Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, NO.107 Yan Jiang Xi Road, Guangzhou, Guangdong Province, People's Republic of China.
Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, People's Republic of China.
Radiat Oncol. 2023 Mar 1;18(1):43. doi: 10.1186/s13014-023-02235-2.
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.
甲泼尼龙被推荐为鼻咽癌放疗后放射性脑坏死（RN）的一线治疗药物。然而，一些患者对甲泼尼龙治疗无效，甚至病情进展。本研究旨在开发和验证一种放射组学模型，以预测 RN 对甲泼尼龙的反应。
纳入 66 名接受甲泼尼龙治疗的患者。从脑磁共振成像的预处理图像中提取了 961 个放射组学特征。然后应用最小绝对收缩和选择算子回归来构建放射组学特征。结合独立的临床预测因子，采用多变量逻辑回归分析构建放射组学模型。评估模型的判别能力、校准能力和临床实用性。采用 10 折交叉验证对模型进行内部验证。
放射组学特征由 16 个选定的特征组成，具有良好的判别性能。将放射组学特征与放射治疗与 RN 诊断之间的时间间隔相结合的放射组学模型，通过 10 折交叉验证获得了 0.966 的 AUC 和 0.967 的校正 AUC，具有良好的判别能力。校准曲线显示出良好的一致性。决策曲线分析证实了该模型的临床实用性。
所提出的放射组学模型可方便地用于预测 RN 患者对甲泼尼龙治疗的反应。
