胶质母细胞瘤（GBM）的肿瘤周围区域由渗透性肿瘤细胞和血管源性水肿组成，在MRI上手动区分非常困难。为了区分GBM肿瘤周围区域中的肿瘤细胞浸润和血管源性水肿，开发一个精准、有效和广泛适用的方法至关重要。我们从28例非增强性脑胶质瘤中提取了379,730个体积元（肿瘤浸润标记物）的图像特征，以及从14例脑膜瘤的肿瘤周围水肿区域提取了365,262个体积元（水肿标记物）的图像特征，这些数据来自常规MRI序列（T1加权图像、增强T1加权图像、T2加权图像、T2流体抑制倒转恢复图像和表观扩散系数图）。利用支持向量机分类器，发展了一个用于预测体素级别的肿瘤细胞浸润和血管源性水肿的模型。然后，使用进行立体定位活检的15例GBM患者对该模型的预测准确性进行评估。训练集的曲线下面积（AUC）、准确性、敏感性和特异性分别为0.93、0.84、0.83和0.85，在测试集（704,992个体积元）中为0.90、0.82、0.83和0.83。对28个活检点的病理验证的准确性为0.79。在体素级别上，似乎可以根据常规MRI序列预测GBM肿瘤周围区域的肿瘤细胞浸润和血管源性水肿。 © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

The peritumoral region of glioblastoma (GBM) is composed of infiltrating tumor cells and vasogenic edema, which are difficult to distinguish manually on MRI. To distinguish tumor cell infiltration and vasogenic edema in GBM peritumoral regions, it is crucial to develop a method that is precise, effective, and widely applicable.We retrieved the image characteristics of 379,730 voxels (marker of tumor infiltration) from 28 non-enhanced gliomas and 365,262 voxels (marker of edema) from the peritumoral edema region of 14 meningiomas on conventional MRI sequences (T1-weighted image, the contrast-enhancing T1-weighted image, the T2-weighted image, the T2-fluid attenuated inversion recovery image, and the apparent diffusion coefficient map). Using the SVM classifier, a model for predicting tumor cell infiltration and vasogenic edema at the voxel level was developed. The accuracy of the model's predictions was then evaluated using 15 GBM patients who underwent stereotactic biopsies.The area under the curve (AUC), accuracy, sensitivity, and specificity of the prediction model were 0.93, 0.84, 0.83, and 0.85 in the training set, and 0.90, 0.82, 0.83, and 0.83 in the test set (704,992 voxels), respectively. The pathology verification of 28 biopsy points with an accuracy of 0.79.At the voxel level, it seems possible to forecast tumor cell infiltration and vasogenic edema in the peritumoral region of GBM based on conventional MRI sequences.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.