Abstract：
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.
摘要：
成人型弥漫性胶质瘤分为星形细胞瘤，IDH-突变体,少突胶质细胞瘤，IDH突变体和1p/19q缺失和胶质母细胞瘤，IDH-基于IDH突变的野生型，和1p/19q共同删除状态。为了确定这些肿瘤的治疗策略，术前预测IDH突变和1p/19q缺失状态可能是有效的。使用机器学习的计算机辅助诊断（CADx）系统已被视为创新的诊断方法。然而,由于各种专家的支持至关重要，因此很难在每个研究所推广机器学习系统的临床应用。在这项研究中,我们使用MicrosoftAzureMachineLearningStudio（MAMLS）建立了一个易于使用的计算机辅助诊断系统来预测这些状态。我们使用来自癌症基因组图谱（TCGA）队列的258例成人型弥漫性神经胶质瘤病例构建了一个分析模型。使用MRIT2加权图像，整体精度，灵敏度,预测IDH突变和1p/19q共缺失的特异性为86.9%，80.9%,92.0%，和94.7%，94.1%,和95.1％，分别。我们还使用包括202例的独立名古屋队列构建了可靠的分析模型，用于预测IDH突变和1p/19q共缺失。这些分析模型在30分钟内建立。这种易于使用的CADx系统可能有助于CADx在各种机构中的临床应用。
