Frontiers in endocrinology · 2026
Development and validation of a prognostic model for stage IV breast cancer based on primary tumor resection with machine learning methods: retrospective cohort study.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Wang Yaoling, Hou Jingyu, Chen Xinhai, Zhu Hongyi, Yao Yuan, Zhao Wenjing, Mo Shuangwei, Xiong Zhenchong, Yang Anli, Liu Wei, Lai Yuanhui, Xiao Weikai
Аннотация
BACKGROUND: Primary Tumor Resection (PTR) remains controversial among women with stage IV breast cancer.
OBJECTIVE: Using a machine learning (ML) approach, this study investigates how PTR is associated with survival outcomes in stage IV breast cancer. We aim to develop a model capable of identifying patient characteristics linked to better prognosis following PTR, ultimately providing a data-informed tool to support prognostic assessment and clinical evaluation.
METHODS: A propensity-score matched analysis of stage IV breast cancer patients in the SEER registry (2000-2020) was conducted, using Cox regression and Kaplan-Meier methods to estimate overall survival (OS) and cancer-specific survival (CSS). Among five ML models, an internally and externally validated ML model was crafted for predicting survival outcomes, with a user-friendly web platform based on the shiny platform for clinical use.
RESULTS: In a cohort of 10,194 stage IV breast cancer patients, with 5,732 matched subjects, Cox regression analysis showed PTR's positive associations with OS (HR, 0.61; 95% CI, 0.57 to 0.66) and CSS (HR, 0.64; 95% CI, 0.59 to 0.67). Subgroup analysis indicated better survival for patients with tumors ≤5 cm, N2 status (OS, HR, 0.52; 95% CI, 0.44 - 0.63;CSS,HR, 0.52; 95% CI, 0.43 - 0.63), and HER2 overexpression (OS, HR, 0.56; 95% CI, 0.52 - 0.61;CSS,HR, 0.56; 95% CI, 0.46 - 0.67), especially those without systemic treatment. The best outcomes were seen with trimodality therapy combining PTR, chemotherapy, and radiotherapy (OS, HR, 0.40; 95% CI, 0.37 - 0.44; CSS, HR, 0.03; 95% CI, 0.01 - 0.10). The Support Vector Machine (SVM) model (6-month: AUC = 0.935; 1-year: AUC = 0.945; 2-year: AUC = 0.941; 3-year: AUC = 0.921) was identified as the most precise tool for survival prediction, demonstrating high accuracy and consistency across external datasets. Furthermore, a user-friendly web application was created to make the prognostic model more accessible.
CONCLUSIONS: Patients with tumors ≤5 cm, N2 status, or HER2 overexpression were associated with better survival after PTR. Our ML model, which is based on eight clinical indicators, predicts survival and assists in identifying surgical candidates for stage IV cancer.
OBJECTIVE: Using a machine learning (ML) approach, this study investigates how PTR is associated with survival outcomes in stage IV breast cancer. We aim to develop a model capable of identifying patient characteristics linked to better prognosis following PTR, ultimately providing a data-informed tool to support prognostic assessment and clinical evaluation.
METHODS: A propensity-score matched analysis of stage IV breast cancer patients in the SEER registry (2000-2020) was conducted, using Cox regression and Kaplan-Meier methods to estimate overall survival (OS) and cancer-specific survival (CSS). Among five ML models, an internally and externally validated ML model was crafted for predicting survival outcomes, with a user-friendly web platform based on the shiny platform for clinical use.
RESULTS: In a cohort of 10,194 stage IV breast cancer patients, with 5,732 matched subjects, Cox regression analysis showed PTR's positive associations with OS (HR, 0.61; 95% CI, 0.57 to 0.66) and CSS (HR, 0.64; 95% CI, 0.59 to 0.67). Subgroup analysis indicated better survival for patients with tumors ≤5 cm, N2 status (OS, HR, 0.52; 95% CI, 0.44 - 0.63;CSS,HR, 0.52; 95% CI, 0.43 - 0.63), and HER2 overexpression (OS, HR, 0.56; 95% CI, 0.52 - 0.61;CSS,HR, 0.56; 95% CI, 0.46 - 0.67), especially those without systemic treatment. The best outcomes were seen with trimodality therapy combining PTR, chemotherapy, and radiotherapy (OS, HR, 0.40; 95% CI, 0.37 - 0.44; CSS, HR, 0.03; 95% CI, 0.01 - 0.10). The Support Vector Machine (SVM) model (6-month: AUC = 0.935; 1-year: AUC = 0.945; 2-year: AUC = 0.941; 3-year: AUC = 0.921) was identified as the most precise tool for survival prediction, demonstrating high accuracy and consistency across external datasets. Furthermore, a user-friendly web application was created to make the prognostic model more accessible.
CONCLUSIONS: Patients with tumors ≤5 cm, N2 status, or HER2 overexpression were associated with better survival after PTR. Our ML model, which is based on eight clinical indicators, predicts survival and assists in identifying surgical candidates for stage IV cancer.