Artificial intelligence-driven body composition analysis enhances chemotherapy toxicity prediction in colorectal cancer

IF 2.6 Q3 NUTRITION & DIETETICS

Clinical nutrition ESPEN Pub Date : 2025-08-11 DOI:10.1016/j.clnesp.2025.08.013

Yu-Zhen Liu , Pei-Fang Su , An-Shun Tai , Meng-Ru Shen , Yi-Shan Tsai

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引用次数: 0

Abstract

Background and aims

Body surface area (BSA)-based chemotherapy dosing remains standard despite its limitations in predicting toxicity. Variations in body composition, particularly skeletal muscle and adipose tissue, influence drug metabolism and toxicity risk. This study aims to investigate the mediating role of body composition in the relationship between BSA-based dosing and dose-limiting toxicities (DLTs) in colorectal cancer patients receiving oxaliplatin-based chemotherapy.

Methods

We retrospectively analyzed 483 stage III colorectal cancer patients treated at National Cheng Kung University Hospital (2013–2021). An artificial intelligence (AI)-driven algorithm quantified skeletal muscle and adipose tissue compartments from lumbar 3 (L3) vertebral-level computed tomography (CT) scans. Mediation analysis evaluated body composition's role in chemotherapy-related toxicities.

Results

Among the cohort, 18.2 % (n = 88) experienced DLTs. While BSA alone was not significantly associated with DLTs (OR = 0.473, p = 0.376), increased intramuscular adipose tissue (IMAT) significantly predicted higher DLT risk (OR = 1.047, p = 0.038), whereas skeletal muscle area was protective. Mediation analysis confirmed that IMAT partially mediated the relationship between BSA and DLTs (indirect effect: 0.05, p = 0.040), highlighting adipose infiltration's role in chemotherapy toxicity.

Conclusion

BSA-based dosing inadequately accounts for interindividual variations in chemotherapy tolerance. AI-assisted body composition analysis provides a precision oncology framework for identifying high-risk patients and optimizing chemotherapy regimens. Prospective validation is warranted to integrate body composition into routine clinical decision-making.

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人工智能驱动的体成分分析增强结直肠癌化疗毒性预测。

背景和目的：基于体表面积（BSA）的化疗剂量仍然是标准的，尽管它在预测毒性方面存在局限性。身体组成的变化，特别是骨骼肌和脂肪组织的变化，影响药物代谢和毒性风险。本研究旨在探讨在接受奥沙利铂化疗的结直肠癌患者中，体成分在bsa给药与剂量限制性毒性（dlt）之间的关系中所起的中介作用。方法：回顾性分析2013-2021年在国立成功大学医院治疗的483例III期结直肠癌患者。一种人工智能（AI）驱动的算法量化了腰椎3 （L3）椎体水平计算机断层扫描（CT）的骨骼肌和脂肪组织区室。中介分析评估了身体成分在化疗相关毒性中的作用。结果：在队列中，18.2% （n = 88）经历了dlt。虽然单独的BSA与DLT没有显著相关性（OR = 0.473, p = 0.376），但增加的肌内脂肪组织（IMAT）显著预测更高的DLT风险（OR = 1.047, p = 0.038），而骨骼肌面积具有保护作用。中介分析证实IMAT部分介导了BSA与dlt之间的关系（间接效应：0.05,p = 0.040），突出了脂肪浸润在化疗毒性中的作用。结论：基于bsa的剂量不能充分解释化疗耐受性的个体差异。人工智能辅助的身体成分分析为识别高危患者和优化化疗方案提供了精确的肿瘤学框架。前瞻性验证有必要将体成分纳入常规临床决策。

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

Clinical nutrition ESPEN NUTRITION & DIETETICS-

CiteScore

4.90

自引率

3.30%

发文量

512

期刊介绍： Clinical Nutrition ESPEN is an electronic-only journal and is an official publication of the European Society for Clinical Nutrition and Metabolism (ESPEN). Nutrition and nutritional care have gained wide clinical and scientific interest during the past decades. The increasing knowledge of metabolic disturbances and nutritional assessment in chronic and acute diseases has stimulated rapid advances in design, development and clinical application of nutritional support. The aims of ESPEN are to encourage the rapid diffusion of knowledge and its application in the field of clinical nutrition and metabolism. Published bimonthly, Clinical Nutrition ESPEN focuses on publishing articles on the relationship between nutrition and disease in the setting of basic science and clinical practice. Clinical Nutrition ESPEN is available to all members of ESPEN and to all subscribers of Clinical Nutrition.