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August 21, 2025Scientific Reports5 citationsOpen Access

Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohorts

YXYiyue XuChinese University of Hong Kong BLButuo LiShandong Tumor Hospital BZBing ZouNanchang University

Key Points

The deep learning survival model shows a C-index of 0.824 in validation, indicating strong predictive capabilities for triple-negative breast cancer patients.
Compared to traditional methods, the model outperformed CPH and RSH with significant enhancements in patient prognosis assessment accuracy.
Analysis used a dataset of 37,818 patients, split into training, validation, and test sets, ensuring robust performance through validation.
The prognosis tools provided better stratification than traditional staging systems, highlighting their potential for clinical use in treatment planning.

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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