Gehin William, Lambert Aurélien, Bibault Jean-Emmanuel
Radiation Therapy Department, Institut de Cancérologie de Lorraine, Vandoeuvre-lès-Nancy, France.
Oncology Department, Institut de Cancérologie de Lorraine, Vandoeuvre-lès-Nancy, France.
Comput Biol Med. 2025 Sep;195:110659. doi: 10.1016/j.compbiomed.2025.110659. Epub 2025 Jun 25.
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.
A dual-axis literature search was conducted to identify clinical studies assessing the prognostic role of sarcopenia in BRPC, and technical studies developing AI-based segmentation models for body composition analysis. Structured data extraction was applied to 13 clinical and 71 technical studies. A PRISMA-inspired flow diagram was included to ensure methodological transparency.
Sarcopenia was consistently associated with worse survival and treatment tolerance in BRPC, yet clinical definitions and cut-offs varied widely. AI models-mostly 2D U-Nets trained on L3-level CT slices-achieved high segmentation accuracy (mean DSC >0.93), but external validation and standardization were often lacking.
CT-based AI assessment of sarcopenia holds promise for improving patient stratification in BRPC. However, its clinical adoption will require standardization, integration into decision-support frameworks, and prospective validation across diverse populations.
肌肉减少症被定义为骨骼肌质量和功能的逐渐丧失，与胰腺癌患者，尤其是临界可切除胰腺癌（BRPC）患者的不良预后相关。尽管可以从常规CT成像中提取身体成分，但肌肉减少症评估在临床实践中仍未得到充分利用。人工智能（AI）的最新进展为实现这一过程的自动化和标准化提供了潜力，但其临床转化仍然有限。本叙述性综述旨在批判性地评估：（1）CT定义的肌肉减少症对BRPC的临床影响；（2）基于AI的CT图像肌肉和脂肪分割方法的性能和成熟度。
进行了双轴文献检索，以识别评估肌肉减少症在BRPC中的预后作用的临床研究，以及开发用于身体成分分析的基于AI的分割模型的技术研究。对13项临床研究和71项技术研究应用了结构化数据提取。纳入了一个受PRISMA启发的流程图，以确保方法的透明度。
肌肉减少症与BRPC患者较差的生存率和治疗耐受性始终相关，但临床定义和临界值差异很大。AI模型（大多数是在L3水平CT切片上训练的二维U-Net）实现了较高的分割精度（平均DSC>0.93），但往往缺乏外部验证和标准化。
基于CT的AI肌肉减少症评估有望改善BRPC患者的分层。然而，其临床应用需要标准化，整合到决策支持框架中，并在不同人群中进行前瞻性验证。
