甲基泼尼松龙被推荐为鼻咽癌放疗引起的脑坏死（RN）的一线治疗。然而，部分患者未能从甲基泼尼松龙中受益，甚至恶化。本研究旨在开发和验证一个基于放射组学模型，预测 RN 对甲基泼尼松龙的响应。本研究招募了接受甲基泼尼松龙治疗的 66 位患者。总共从脑部预处理磁共振图像中提取了 961 个放射组学特征。然后运用最小绝对收缩和选择算子回归构建放射组学签名。结合独立临床预测因子，利用多元 logistic 回归分析建立了一个放射组学模型。评估了模型的鉴别性、校准性和临床实用性，使用 10 折交叉验证进行内部验证。放射组学签名由 16 个选定特征组成，具有良好的鉴别性能力。将放射组学签名和放疗与 RN 诊断之间的时间结合，利用 10 折交叉验证得出 AUC 为 0.966 和经过校正的 AUC 为 0.967 的放射组学模型，也显示出良好的鉴别性。校准曲线显示良好的一致性。决策曲线分析证实了该模型的临床实用性。所呈现的放射组学模型可方便地用于促进 RN 患者对甲基泼尼松龙作出个体化的预测。 © 2023 年作者。

Methylprednisolone is recommended as the front-line therapy for radiation-induced brain necrosis (RN) after radiotherapy for nasopharyngeal carcinoma. However, some patients fail to benefit from methylprednisolone or even progress. This study aimed to develop and validate a radiomic model to predict the response to methylprednisolone in RN.Sixty-six patients receiving methylprednisolone were enrolled. In total, 961 radiomic features were extracted from the pre-treatment magnetic resonance imagings of the brain. Least absolute shrinkage and selection operator regression was then applied to construct the radiomics signature. Combined with independent clinical predictors, a radiomics model was built with multivariate logistic regression analysis. Discrimination, calibration and clinical usefulness of the model were assessed. The model was internally validated using 10-fold cross-validation.The radiomics signature consisted of 16 selected features and achieved favorable discrimination performance. The radiomics model incorporating the radiomics signature and the duration between radiotherapy and RN diagnosis, yielded an AUC of 0.966 and an optimism-corrected AUC of 0.967 via 10-fold cross-validation, which also revealed good discrimination. Calibration curves showed good agreement. Decision curve analysis confirmed the clinical utility of the model.The presented radiomics model can be conveniently used to facilitate individualized prediction of the response to methylprednisolone in patients with RN.© 2023. The Author(s).