The grading of glioma has clinical significance in determining a treatment strategy and evaluating prognosis to investigate a novel set of radiomic features extracted from the fractional anisotropy (FA) and mean diffusivity (MD) maps of brain diffusion tensor imaging (DTI) sequences for computer-aided grading of gliomas. This retrospective study included 108 patients who had pathologically confirmed brain gliomas and DTI scanned during 2012-2018. This cohort included 43 low-grade gliomas (LGGs; all grade II) and 65 high-grade gliomas (HGGs; grade III or IV). We extracted a set of radiomic features, including traditional texture, morphological, and novel deep features derived from pre-trained convolutional neural network models, in the manually-delineated tumor regions. We employed support vector machine and these radiomic features for two classification tasks: LGGs vs HGGs, and grade III vs IV. The area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity was reported as the performance metrics using the leave-one-out cross-validation method. When combining FA+MD, AUC = 0.93, accuracy = 0.94, sensitivity = 0.98, and specificity = 0.86 in classifying LGGs from HGGs, while AUC = 0.99, accuracy = 0.98, sensitivity = 0.98, and specificity = 1.00 in classifying grade III from IV. The AUC and accuracy remain close when features were extracted from only the solid tumor or additionally including necrosis, cyst, and peritumoral edema. Still, the effects in terms of sensitivity and specificity are mixed. Deep radiomic features derived from pre-trained convolutional neural networks showed higher prediction ability than the traditional texture and shape features in both classification experiments. Radiomic features extracted on the FA and MD maps of brain DTI images are useful for noninvasively classification/grading of LGGs vs HGGs, and grade III vs IV.

中文翻译：

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用于神经胶质瘤分级分类的扩散张量图像的深度卷积放射组学特征。

胶质瘤的分级在确定治疗策略和评估预后方面具有临床意义，以研究从脑扩散张量成像 (DTI) 序列的分数各向异性 (FA) 和平均扩散率 (MD) 图中提取的一组新的放射组学特征，用于计算机-胶质瘤的辅助分级。这项回顾性研究包括 108 名在 2012-2018 年期间经病理证实为脑胶质瘤和 DTI 扫描的患者。该队列包括 43 例低级别胶质瘤（LGG；均为 II 级）和 65 例高级别胶质瘤（HGG；III 级或 IV 级）。我们在手动描绘的肿瘤区域中提取了一组放射组学特征，包括传统纹理、形态学和源自预训练卷积神经网络模型的新深度特征。我们将支持向量机和这些放射组学特征用于两个分类任务：LGG 与 HGG，以及 III 级与 IV 级。使用留一法交叉验证方法将受试者工作特征 (ROC) 曲线下面积 (AUC)、准确性、灵敏度和特异性报告为性能指标。当结合 FA+MD 时，AUC = 0.93，准确度 = 0.94，敏感性 = 0.98，特异性 = 0.86，用于从 HGGs 中分类 LGGs，而 AUC = 0.99，准确度 = 0.98，敏感性 = 0.98，特异性 = 1.00，用于分类 III 级四、当仅从实体肿瘤或另外包括坏死、囊肿和瘤周水肿中提取特征时，AUC 和准确性仍然接近。尽管如此，敏感性和特异性方面的影响是混合的。来自预训练卷积神经网络的深度放射组学特征在两个分类实验中都显示出比传统纹理和形状特征更高的预测能力。在脑 DTI 图像的 FA 和 MD 地图上提取的放射学特征可用于 LGG 与 HGG 以及 III 级与 IV 级的无创分类/分级。