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自动CT衍生的身体组成预测了在非小细胞肺癌中对新辅助免疫疗法的病理反应

Yilong Huang¹, Zhitao Wei², Guanchao Ye³

¹Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, 650032, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510030, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong, 510030, China.

|January 8, 2026

概括

对CT扫描的人工智能 (AI) 分析显示,身体组成指标可以预测非小细胞肺癌 (NSCLC) 的治疗反应. 高的骨肌体积指数和肌肉和脂肪的变化预测了对新辅助免疫化学疗法 (NICT) 的病理完整反应.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

单独的瘤内在生物标志物不足以预测非小细胞肺癌 (NSCLC) 中新辅助免疫化学疗法 (NICT) 的病理完整反应 (pCR).
以人工智能为基础的CT衍生体质成分分析提供了潜在的补充预测价值.

研究的目的:

评估基于人工智能的CT衍生体质成分指标与在NSCLC患者中NICT后的PCR之间的关联.
评估机器学习模型的性能,包括用于PCR预测的身体组成数据.

主要方法:

对657名用NICT.治疗的NSCLC患者进行了回顾性多中心研究.
治疗前后CT扫描的自动细分,以量化骨肌肉,肌际,内脏和皮下脂肪体积指数 (SAVI).
机器学习模型的开发和验证,将临床变量与身体组成指标 (SMVI,% ΔSMVI,% ΔSAVI) 整合在一起.

主要成果:

高基线骨肌体积指数 (SMVI) 与pCR独立相关 (OR=2.22).
在NICT期间SMVI (OR=1.16) 和SAVI (OR=1.56) 的相对增加与更高的PCR概率有关.
结合身体组成指标的机器学习模型在所有队列中显示,与单独的临床变量相比,pCR歧视显著改善.

结论:

基于AI的CT衍生体构,包括基线SMVI和SMVI和SAVI的动态变化,与NSCLC的pCR独立相关.
将这些可修改的生物标志物纳入预测模型可以提高预测准确度,而不仅仅是临床变量.

关键词:

人工智能的人工智能是人工智能.身体组成身体组成免疫治疗是一种免疫疗法.非小细胞肺癌是非小细胞肺癌.病理完整反应的病理完整反应.

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