Development and validation of explainable machine learning models for the prediction of survival in patients with M1 breast cancer
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background: The prognosis of patients with metastatic (M1) breast cancer is controversial, and the prognostic value of local therapy has not been well established. We aimed to develop and validate explainable machine learning (ML)-based survival models to predict overall survival (OS) in this population. Methods: We retrospectively identified 10,214 female patients with histologically confirmed M1 breast cancer diagnosed between January 2013 and December 2018 from the Surveillance, Epidemiology, and End Result (SEER) database, each with a single malignant lesion. Patients with ambiguous or incomplete metastasis data were excluded. Candidate predictors included age; sex; laterality; American Joint Committee on Cancer (AJCC) Tumor, Node, and Metastasis stage; surgery of the primary site; breast subtype; estrogen receptor and progesterone receptor status; marital status; radiotherapy; chemotherapy; tumor grade; histology; and metastasis to the bone, brain, liver, and lung. Two time-to-OS prediction models-a neural network and a Cox proportional hazards model-were trained, internally validated, and externally tested in a cohort of 100 patients with M1 breast cancer from China. Model interpretability was assessed through global and individual feature importance analyses. Results: . 0.632) and in the external validation (concordance index: 0.782 and 0.650). Several important prognostic factors were identified by the deep learning model, including breast subtype, metastatic site, and surgery status. Surgery was associated with improved OS in patients with bone metastases selected after propensity score matching, with 5-year OS rates of 76.9% and 27.2% in the surgery and nonsurgery groups, respectively (P=0.001). Conclusions: We developed and externally validated ML models that accurately predict survival in patients with M1 breast cancer. Breast subtype, metastatic site, and surgery status were the most important factors for survival prediction in this population. Patients with non-triple-negative breast cancer and metastasis to the bone may benefit from surgery, while those with metastasis to the brain, lung, or liver may not.
Source: Gland Surgery
DOI: 10.21037/gs-2025-350
Authors: Long Jin, Qifan Zhao, Shenbo Fu, Zheng Chao, Fei Cao, Jie Wu, Dede Ma, Xulong Zhu, Yuan Zhang
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7128638603
FWCI:
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