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人工智能驱动的身体成分分析增强了结直肠癌中化疗毒性预测

Yu-Zhen Liu¹, Pei-Fang Su¹, An-Shun Tai¹

¹Department of Statistics, National Cheng Kung University, No. 1, University Road, East District, Tainan 701, Taiwan.

Clinical nutrition ESPEN

|August 13, 2025

概括

身体成分会影响化疗的毒性. 使用人工智能识别的肌肉内脂肪组织增加,预测结直肠癌患者的风险更高,表明需要个性化剂量超出身体表面积.

科学领域:

在瘤学瘤学.
药理学药理学是指药理学的学科.
医疗成像医学成像

背景情况:

基于身体表面积 (BSA) 的化疗剂量是标准的,但在预测毒性方面存在局限性.
身体组成的个体变化,如骨肌肉和脂肪组织,会影响药物代谢和毒性.
了解这些变异对于优化癌症治疗至关重要.

研究的目的:

调查身体成分在基于BSA的剂量和剂量限制毒性 (DLT) 之间的关系中扮演的调解角色.
评估骨肌肉和脂肪组织对结直肠癌患者化疗毒性的预测价值.
探索人工智能驱动的身体组成分析对精确瘤学的实用性.

主要方法:

对483名III期结直肠癌患者的回顾性分析.
人工智能算法用于从CT扫描中量化骨肌肉和脂肪组织.
调解分析,以评估身体成分在化疗相关毒性中的作用.

主要成果:

18.2%的患者经历了剂量限制性毒性 (DLT).
肌肉内脂肪组织 (IMAT) 的增加显著预测了较高的DLT风险 (OR = 1.047,p = 0.038).
IMAT部分调解了BSA和DLT之间的关系,表明其在化疗毒性中的作用.

结论:

基于BSA的剂量不足以解释个人化学疗法耐受性.
人工智能辅助的身体成分分析提供了一个精确的瘤学方法来识别高风险患者.
需要进行前性验证,以将身体成分纳入临床实践,以优化化疗方案.

关键词:

人工智能的人工智能是人工智能.身体组成身体组成身体表面积身体表面积身体表面积化疗剂量化疗剂量化疗剂量结肠直肠癌是一种癌症.剂量限制性毒性剂量限制性毒性

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