弥漫性内在脑桥胶质瘤（DIPG）是一种致命的儿童中枢神经系统肿瘤。肿瘤对治疗反应的诊断和监测基于磁共振成像（MRI）。基于 MRI 的肿瘤体积和外观分析可能有助于预测患者总生存期 (OS)。从影像学诊断的经典 DIPG 儿童中回顾性收集对比增强 T1 和 FLAIR/T2 加权 MR 图像 (n = 43)患者）。在诊断时（n = 43 名患者）和放射后（n = 40 名患者）评估 MRI 特征，以确定 OS 结果预测因素。特征包括 3D 肿瘤体积 (Twv)、对比增强肿瘤核心体积 (Tc)、Tc 相对于 Twv (TC/Twv) 以及 Twv 相对于全脑体积。支持向量机 (SVM) 学习用于识别预测 OS 结果的特征组合（定义为诊断后 OS 短于或长于 12 个月）。与不良 OS 结果相关的特征包括诊断时存在对比增强肿瘤，>15放射治疗 (RT) 后 % Tc/Twv，以及 RT 后 >20% ΔTc/Twv。一致地，SVM 学习将诊断时的 Tc/Twv（预测准确度为 74%）和 RT 后 2 个月内的 ΔTc/Twv（准确度 = 75%）确定为生存不良的主要特征。这项研究表明，肿瘤成像特征在诊断和 RT 4 个月内可以预测 DIPG 的不同 OS 结果。这些发现提供了一个框架，可将基于肿瘤体积的预测分析纳入临床环境，并具有根据肿瘤风险特征和基于机器学习的分析的未来应用进行定制治疗的潜力。© 作者 2024。出版者牛津大学出版社、神经肿瘤学会和欧洲神经肿瘤学会。

Diffuse intrinsic pontine glioma (DIPG) is a fatal childhood central nervous system tumor. Diagnosis and monitoring of tumor response to therapy is based on magnetic resonance imaging (MRI). MRI-based analyses of tumor volume and appearance may aid in the prediction of patient overall survival (OS).Contrast-enhanced T1- and FLAIR/T2-weighted MR images were retrospectively collected from children with classical DIPG diagnosed by imaging (n = 43 patients). MRI features were evaluated at diagnosis (n = 43 patients) and post-radiation (n = 40 patients) to determine OS outcome predictors. Features included 3D tumor volume (Twv), contrast-enhancing tumor core volume (Tc), Tc relative to Twv (TC/Twv), and Twv relative to whole brain volume. Support vector machine (SVM) learning was used to identify feature combinations that predicted OS outcome (defined as OS shorter or longer than 12 months from diagnosis).Features associated with poor OS outcome included the presence of contrast-enhancing tumor at diagnosis, >15% Tc/Twv post-radiation therapy (RT), and >20% ∆Tc/Twv post-RT. Consistently, SVM learning identified Tc/Twv at diagnosis (prediction accuracy of 74%) and ∆Tc/Twv at <2 months post-RT (accuracy = 75%) as primary features of poor survival.This study demonstrates that tumor imaging features at diagnosis and within 4 months of RT can predict differential OS outcomes in DIPG. These findings provide a framework for incorporating tumor volume-based predictive analyses into the clinical setting, with the potential for treatment customization based on tumor risk characteristics and future applications of machine-learning-based analysis.© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.