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<p>The histological FNCLCC grade is the primary prognostic factor in soft-tissue sarcoma (STS) but fails to fully capture high risk patients. This study aimed to develop and validate a deep learning (DL) model to predict metastatic relapse-free survival (MFS) using digital hematoxylin and eosin-stained whole-slide images. A retrospective analysis was conducted on 308 STS patients from two cancer centers, divided into a training cohort (149 patients) and two independent validation cohorts (64 and 95 patients). Supervised multi-instance learning convolutional neural network models were trained on distinct tumor regions—center (C), periphery (P), and margins (R)—to optimize predictive performance. Univariable analysis showed DL models using tumor center (DL-C), periphery (DL-P), and their combination (DL-CP) were consistently associated with MFS across cohorts, while models incorporating margins (DL-R and DL-CPR) demonstrated less reliable associations. Multivariable Cox regression confirmed that high risk scores from DL models were independent predictors of MFS. The DL-CP model outperformed FNCLCC grading in prognostic accuracy, with c-indices ≥ 0.74 in validation cohorts. Adding tumor margin information did not improve predictions.DL models focusing on tumor center and periphery provide superior prognostic value in STS, offering a streamlined, effective approach for digital pathology-based risk stratification.</p>

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Prognostic prediction in soft-tissue sarcomas using deep learning and digital pathology of tumor and margin areas

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月4日

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  • Audrey Michot,
  • Van-Linh Le,
  • Jean-Michel Coindre,
  • Valérie Velasco,
  • Malika Soussi,
  • Nouria Mesli,
  • Antoine Italiano,
  • Maud Toulmonde,
  • Axel Le Cesne,
  • Sylvie Bonvalot,
  • Lucile Vanhersecke,
  • Charles Honoré,
  • Carine Ngo,
  • François Le Loarer,
  • Olivier Saut,
  • Amandine Crombé

摘要

The histological FNCLCC grade is the primary prognostic factor in soft-tissue sarcoma (STS) but fails to fully capture high risk patients. This study aimed to develop and validate a deep learning (DL) model to predict metastatic relapse-free survival (MFS) using digital hematoxylin and eosin-stained whole-slide images. A retrospective analysis was conducted on 308 STS patients from two cancer centers, divided into a training cohort (149 patients) and two independent validation cohorts (64 and 95 patients). Supervised multi-instance learning convolutional neural network models were trained on distinct tumor regions—center (C), periphery (P), and margins (R)—to optimize predictive performance. Univariable analysis showed DL models using tumor center (DL-C), periphery (DL-P), and their combination (DL-CP) were consistently associated with MFS across cohorts, while models incorporating margins (DL-R and DL-CPR) demonstrated less reliable associations. Multivariable Cox regression confirmed that high risk scores from DL models were independent predictors of MFS. The DL-CP model outperformed FNCLCC grading in prognostic accuracy, with c-indices ≥ 0.74 in validation cohorts. Adding tumor margin information did not improve predictions.DL models focusing on tumor center and periphery provide superior prognostic value in STS, offering a streamlined, effective approach for digital pathology-based risk stratification.