Purpose: In 2021, the WHO central nervous system (CNS) tumor classification criteria added the diagnosis of diffuse astrocytic glioma, IDH wild-type, with molecular features of glioblastoma, WHO grade 4 (DAG-G). DAG-G may exhibit the aggressiveness and malignancy of glioblastoma (GBM) despite the lower histological grade, and thus a precise preoperative diagnosis can help neurosurgeons develop more refined individualized treatment plans. This study aimed to establish a predictive model for the non-invasive identification of DAG-G based on preoperative MRI radiomics. Patients and Methods: Patients with pathologically confirmed glioma in Huashan Hospital, Fudan University, between September 2019 and July 2021 were retrospectively analyzed. Furthermore, two external validation datasets from Wuhan Union Hospital and Xuzhou Cancer Hospital were also utilized to verify the reliability and accuracy of the prediction model. Two regions of interest (ROI) were delineated on the preoperative MRI images of the patients using the semi-automatic tool ITK-SNAP (version 4.0.0), which were named the maximum anomaly region (ROI1) and the tumor region (ROI2), and Pyradiomics 3.0 was applied for feature extraction. Feature selection was performed using a least absolute shrinkage and selection operator (LASSO) filter and a Spearman correlation coefficient. Six classifiers, including Gauss naive Bayes (GNB), K-nearest neighbors (KNN), Random forest (RF), Adaptive boosting (AB), and Support vector machine (SVM) with linear kernel and multilayer perceptron (MLP), were used to build the prediction models, and the prediction performance of the six classifiers was evaluated by fivefold cross-validation. Moreover, the performance of prediction models was evaluated using area under the curve (AUC), precision (PRE), and other metrics. Results: According to the inclusion and exclusion criteria, 172 patients with grade 2–3 astrocytoma were finally included in the study, and a total of 44 patients met the diagnosis of DAG-G. In the prediction task of DAG-G, the average AUC of GNB classifier was 0.74 ± 0.07, that of KNN classifier was 0.89 ± 0.04, that of RF classifier was 0.96 ± 0.03, that of AB classifier was 0.97 ± 0.02, that of SVM classifier was 0.88 ± 0.05, and that of MLP classifier was 0.91 ± 0.03, among which, AB classifier achieved the best prediction performance. In addition, the AB classifier achieved AUCs of 0.91 and 0.89 in two external validation datasets obtained from Wuhan Union Hospital and Xuzhou Cancer Hospital, respectively. Conclusions: The prediction model constructed based on preoperative MRI radiomics established in this study can basically realize the prospective, non-invasive, and accurate diagnosis of DAG-G, which is of great significance to help further optimize treatment plans for such patients, including expanding the extent of surgery and actively administering radiotherapy, targeted therapy, or other treatments after surgery, to fundamentally maximize the prognosis of patients.
目的：2021年世界卫生组织（WHO）中枢神经系统（CNS）肿瘤分类标准新增了“具有胶质母细胞瘤分子特征、IDH野生型的弥漫性星形细胞瘤，WHO 4级”（DAG-G）的诊断。DAG-G尽管组织学级别较低，但可能表现出与胶质母细胞瘤（GBM）相当的侵袭性和恶性程度，因此术前精准诊断有助于神经外科医生制定更精细的个体化治疗方案。本研究旨在基于术前MRI影像组学建立一种用于无创识别DAG-G的预测模型。 患者与方法：回顾性分析2019年9月至2021年7月复旦大学附属华山医院经病理证实的胶质瘤患者。此外，还利用来自武汉协和医院和徐州肿瘤医院的两个外部验证数据集来验证预测模型的可靠性和准确性。使用半自动工具ITK-SNAP（版本4.0.0）在患者术前MRI图像上勾画两个感兴趣区域（ROI），分别命名为最大异常区域（ROI1）和肿瘤区域（ROI2），并应用Pyradiomics 3.0进行特征提取。特征选择采用最小绝对收缩和选择算子（LASSO）滤波器及斯皮尔曼相关系数法。使用六种分类器构建预测模型，包括高斯朴素贝叶斯（GNB）、K近邻（KNN）、随机森林（RF）、自适应提升（AB）、线性核支持向量机（SVM）以及多层感知器（MLP），并通过五折交叉验证评估六种分类器的预测性能。此外，使用曲线下面积（AUC）、精确率（PRE）等指标评估预测模型的性能。 结果：根据纳入和排除标准，最终172例2-3级星形细胞瘤患者纳入研究，其中共44例符合DAG-G诊断。在DAG-G预测任务中，GNB分类器的平均AUC为0.74 ± 0.07，KNN分类器为0.89 ± 0.04，RF分类器为0.96 ± 0.03，AB分类器为0.97 ± 0.02，SVM分类器为0.88 ± 0.05，MLP分类器为0.91 ± 0.03，其中AB分类器取得了最佳的预测性能。此外，AB分类器在来自武汉协和医院和徐州肿瘤医院的两个外部验证数据集中分别取得了0.91和0.89的AUC。 结论：本研究建立的基于术前MRI影像组学的预测模型基本能够实现对DAG-G的前瞻性、无创、精准诊断，这对于帮助进一步优化此类患者的治疗方案，包括扩大手术范围以及术后积极进行放疗、靶向治疗或其他治疗，从而从根本上最大化患者预后具有重要意义。
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