Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer
IntroductionTriple-negative breast cancer (TNBC) is a highly aggressive breast cancer subtype associated with poor prognosis and limited treatment options. This study utilized the SEER database to investigate clinicopathologic characteristics and prognostic factors in TNBC patients.MethodsMachine le...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1561563/full |
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| author | Chenxi Wang Xiangqian Zhao Dawei Wang Jinyun Wu Jizhen Lin Weiwei Huang Yangkun Shen Qi Chen |
| author_facet | Chenxi Wang Xiangqian Zhao Dawei Wang Jinyun Wu Jizhen Lin Weiwei Huang Yangkun Shen Qi Chen |
| author_sort | Chenxi Wang |
| collection | DOAJ |
| description | IntroductionTriple-negative breast cancer (TNBC) is a highly aggressive breast cancer subtype associated with poor prognosis and limited treatment options. This study utilized the SEER database to investigate clinicopathologic characteristics and prognostic factors in TNBC patients.MethodsMachine learning algorithms specifically Gradient Boosting Machines (XGBoost) and Random Forest classifiers were applied to develop survival prediction models and identify key prognostic markers.ResultsResults indicated significant predictors of survival, including tumor size, lymph node involvement, and distant metastases. Our proposed work showed better predictive performance, with a C-index of 0.8544 and AUC-ROC values of 0.9008 and 0.8344 for one year and three year overall survival predictions. Major predictors of survival comprises tumor size, HR is 3.657 for T4, lymph node involvement, HR is 3.018 for N3, distant metastases, HR is 1.743 for M1, and prior treatments includes surgery, HR is 0.298, chemotherapy, HR is 0.442, and radiotherapy, HR is 0.607.DiscussionThe findings emphasize the clinical utility of AI-driven models in improving TNBC prognosis and guiding personalized treatment strategies. This study provides novel insights into the survival dynamics of TNBC patients and underscores the potential of predictive analytics in oncology. |
| format | Article |
| id | doaj-art-413cb9ff2d2e474d9bdb754c19349028 |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-413cb9ff2d2e474d9bdb754c193490282025-08-20T02:57:32ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-04-011610.3389/fimmu.2025.15615631561563Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancerChenxi Wang0Xiangqian Zhao1Dawei Wang2Jinyun Wu3Jizhen Lin4Weiwei Huang5Yangkun Shen6Qi Chen7Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, Fujian, ChinaThe Cancer Center, Fujian Medical University Union Hospital, Fuzhou, Fujian, ChinaFujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, Fujian, ChinaFujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, Fujian, ChinaThe Cancer Center, Fujian Medical University Union Hospital, Fuzhou, Fujian, ChinaDepartment of Medical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian Provincial Key Laboratory of Translational Cancer Medicine, Fuzhou, Fujian, ChinaFujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, Fujian, ChinaFujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, Fujian, ChinaIntroductionTriple-negative breast cancer (TNBC) is a highly aggressive breast cancer subtype associated with poor prognosis and limited treatment options. This study utilized the SEER database to investigate clinicopathologic characteristics and prognostic factors in TNBC patients.MethodsMachine learning algorithms specifically Gradient Boosting Machines (XGBoost) and Random Forest classifiers were applied to develop survival prediction models and identify key prognostic markers.ResultsResults indicated significant predictors of survival, including tumor size, lymph node involvement, and distant metastases. Our proposed work showed better predictive performance, with a C-index of 0.8544 and AUC-ROC values of 0.9008 and 0.8344 for one year and three year overall survival predictions. Major predictors of survival comprises tumor size, HR is 3.657 for T4, lymph node involvement, HR is 3.018 for N3, distant metastases, HR is 1.743 for M1, and prior treatments includes surgery, HR is 0.298, chemotherapy, HR is 0.442, and radiotherapy, HR is 0.607.DiscussionThe findings emphasize the clinical utility of AI-driven models in improving TNBC prognosis and guiding personalized treatment strategies. This study provides novel insights into the survival dynamics of TNBC patients and underscores the potential of predictive analytics in oncology.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1561563/fulltriple-negative breast cancerSEER databaseprognostic modellingcolumnar plotsmachine learning |
| spellingShingle | Chenxi Wang Xiangqian Zhao Dawei Wang Jinyun Wu Jizhen Lin Weiwei Huang Yangkun Shen Qi Chen Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer Frontiers in Immunology triple-negative breast cancer SEER database prognostic modelling columnar plots machine learning |
| title | Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer |
| title_full | Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer |
| title_fullStr | Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer |
| title_full_unstemmed | Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer |
| title_short | Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer |
| title_sort | analysis of prognostic factors and nomogram construction for postoperative survival of triple negative breast cancer |
| topic | triple-negative breast cancer SEER database prognostic modelling columnar plots machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1561563/full |
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