这项研究的目的是将深度学习（DL）方法应用于临床标准CT图像，实现对身体成分（BC）的半自动分析，并探究BC在患有淋巴瘤的儿科、青少年和年轻成人（AYA）患者中的预测价值。这项为期10年的单中心回顾性研究共涉及110名患有淋巴瘤的儿童和AYA患者，手动分割了260份CT影像数据集中脂肪和肌肉组织。通过训练DL模型进行半自动图像分割，评估BC测量指标与3年远期不良反应的关联性。DL指导下的BC测量与人工测量高度一致，每个感兴趣组织的Dice评分（≥ 0.95）和相关性（r> 0.99）均很高。Cox比例危险回归分析表明，基线和首次随访时皮下脂肪组织含量较高以及首次随访时骨骼肌体积较低的患者相对于同龄人更容易出现远期不良反应。深度学习可实现BC图像测量的快速准确定量，并与淋巴瘤儿童及AYA病人的治疗相关不良反应风险相关联。基于图像的BC测量监测可能通过识别高风险患者提高未来个性化医疗的机会。• 深度学习引导的CT图像分析实现了与手动图像分析高度一致的水平。• 在癌症治疗过程中，儿科患者脂肪组织含量较高且肌肉组织含量较低时可能比他们的临床同行更容易出现严重不良反应。• BC深度学习可能通过提供实时监测高风险儿童、青少年和年轻成人的CT图像，为常规CT成像增添价值，并有助于预测癌症治疗的远期不良反应风险。©2023年。作者（以独家许可授予欧洲放射学会）。

The objective of this study was to translate a deep learning (DL) approach for semiautomated analysis of body composition (BC) measures from standard of care CT images to investigate the prognostic value of BC in pediatric, adolescent, and young adult (AYA) patients with lymphoma.This 10-year retrospective, single-site study of 110 pediatric and AYA patients with lymphoma involved manual segmentation of fat and muscle tissue from 260 CT imaging datasets obtained as part of routine imaging at initial staging and first therapeutic follow-up. A DL model was trained to perform semiautomated image segmentation of adipose and muscle tissue. The association between BC measures and the occurrence of 3-year late effects was evaluated using Cox proportional hazards regression analyses.DL-guided measures of BC were in close agreement with those obtained by a human rater, as demonstrated by high Dice scores (≥ 0.95) and correlations (r > 0.99) for each tissue of interest. Cox proportional hazards regression analyses revealed that patients with elevated subcutaneous adipose tissue at baseline and first follow-up, along with patients who possessed lower volumes of skeletal muscle at first follow-up, have increased risk of late effects compared to their peers.DL provides rapid and accurate quantification of image-derived measures of BC that are associated with risk for treatment-related late effects in pediatric and AYA patients with lymphoma. Image-based monitoring of BC measures may enhance future opportunities for personalized medicine for children with lymphoma by identifying patients at the highest risk for late effects of treatment.• Deep learning-guided CT image analysis of body composition measures achieved high agreement level with manual image analysis. • Pediatric patients with more fat and less muscle during the course of cancer treatment were more likely to experience a serious adverse event compared to their clinical counterparts. • Deep learning of body composition may add value to routine CT imaging by offering real-time monitoring of pediatric, adolescent, and young adults at high risk for late effects of cancer treatment.© 2023. The Author(s), under exclusive licence to European Society of Radiology.