AI-based CT assessment of sarcopenia in borderline resectable pancreatic Cancer: A narrative review of clinical and technical perspectives.

Journal: Computers in biology and medicine
Published Date: Jun 25, 2025

Abstract

BACKGROUND: Sarcopenia, defined as the progressive loss of skeletal muscle mass and function, has been associated with poor prognosis in patients with pancreatic cancer, particularly those with borderline resectable pancreatic cancer (BRPC). Although body composition can be extracted from routine CT imaging, sarcopenia assessment remains underused in clinical practice. Recent advances in artificial intelligence (AI) offer the potential to automate and standardize this process, but their clinical translation remains limited. This narrative review aims to critically evaluate (1) the clinical impact of CT-defined sarcopenia in BRPC, and (2) the performance and maturity of AI-based methods for automated muscle and fat segmentation on CT images.

Authors

  • William Gehin
    Academic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine - Alexis-Vautrin CLCC - Unicancer, 6 avenue de Bourgogne - CS 30 519, 54 511, Vandoeuvre-Lès-Nancy Cedex, France.
  • Aurélien Lambert
    Service de radiologie, IM2P, Dijon, France. Electronic address: aurelien.lambert@im2p.fr.
  • Jean-Emmanuel Bibault
    Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France; INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France. Electronic address: jean-emmanuel.bibault@aphp.fr.

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