Easy-to-use machine learning system for the prediction of IDH mutation and 1p/19q codeletion using MRI images of adult-type diffuse gliomas

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.
Source: Brain Tumor Pathology - Category: Neurology Source Type: research