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Abstract：
OBJECTIVE: 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.
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.
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.
摘要：
目的：2021年，WHO中枢神经系统（CNS）肿瘤分类标准增加了弥漫性星形胶质细胞瘤的诊断，IDH野生型,具有胶质母细胞瘤的分子特征，世卫组织4级(DAG-G)。尽管组织学分级较低，但DAG-G可能表现出胶质母细胞瘤（GBM）的侵袭性和恶性。因此，精确的术前诊断可以帮助神经外科医生制定更精细的个性化治疗计划。本研究旨在建立基于术前MRI影像组学的DAG-G无创鉴别预测模型。
方法：华山医院经病理证实的脑胶质瘤患者，复旦大学,对2019年9月至2021年7月间的数据进行回顾性分析。此外，利用武汉协和医院和徐州市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（4.0.0版）在患者的术前MRI图像上描绘了两个感兴趣区域（ROI），分别命名为最大异常区域(ROI1)和肿瘤区域(ROI2)，应用Pyradiomics3.0进行特征提取。使用最小绝对收缩和选择算子（LASSO）滤波器和Spearman相关系数进行特征选择。六个分类器，包括高斯朴素贝叶斯(GNB)，K-最近邻(KNN),随机森林(RF)，自适应提升(AB)，以及具有线性核和多层感知器(MLP)的支持向量机(SVM)，用于构建预测模型，并通过五次交叉验证评估了六个分类器的预测性能。此外，使用曲线下面积(AUC)评估预测模型的性能，精度（PRE），和其他指标。
结果：根据纳入和排除标准，172例2-3级星形细胞瘤患者最终纳入研究，共有44例患者符合DAG-G的诊断。在DAG-G的预测任务中，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分类器取得了最好的预测性能。此外,AB分类器在武汉协和医院和徐州肿瘤医院获得的两个外部验证数据集中获得的AUC分别为0.91和0.89，分别。
结论：本研究建立的基于术前MRI影像组学的预测模型基本可以实现前瞻性,非侵入性，和DAG-G的准确诊断，这对进一步优化此类患者的治疗计划具有重要意义，包括扩大手术范围和积极进行放射治疗，靶向治疗,或手术后的其他治疗，从根本上最大限度地提高患者的预后。
方法：华山医院经病理证实的脑胶质瘤患者，复旦大学,对2019年9月至2021年7月间的数据进行回顾性分析。此外，利用武汉协和医院和徐州市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（4.0.0版）在患者的术前MRI图像上描绘了两个感兴趣区域（ROI），分别命名为最大异常区域(ROI1)和肿瘤区域(ROI2)，应用Pyradiomics3.0进行特征提取。使用最小绝对收缩和选择算子（LASSO）滤波器和Spearman相关系数进行特征选择。六个分类器，包括高斯朴素贝叶斯(GNB)，K-最近邻(KNN),随机森林(RF)，自适应提升(AB)，以及具有线性核和多层感知器(MLP)的支持向量机(SVM)，用于构建预测模型，并通过五次交叉验证评估了六个分类器的预测性能。此外，使用曲线下面积(AUC)评估预测模型的性能，精度（PRE），和其他指标。
结果：根据纳入和排除标准，172例2-3级星形细胞瘤患者最终纳入研究，共有44例患者符合DAG-G的诊断。在DAG-G的预测任务中，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分类器取得了最好的预测性能。此外,AB分类器在武汉协和医院和徐州肿瘤医院获得的两个外部验证数据集中获得的AUC分别为0.91和0.89，分别。
结论：本研究建立的基于术前MRI影像组学的预测模型基本可以实现前瞻性,非侵入性，和DAG-G的准确诊断，这对进一步优化此类患者的治疗计划具有重要意义，包括扩大手术范围和积极进行放射治疗，靶向治疗,或手术后的其他治疗，从根本上最大限度地提高患者的预后。
