Background and Objective. Although radiotherapy has become one of the main treatment methods for cancer, there is no noninvasive method to predict the radiotherapeutic response of individual glioblastoma (GBM) patients before surgery. The purpose of this study is to develop and validate a machine learning-based radiomics signature to predict the radiotherapeutic response of GBM patients. Methods. The MRI images, genetic data, and clinical data of 152 patients with GBM were analyzed. 122 patients from the TCIA dataset (training set: ; validation set: ) and 30 patients from local hospitals were used as an independent test dataset. Radiomics features were extracted from multiple regions of multiparameter MRI. Kaplan-Meier survival analysis was used to verify the ability of the imaging signature to predict the response of GBM patients to radiotherapy before an operation. Multivariate Cox regression including radiomics signature and preoperative clinical risk factors was used to further improve the ability to predict the overall survival (OS) of individual GBM patients, which was presented in the form of a nomogram. Results. The radiomics signature was built by eight selected features. The -index of the radiomics signature in the TCIA and independent test cohorts was 0.703 () and 0.757 (), respectively. Multivariate Cox regression analysis confirmed that the radiomics signature (HR: 0.290, ), age (HR: 1.023, ), and KPS (HR: 0.968, ) were independent risk factors for OS in GBM patients before surgery. When the radiomics signature and preoperative clinical risk factors were combined, the radiomics nomogram further improved the performance of OS prediction in individual patients ( and 0.758 in the TCIA and test cohorts, respectively). Conclusion. This study developed a radiomics signature that can predict the response of individual GBM patients to radiotherapy and may be a new supplement for precise GBM radiotherapy.

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基于多参数和多区域放射组学特征的机器学习预测胶质母细胞瘤患者的放射治疗反应

背景和目的。尽管放射治疗已成为癌症的主要治疗方法之一，但尚无无创方法可以预测个体胶质母细胞瘤（GBM）患者术前的放射治疗反应。本研究的目的是开发和验证基于机器学习的放射组学特征，以预测 GBM 患者的放射治疗反应。方法。分析了 152 例 GBM 患者的 MRI 图像、遗传数据和临床数据。来自 TCIA 数据集的 122 名患者（训练集：; 验证集：)和来自当地医院的 30 名患者被用作独立的测试数据集。从多参数 MRI 的多个区域提取放射组学特征。Kaplan-Meier 生存分析用于验证成像特征预测 GBM 患者在手术前对放射治疗的反应的能力。包括放射组学特征和术前临床风险因素在内的多变量 Cox 回归用于进一步提高预测个体 GBM 患者总生存期 (OS) 的能力，以列线图的形式呈现。结果。放射组学特征由八个选定的特征构建。TCIA 和独立测试队列中放射组学特征的-指数为 0.703 ()和 0.757 ()，分别。多变量 Cox 回归分析证实放射组学特征（HR：0.290，),年龄 (HR: 1.023,)和 KPS (HR: 0.968,)是 GBM 患者术前 OS 的独立危险因素。当放射组学特征和术前临床危险因素相结合时，放射组学列线图进一步提高了个体患者 OS 预测的性能。和 0.758 在 TCIA 和测试队列中，分别）。结论。该研究开发了一种放射组学特征，可以预测个体 GBM 患者对放射治疗的反应，并可能成为精确 GBM 放射治疗的新补充。