Deep Learning for Noninvasive Assessment of <scp>H3 K27M</scp> Mutation Status in Diffuse Midline Gliomas Using <scp>MR</scp> Imaging

Background: Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG. Purpose: This study aimed to develop a deep learning (DL) approach to noninvasively predict H3 K27M mutation in DMG using T2-weighted images. Study Type: Retrospective and prospective. Population: For diffuse midline brain gliomas, 341 patients from Center-1 (27 &#177; 19 years, 184 males), 42 patients from Center-2 (33 &#177; 19 years, 27 males) and 35 patients (37 &#177; 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 &#177; 15 years, 80 males). Field Strength/Sequence: 5T and 3T, T2-weighted turbo spin echo imaging. Assessment: Conventional radiological features were independently reviewed by two neuroradiologists. H3 K27M status was determined by histopathological examination. The Dice coefficient was used to evaluate segmentation performance. Classification performance was evaluated using accuracy, sensitivity, specificity, and area under the curve. Statistical Tests: Pearson&#39;s Chi-squared test, Fisher&#39;s exact test, two-sample Student&#39;s t-test and Mann–Whitney U test. A two-sided P value