AUTHOR=Wang Chenxi , Zhao Xiangqian , Wang Dawei , Wu Jinyun , Lin Jizhen , Huang Weiwei , Shen Yangkun , Chen Qi TITLE=Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer JOURNAL=Frontiers in Immunology VOLUME=Volume 16 - 2025 YEAR=2025 URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1561563 DOI=10.3389/fimmu.2025.1561563 ISSN=1664-3224 ABSTRACT=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.