Leveraging Sarcopenia index by automated CT body composition analysis for pan cancer prognostic stratification.

IF 15.1 1区医学 Q1 HEALTH CARE SCIENCES & SERVICES

NPJ Digital Medicine Pub Date : 2025-10-14 DOI:10.1038/s41746-025-02016-z

Katarzyna Borys,Johannes Haubold,Julius Keyl,Maria A Bali,Riccardo De Angelis,Kévin Brou Boni,Nicolas Coquelet,Judith Kohnke,Giulia Baldini,Lennard Kroll,Sara Schramm,Andreas Stang,Eugen Malamutmann,Jens Kleesiek,Moon Kim,Stefan Kasper,Jens T Siveke,Marcel Wiesweg,Anja Merkel-Jens,Benedikt M Schaarschmidt,Viktor Gruenwald,Sebastian Bauer,Arzu Oezcelik,Servet Bölükbas,Ken Herrmann,Rainer Kimmig,Stephan Lang,Jürgen Treckmann,Martin Stuschke,Boris Hadaschik,Lale Umutlu,Michael Forsting,Dirk Schadendorf,Christoph M Friedrich,Martin Schuler,René Hosch,Felix Nensa

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引用次数: 0

Abstract

This study evaluates the CT-based volumetric sarcopenia index (SI) as a baseline prognostic factor for overall survival (OS) in 10,340 solid tumor patients (40% female). Automated body composition analysis was applied to internal baseline abdomen CTs and to thorax CTs. SI's prognostic value was assessed using multivariable Cox proportional hazards regression, accelerated failure time models, and gradient-boosted machine learning. External validation included 439 patients (40% female). Higher SI was associated with prolonged OS in the internal abdomen (HR 0.56, 95% CI 0.52-0.59; P < 0.001) and thorax cohorts (HR 0.40, 95% CI 0.37-0.43; P < 0.001), as well as in the external validation cohort (HR 0.56, 95% CI 0.41-0.79; P < 0.001). Machine learning models identified SI as the most important factor in survival prediction. Our results demonstrate SI's potential as a fully automated body composition feature for standard oncologic workflows.

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利用自动CT体成分分析的肌少症指数进行泛癌预后分层。

本研究评估了10340例实体瘤患者（40%为女性）基于ct的体积性肌肉减少指数（SI）作为总生存期（OS）的基线预后因素。自动身体成分分析应用于内部基线腹部ct和胸部ct。使用多变量Cox比例风险回归、加速故障时间模型和梯度增强机器学习来评估SI的预后价值。外部验证纳入439例患者（40%为女性）。高SI与腹部（HR 0.56, 95% CI 0.52-0.59, P < 0.001）和胸腔队列（HR 0.40, 95% CI 0.37-0.43, P < 0.001）以及外部验证队列（HR 0.56, 95% CI 0.41-0.79, P < 0.001）的OS延长相关。机器学习模型将SI确定为生存预测中最重要的因素。我们的研究结果证明了SI作为标准肿瘤工作流程的全自动身体成分功能的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

NPJ Digital Medicine Multiple-

CiteScore

25.10

自引率

3.30%

发文量

170

审稿时长

15 weeks

期刊介绍： npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.