@Radiology_AI

To develop an algorithm to classify postcontrast T1-weighted MRI scans by tumor classes (high-grade glioma, low-grade glioma [LGG], brain metastasis, meningioma, pituitary adenoma, and acoustic neuroma) and a healthy tissue (HLTH) class. In this retrospective study, preoperative postcontrast T1-weighted MR scans from four publicly available datasets--the Brain Tumor Image Segmentation dataset (n 378), the LGG-1p19q dataset (n 145), The Cancer Genome Atlas Glioblastoma Multiforme dataset (n 141), and The Cancer Genome Atlas Low Grade Glioma dataset (n 68)--and an internal clinical dataset (n 1373) were used. In all, a total of 2105 images were split into a training dataset (n 1396), an internal test set (n 361), and an external test dataset (n 348). A convolutional neural network was trained to classify the tumor type and to discriminate between images depicting HLTH and images depicting tumors. The performance of the model was evaluated by using cross-validation, internal testing, and external testing.