ArticleScientific reports2025
Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohorts.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- AI-based multimodal fusion for preoperative prediction of breast microcalcifications: combining mammography and tomosynthesis.BMC medical imaging · 2026Article
- Validation of the postoperative prognostication tool PREDICT version 2.2 and 3.0 using data from the National cancer center hospital in Japan.Breast cancer (Tokyo, Japan) · 2026Article
- Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
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Authors and funding
8 authors.
Funding
Abstract
Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous disease, highlighting the need for better patient stratification to guide treatment. We developed a deep learning-based survival model and an individualized prognosis system using data from 37,818 TNBC patients in the SEER database (split into training [65%], validation [17.5%], and test [17.5%] sets). The survival model, built using the pysurvival algorithm, achieved strong performance (C-index: 0.824 in validation set, 0.816 in test set), outperforming traditional methods (CPH: 0.781 and 0.785; RSH: 0.779 and 0.766). External validation on a real-world cohort confirmed its robustness (C-index: 0.758). Our individualized prognosis system also showed higher predictive accuracy than traditional AJCC-TNM staging (AUC 0.821 vs. 0.771). These tools improve TNBC prognosis assessment, enable better patient stratification, and provide clinicians with significant treatment recommendations.
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