Scholarlink

<p>Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients with estrogen receptor-positive, HER2-negative (ER + /HER2 − ) early breast cancer (EBC). Our deep learning model, RlapsRisk BC, independently predicts MFS and provides significant prognostic value beyond traditional clinico-pathological variables (C-index 0.81 vs 0.76, p &lt; 0.05). Applying a 5% MFS event probability threshold stratifies patients into low- and high-risk groups. After dichotomization, combining RlapsRisk BC with clinico-pathological factors increases cumulative sensitivity (0.69 vs 0.63) and dynamic specificity (0.80 vs 0.76) compared to clinical factors alone. Expert analysis of high-impact regions identified by the model highlights well-established morphological features, supporting its interpretability and biological relevance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

查看全文
  • I. Garberis,
  • V. Gaury,
  • C. Saillard,
  • D. Drubay,
  • K. Elgui,
  • B. Schmauch,
  • A. Jaeger,
  • L. Herpin,
  • J. Linhart,
  • M. Sapateiro,
  • F. Bernigole,
  • A. Kamoun,
  • A. Filiot,
  • O. Tchita,
  • R. Dubois,
  • M. Auffret,
  • L. Guillou,
  • I. Bousaid,
  • M. Azoulay,
  • J. Lemonnier,
  • M. Sefta,
  • S. Everhard,
  • A. Sarrazin,
  • J-F Reboud,
  • F. Brulport,
  • J. Dachary,
  • B. Pistilli,
  • S. Delaloge,
  • P. Courtiol,
  • F. André,
  • V. Aubert,
  • M. Lacroix-Triki

摘要

Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients with estrogen receptor-positive, HER2-negative (ER + /HER2 − ) early breast cancer (EBC). Our deep learning model, RlapsRisk BC, independently predicts MFS and provides significant prognostic value beyond traditional clinico-pathological variables (C-index 0.81 vs 0.76, p < 0.05). Applying a 5% MFS event probability threshold stratifies patients into low- and high-risk groups. After dichotomization, combining RlapsRisk BC with clinico-pathological factors increases cumulative sensitivity (0.69 vs 0.63) and dynamic specificity (0.80 vs 0.76) compared to clinical factors alone. Expert analysis of high-impact regions identified by the model highlights well-established morphological features, supporting its interpretability and biological relevance.