成人型弥漫性神经胶质瘤是中枢神经系统最具侵袭性的恶性原发性肿瘤之一。尽管全身治疗取得了进步，放射肿瘤治疗技术也得到了改进，但这些患者的生存结果仍然很差。快速准确地评估肿瘤对肿瘤治疗的反应至关重要，因为它可以早期发现复发性或难治性神经胶质瘤，从而可以及时干预延长生命的挽救疗法。放射组学是一个发展中的领域，在改善医学图像解释方面具有巨大潜力。本研究旨在应用基于放射组学的预测模型对治疗后前 3 个月内的放疗反应进行分类。从 Burdenko 胶质母细胞瘤进展数据集中选择了 95 名患者。在对比增强 T1 (CE T1W) 和 T2 流体衰减反转恢复 (T2_FLAIR) 磁共振成像 (MRI) 上在轴向平面上描绘肿瘤区域。使用 Python (3.10) 中的 PyRadiomics (3.7.6) 提取手工制作的放射组学 (HCR) 特征，包括一阶和二阶特征。然后，应用随机森林（RF）分类器的递归特征消除来降低特征维数。 RF 和支持向量机 (SVM) 分类器的构建是为了使用所选特征来预测治疗结果。采用留一法交叉验证来调整超参数并评估模型。对于每个分割目标，从 MRI 序列中提取 186 个 HCR 特征。使用 CE T1W 和 T2_FLAIR 组合的顶级放射组学特征，优化的分类器使用 RF 分类器实现了最高平均曲线下面积 (AUC) 0.829 ± 0.075。 CE T1W 的 HCR 特征在所有模型中产生了最差的结果（RF 和 SVM 分类器分别为 0.603 ± 0.024 和 0.615 ± 0.075）。我们开发并评估了基于放射组学的早期肿瘤放疗反应预测模型，表现出优异的性能高 AUC 值的支持。该模型利用多模态 MRI 的放射组学特征，与单模态 MRI 方法相比，显示出卓越的预测性能。这些结果强调了放射组学在该疾病过程的临床决策支持中的潜力。© 2024 美国医学物理学家协会。

Adult-type diffuse gliomas are among the central nervous system's most aggressive malignant primary neoplasms. Despite advancements in systemic therapies and technological improvements in radiation oncology treatment delivery, the survival outcome for these patients remains poor. Fast and accurate assessment of tumor response to oncologic treatments is crucial, as it can enable the early detection of recurrent or refractory gliomas, thereby allowing timely intervention with life-prolonging salvage therapies.Radiomics is a developing field with great potential to improve medical image interpretation. This study aims to apply a radiomics-based predictive model for classifying response to radiotherapy within the first 3 months post-treatment.Ninety-five patients were selected from the Burdenko Glioblastoma Progression Dataset. Tumor regions were delineated in the axial plane on contrast-enhanced T1(CE T1W) and T2 fluid-attenuated inversion recovery (T2_FLAIR) magnetic resonance imaging (MRI). Hand-crafted radiomic (HCR) features, including first- and second-order features, were extracted using PyRadiomics (3.7.6) in Python (3.10). Then, recursive feature elimination with a random forest (RF) classifier was applied for feature dimensionality reduction. RF and support vector machine (SVM) classifiers were built to predict treatment outcomes using the selected features. Leave-one-out cross-validation was employed to tune hyperparameters and evaluate the models.For each segmented target, 186 HCR features were extracted from the MRI sequence. Using the top-ranked radiomic features from a combination of CE T1W and T2_FLAIR, an optimized classifier achieved the highest averaged area under the curve (AUC) of 0.829 ± 0.075 using the RF classifier. The HCR features of CE T1W produced the worst outcomes among all models (0.603 ± 0.024 and 0.615 ± 0.075 for RF and SVM classifiers, respectively).We developed and evaluated a radiomics-based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi-modal MRI, showed superior predictive performance compared to single-modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process.© 2024 American Association of Physicists in Medicine.