成年型散发性胶质瘤根据IDH突变和1p/19q缺失状态分为星形细胞瘤，IDH突变，少突胶质母细胞瘤，IDH突变和1p/19q缺失以及胶质母细胞瘤，IDH野生型。为确定这些肿瘤的治疗策略，术前预测IDH突变和1p/19q缺失状态可能会有益。机器学习支持的计算机辅助诊断（CADx）系统被认为是创新的诊断方法。然而，在各个医院推广机器学习系统的临床应用是困难的，因为需要各种专家的支持。在本研究中，我们使用微软Azure Machine Learning Studio（MAMLS）建立了一个易于使用的CADx系统，以预测这些状态。我们使用来自癌症基因组图谱（TCGA）队列的258例成年型散发性胶质瘤病例构建了分析模型。在使用MRI T2加权图像进行预测IDH突变和1p/19q缺失的过程中，准确性、灵敏度和特异度分别为86.9％、80.9％和92.0％，以及94.7％、94.1％和95.1％。我们还使用202例独立的名古屋队列构建了一个可靠的分析模型，以预测IDH突变和1p/19q缺失。这些分析模型在30分钟内建立。这个易于使用的CADx系统可能对各种医院的CADx临床应用有用。© 2023年作者，独家授权给日本脑肿瘤病理学会。

Adult-type diffuse gliomas are divided into Astrocytoma, IDH-mutant, Oligodendroglioma, IDH-mutant and 1p/19q-codeleted and Glioblastoma, IDH-wildtype based on the IDH mutation, and 1p/19q codeletion status. To determine the treatment strategy for these tumors, pre-operative prediction of IDH mutation and 1p/19q codeletion status might be effective. Computer-aided diagnosis (CADx) systems using machine learning have been noted as innovative diagnostic methods. However, it is difficult to promote the clinical application of machine learning systems at each institute because the support of various specialists is essential. In this study, we established an easy-to-use computer-aided diagnosis system using Microsoft Azure Machine Learning Studio (MAMLS) to predict these statuses. We constructed an analysis model using 258 adult-type diffuse glioma cases from The Cancer Genome Atlas (TCGA) cohort. Using MRI T2-weighted images, the overall accuracy, sensitivity, and specificity for the prediction of IDH mutation and 1p/19q codeletion were 86.9%, 80.9%, and 92.0%, and 94.7%, 94.1%, and 95.1%, respectively. We also constructed an reliable analysis model for the prediction of IDH mutation and 1p/19q codeletion using an independent Nagoya cohort including 202 cases. These analysis models were established within 30 min. This easy-to-use CADx system might be useful for the clinical application of CADx in various institutes.© 2023. The Author(s), under exclusive licence to The Japan Society of Brain Tumor Pathology.