为了评估基于18F-FDG PET/CT的放射组学评分法对儿童神经母细胞瘤骨髓受累的预测能力，我们对两家医疗中心共241名行18F-FDG PET/CT检查的神经母细胞瘤患者进行了回顾性评估。其中，来自A中心的数据（n = 200）被随机分为训练组（n = 140）和内部验证组（n = 60），而来自B中心的数据（n = 41）构成了外部验证组。对每位患者，我们使用肿瘤和骨骼轴定义了两个感兴趣区域。通过提取临床因素和放射组学特征建立了临床和放射组学模型。将临床因素和放射组学特征结合起来构建了放射组学评分法图谱。使用受试者工作特征曲线下面积（AUC）来评估模型的性能。在训练组中，基于肿瘤和骨骼轴的放射组学模型的AUC分别为0.773和0.900，而临床模型的AUC为0.858。通过结合临床危险因素和基于骨骼轴的放射组学特征，放射组学评分法图谱在训练组、内部验证组和外部验证组的AUC分别为0.932、0.887和0.733。基于骨骼轴的放射组学模型在预测骨髓受累中表现优于基于肿瘤的放射组学模型。此外，放射组学评分法图谱显示将基于骨骼轴的放射组学特征与临床危险因素结合能够改善它们的性能。版权所有©2023年大学放射学协会。Elsevier Inc.发行，版权所有。

To assess the predictive ability of an 18F-FDG PET/CT-based radiomics nomogram for bone marrow involvement in pediatric neuroblastoma.A total of 241 neuroblastoma patients who underwent 18F-FDG PET/CT at two medical centers were retrospectively evaluated. Data from center A (n = 200) were randomized into a training cohort (n = 140) and an internal validation cohort (n = 60), while data from center B (n = 41) constituted the external validation cohort. For each patient, two regions of interest were defined using the tumor and axial skeleton. The clinical factors and radiomics features were derived to construct the clinical and radiomics models. The radiomics nomogram was built by combining clinical factors and radiomics features. The area under the receiver operating characteristic curves (AUCs) were used to assess the performance of the models.Radiomics models created from tumor and axial skeleton achieved AUCs of 0.773 and 0.900, and the clinical model had an AUC of 0.858 in the training cohort. By incorporating clinical risk factors and axial skeleton-based radiomics features, the AUC of the radiomics nomogram in the training cohort, internal validation cohort, and external validation cohort was 0.932, 0.887, and 0.733, respectively.The axial skeleton-based radiomics model performed better than the tumor-based radiomics model in predicting bone marrow involvement. Moreover, the radiomics nomogram showed that combining axial skeleton-based radiomics features with clinical risk factors improved their performance.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.