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

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...

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Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 38534 - 17
Main Authors:	Michot, Audrey, Le, Van-Linh, Coindre, Jean-Michel, Velasco, Valérie, Soussi, Malika, Mesli, Nouria, Italiano, Antoine, Toulmonde, Maud, Le Cesne, Axel, Bonvalot, Sylvie, Vanhersecke, Lucile, Honoré, Charles, Ngo, Carine, Le Loarer, François, Saut, Olivier, Crombé, Amandine
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 04.11.2025 Nature Publishing Group Nature Portfolio
Subjects:	631/114 631/67 639/705/1041 692/4028 692/53 Adult Aged Artificial intelligence Data collection Deep Learning Digital pathology Female Histologic grading Humanities and Social Sciences Humans Liposarcoma Male Margins of Excision Medical prognosis Metastases Metastasis Middle Aged multidisciplinary Neoplasm Grading Neural networks Pathology Patients Prognosis Prognosis prediction Retrospective Studies Risk groups Sarcoma Sarcoma - diagnosis Sarcoma - mortality Sarcoma - pathology Science Science (multidisciplinary) Soft-tissue sarcoma Software Surgery Tumors France Japan Deep learning Digital pathology Histologic grading Prognosis prediction Artificial intelligence Soft-tissue sarcoma
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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