Interpretable machine learning for survival prediction and risk stratification in elderly patients with breast cancer after breast-conserving surgery

We successfully established a prognostic risk stratification system identifying three distinct survival groups (low-risk, intermediate-risk, and high-risk). The low-risk group's 5-year survival matched radiotherapy-treated patients in a previous study (Yang <i>et al.</i>, 88.6%). Our system provides prognostic information that, integrated with existing radiotherapy evidence, can inform individualised treatment discussions. Prospective studies comparing radiotherapy outcomes within risk strata are needed to validate clinical utility for treatment decision-making.

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Publication Details

Journal
Gland Surgery
Published
2026-02-01
DOI
https://doi.org/10.21037/gs-2025-aw-462
Primary Topic
AI in cancer detection
Type
article
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article

Interpretable machine learning for survival prediction and risk stratification in elderly patients with breast cancer after breast-conserving surgery

Qingyi Ling, Zhenhua Sun
Gland Surgery
AI in cancer detection
article

Interpretable machine learning for survival prediction and risk stratification in elderly patients with breast cancer after breast-conserving surgery

Qingyi Ling, Zhenhua Sun
article en

Abstract

We successfully established a prognostic risk stratification system identifying three distinct survival groups (low-risk, intermediate-risk, and high-risk). The low-risk group's 5-year survival matched radiotherapy-treated patients in a previous study (Yang <i>et al.</i>, 88.6%). Our system provides prognostic information that, integrated with existing radiotherapy evidence, can inform individualised treatment discussions. Prospective studies comparing radiotherapy outcomes within risk strata are needed to validate clinical utility for treatment decision-making.

Gland SurgeryVol. 15(2)
Jiangsu University (CN), Affiliated Hospital of Jiangsu University (CN)
Openalex Percentile: Top 59%
AI in cancer detection
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