forecasting the prognosis of MPTB.\u003c/p\u003e \u003cp\u003eUsing the latest population-based cohort data from the SEER database, we investigated the prognostic factors among patients diagnosed with MPTB. Our findings indicate that despite the potential for aggressive local growth and distant spread, women with MPTB generally have a favorable prognosis in terms of both OS and BCSS, consistent with earlier studies[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The study revealed a higher incidence of MPTB among white individuals compared to black and other racial groups, with the peak occurrence observed in individuals aged 40\u0026ndash;69 years. By employing both univariate and multivariate Cox regression analyses, we pinpointed eight autonomous factors influencing OS and six autonomous factors impacting BCSS, including marital status, income, T stage, N stage, surgery type, age, chemotherapy and disease stage. These factors were incorporated into a nomogram to develop a predictive model for estimating the OS and BCSS in individuals with MPTB. The nomogram demonstrates good accuracy and reliability, offering significant value for clinical decision-making. It is user-friendly and easily applicable to a wide range of individuals.\u003c/p\u003e \u003cp\u003eEarlier investigations have emphasized multiple prognostic elements like patient age, tumor site, surgical procedure type, and local relapse, all impacting patient survival results. In our study, the significance of age as a separate risk element in MPTB is highlighted, showing that advanced age correlates with diminished survival rates, aligning with established research findings. underscores age as an independent risk factor for MPTB, with older patients exhibiting lower survival rates, consistent with existing literature[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Like previous studies[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], our examination validates a notable link between increased tumor size and decreased overall survival within the multifactorial framework, potentially indicating the aggressive biological characteristics of larger tumors and their unfavorable prognosis. In our research, lymph node metastasis emerged as a standalone prognostic determinant for MPTB results, despite the infrequent occurrence of axillary lymph node engagement, which typically precludes axillary lymph node dissection as a routine procedure[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSurgical tumor resection remains the standard treatment approach, although the choice between mastectomy and breast-conserving surgery (BCS) remains controversial, particularly in MPTB cases. Consistent with our findings, several authors emphasize the importance of achieving negative surgical margins to control recurrence and distant metastasis in MPTB cases[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Negative margins are now acknowledged as an autonomous indicator of local recurrence following surgery. The presence of clear margins is recognized as an independent predictor of local recurrence post-surgery. According to the NCCN Guidelines, a minimum margin of 1 cm is recommended for borderline and MPTB, with total mastectomy proposed as a feasible substitute when necessary[\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUncertainty persists regarding the efficacy of chemotherapy and radiation therapy in the management of PT, primarily due to the absence of randomized controlled trials, which complicates the decision-making process [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. An earlier investigation revealed a disparity in the 5-year disease-free survival rates between individuals with malignant tumors who were administered adjuvant radiotherapy and those who were not, showcasing rates of 61% and 25% respectively [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Nonetheless, the observed contrast did not attain statistical significance in their evaluation, with a p-value of 0.16. A separate study demonstrated that postoperative adjuvant radiotherapy led to a notable decrease in the incidence of local recurrence, but it did not result in enhancements in overall survival or disease-free survival rates[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Within our present investigation, there was no statistically significant variance observed in the OS and BCSS between MPTB patients who underwent radiotherapy and those who did not. Chemotherapy, typically reserved for recurrent or metastatic disease, has not shown clear survival benefits in PT patients. Patients diagnosed with recurrent or metastatic PT frequently adhere to the treatment protocols recommended by the NCCN for metastatic soft tissue sarcomas [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Previous studies on adjuvant chemotherapy have not demonstrated improvements in metastasis-free survival[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Interestingly, our study indicates that MPTB patients who underwent chemotherapy had a poorer prognosis in contrast to those who did not undergo this form of treatment.\u003c/p\u003e \u003cp\u003eAlthough our research offers valuable insights, it is important to acknowledge its inherent limitations. The retrospective nature of the study introduces potential selection biases that could not be fully addressed, leading to baseline characteristic mismatches that may have impacted the analysis outcomes. Furthermore, it is crucial to highlight that the SEER database has a specific classification for PT, with only cases of malignant PT being documented. The potential misinterpretation of borderline tumors as malignant conditions could have influenced the analysis results, potentially leading to a more optimistic prognosis than anticipated based on findings from other clinical studies.\u003c/p\u003e \u003cp\u003eIn summary, a nomogram has been created and validated to forecast the risks of OS and BCSS in patients with MPTB. We believe this tool can help identify high-risk subgroups within the MPTB population and offer clinicians valuable information to optimize treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors report no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources:\u003c/strong\u003e This work was supported by the Wenzhou Science and Technology Bureau Project [number Y20240703].\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Wenzhou Science and Technology Bureau Project [number Y20240703].\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors report no conflict of interest.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eC Pan: study concept and design, drafting of the manuscript.\u003c/p\u003e\n\u003cp\u003eR Han: statistical analysis.\u003c/p\u003e\n\u003cp\u003eS Xia: collection and assembly of data.\u003c/p\u003e\n\u003cp\u003eD Fan: critical revision of the manuscript.\u003c/p\u003e\n\u003cp\u003eD Yang: statistical analysis, data acquisition.\u003c/p\u003e\n\u003cp\u003eH Ma: revision for the manuscript.\u003c/p\u003e\n\u003cp\u003eThe final article has been approved by all authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical Statement\u003c/p\u003e\n\u003cp\u003eThe authors bear responsibility for all aspects of the study, ensuring that any questions regarding the accuracy or integrity of the work are duly investigated and addressed. The research was carried out in accordance with the Declaration of Helsinki as revised in 2013. Ethical approval for the research protocol was obtained from the Clinical Research Ethics Committee of Wenzhou Central Hospital, and individual consent for this retrospective analysis was waived.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTse, G.M., et al., \u003cem\u003eHormonal receptors expression in epithelial cells of mammary phyllodes tumors correlates with pathologic grade of the tumor: a multicenter study of 143 cases.\u003c/em\u003e American journal of clinical pathology, 2002. \u003cstrong\u003e118\u003c/strong\u003e(4): p. 522-526.\u003c/li\u003e\n\u003cli\u003eZhou, Z.-R., et al., \u003cem\u003ePhyllodes tumors of the breast: diagnosis, treatment and prognostic factors related to recurrence.\u003c/em\u003e Journal of Thoracic Disease, 2016. \u003cstrong\u003e8\u003c/strong\u003e(11): p. 3361.\u003c/li\u003e\n\u003cli\u003ePapas, Y., et al., \u003cem\u003eMalignant phyllodes tumors of the breast: a comprehensive literature review.\u003c/em\u003e The Breast Journal, 2020. \u003cstrong\u003e26\u003c/strong\u003e(2): p. 240-244.\u003c/li\u003e\n\u003cli\u003eZhang, Y. and C.G. 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Stroud, \u003cem\u003eMalignant phyllodes tumors of the breast: a study in clinical practice.\u003c/em\u003e International surgery, 2012. \u003cstrong\u003e97\u003c/strong\u003e(2): p. 95-98.\u003c/li\u003e\n\u003cli\u003eChoi, N., et al., \u003cem\u003eMalignant and borderline phyllodes tumors of the breast: a multicenter study of 362 patients (KROG 16-08).\u003c/em\u003e Breast Cancer Research and Treatment, 2018. \u003cstrong\u003e171\u003c/strong\u003e: p. 335-344.\u003c/li\u003e\n\u003cli\u003eChen, C., et al., \u003cem\u003eRethinking on the management strategy of malignant phyllodes tumor of the breast: An analysis based on the SEER database.\u003c/em\u003e Medicine (Baltimore), 2023. \u003cstrong\u003e102\u003c/strong\u003e(12): p. e33326.\u003c/li\u003e\n\u003cli\u003eWei, Y., et al., \u003cem\u003ePredicting the recurrence-free survival of phyllodes tumor of the breast: a nomogram based on clinicopathology features, treatment, and surgical margin.\u003c/em\u003e Gland Surg, 2023. \u003cstrong\u003e12\u003c/strong\u003e(2): p. 152-164.\u003c/li\u003e\n\u003cli\u003eSpanheimer, P.M., et al., \u003cem\u003eLong-term outcomes after surgical treatment of malignant/borderline phyllodes tumors of the breast.\u003c/em\u003e Annals of surgical oncology, 2019. \u003cstrong\u003e26\u003c/strong\u003e: p. 2136-2143.\u003c/li\u003e\n\u003cli\u003eZhang, H., et al., \u003cem\u003eLong-term Survival After Diverse Therapeutic Modalities in Malignant Phyllodes Tumors of the Breast.\u003c/em\u003e Technol Cancer Res Treat, 2022. \u003cstrong\u003e21\u003c/strong\u003e: p. 15330338221121086.\u003c/li\u003e\n\u003cli\u003eCheng, S.-P., et al., \u003cem\u003ePhyllodes tumor of the breast: the challenge persists.\u003c/em\u003e World journal of surgery, 2006. \u003cstrong\u003e30\u003c/strong\u003e: p. 1414-1421.\u003c/li\u003e\n\u003cli\u003eMangi, A.A., et al., \u003cem\u003eSurgical management of phyllodes tumors.\u003c/em\u003e Archives of Surgery, 1999. \u003cstrong\u003e134\u003c/strong\u003e(5): p. 487-493.\u003c/li\u003e\n\u003cli\u003ePandey, M., et al., \u003cem\u003ePrimary sarcoma of the breast.\u003c/em\u003e Journal of surgical oncology, 2004. \u003cstrong\u003e87\u003c/strong\u003e(3): p. 121-125.\u003c/li\u003e\n\u003cli\u003eDamak, T., et al., \u003cem\u003ePhyllodes tumors of the breast: a case series of 106 patients.\u003c/em\u003e The American journal of surgery, 2006. \u003cstrong\u003e192\u003c/strong\u003e(2): p. 141-147.\u003c/li\u003e\n\u003cli\u003eBhargav, P., et al., \u003cem\u003ePhyllodes tumour of the breast: clinicopathological analysis of recurrent vs. non-recurrent cases.\u003c/em\u003e Asian journal of surgery, 2009. \u003cstrong\u003e32\u003c/strong\u003e(4): p. 224-228.\u003c/li\u003e\n\u003cli\u003eHaberer, S., et al., \u003cem\u003eManagement of malignant phyllodes tumors of the breast: the experience of the Institut Curie.\u003c/em\u003e Cancer Radiotherapie: Journal de la Societe Francaise de Radiotherapie Oncologique, 2009. \u003cstrong\u003e13\u003c/strong\u003e(4): p. 305-312.\u003c/li\u003e\n\u003cli\u003eJang, J.H., et al., \u003cem\u003eClinicopathologic risk factors for the local recurrence of phyllodes tumors of the breast.\u003c/em\u003e Annals of surgical oncology, 2012. \u003cstrong\u003e19\u003c/strong\u003e: p. 2612-2617.\u003c/li\u003e\n\u003cli\u003eSawalhi, S. and M. Al-Shatti, \u003cem\u003ePhyllodes tumor of the breast: a retrospective study of the impact of histopathological factors in local recurrence and distant metastasis.\u003c/em\u003e Annals of Saudi medicine, 2013. \u003cstrong\u003e33\u003c/strong\u003e(2): p. 162-168.\u003c/li\u003e\n\u003cli\u003eFajdić, J., et al., \u003cem\u003ePhyllodes tumors of the breast\u0026ndash;diagnostic and therapeutic dilemmas.\u003c/em\u003e Oncology research and treatment, 2007. \u003cstrong\u003e30\u003c/strong\u003e(3): p. 113-118.\u003c/li\u003e\n\u003cli\u003eHolthouse, D.J., et al., \u003cem\u003eCystosarcoma phyllodes: the Western Australian experience.\u003c/em\u003e Australian and New Zealand journal of surgery, 1999. \u003cstrong\u003e69\u003c/strong\u003e(9): p. 635-638.\u003c/li\u003e\n\u003cli\u003eGradishar, W.J., et al., \u003cem\u003eNCCN guidelines insights: breast cancer, version 1.2017.\u003c/em\u003e Journal of the national comprehensive cancer network, 2017. \u003cstrong\u003e15\u003c/strong\u003e(4): p. 433-451.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"malignant phyllodes tumors of the breast, nomogram, SEER database, overall survival, cancer-specific survival","lastPublishedDoi":"10.21203/rs.3.rs-5691529/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5691529/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eMalignant Phyllodes Tumor of the Breast (MPTB) represents a distinct breast tumor subtype associated with a poor prognosis. The objective of this research was to create and verify a nomogram to predict both overall survival (OS) and breast cancer-specific survival (BCSS) for individuals with a diagnosis of MPTB.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFrom the Surveillance, Epidemiology, and End Results (SEER) database, clinicopathological data of MPTB patients diagnosed between 2000 and 2020 were collected. We performed logistic regression analyses to determine the independent factors that predict both OS and BCSS. Subsequently, a nomogram was developed integrating these significant predictors. Model performance was assessed using metrics such as the calibration curve, area under receiver operating characteristic curve (AUC), and decision curve analysis (DCA). Based on the nomogram scores, patients were categorized into low-risk and high-risk groups, and survival differences were then assessed through the log-rank test and Kaplan-Meier curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study encompassed 1692 MPTB patients, randomly allocated into a training cohort (N\u0026thinsp;=\u0026thinsp;1188, 70%,) and a validation cohort (N\u0026thinsp;=\u0026thinsp;504, 30%). Eight independent predictors for OS were identified through univariate and multivariate analyses: age, marital status, income, stage, tumor stage, nodal stage, surgery, and chemotherapy. Additionally, six independent predictors for BCSS were identified through the same analytical approach: age, stage, tumor stage, nodal stage, surgery, and chemotherapy. Nomograms were constructed based on these variables to forecast OS and BCSS rates for patients with MPTB. Evaluation of the model's discriminative ability using AUC demonstrated satisfactory predictive performance for OS and BCSS in both cohorts. Strong concordance between the probabilities observed and those predicted was indicated by the calibration curve. Furthermore, DCA underscored the clinical utility of the nomogram.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn this investigation, a nomogram was effectively constructed and internally confirmed to forecast OS and BCSS among individuals with MPTB. This predictive tool provides clinicians with essential prognostic information to guide their clinical decision-making processes.\u003c/p\u003e","manuscriptTitle":"Construction of A Clinical Prediction Model for Overall Survival and Cancer-Specific Survival in Malignant Phyllode Tumor of the Breast Based on the SEER Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-01 16:53:35","doi":"10.21203/rs.3.rs-5691529/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-07T06:53:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-07T00:59:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-06T12:48:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315754786479437020667014989047333809765","date":"2025-03-06T00:55:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8157660990289094148541970978684581393","date":"2025-03-05T14:52:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119233954534391346617300511217539054998","date":"2025-03-05T10:00:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-31T21:59:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155570047709513191081028620781588844682","date":"2025-01-30T09:41:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-09T05:26:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-02T07:20:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-30T15:33:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2024-12-21T23:45:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01e5e6c4-5435-4dad-a81f-ad3903aabbf8","owner":[],"postedDate":"January 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-18T11:23:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-01 16:53:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5691529","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5691529","identity":"rs-5691529","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}