Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR 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 +/- 19 years, 184 males), 42 patients from Center-2 (33 +/- 19 years, 27 males) and 35 patients (37 +/- 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 +/- 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's Chi-squared test, Fisher's exact test, two-sample Student's t-test and Mann-Whitney U test. A two-sided P value < 0.05 was considered statistically significant.Results: In the testing cohort, Dice coefficients of tumor segmentation using DL were 0.87 for diffuse midline brain and 0.81 for spinal cord gliomas. In the internal prospective testing dataset, the predictive accuracies, sensitivities, and specific-ities of H3 K27M mutation status were 92.1%, 98.2%, 82.9% in diffuse midline brain gliomas and 85.4%, 88.9%, 82.6% in spinal cord gliomas. Furthermore, this study showed that the performance generalizes to external institutions, with predictive accuracies of 85.7%-90.5%, sensitivities of 90.9%-96.0%, and specificities of 82.4%-83.3%.Data Conclusion: In this study, an automatic DL framework was developed and validated for accurately predicting H3 K27M mutation using T2-weighted images, which could contribute to the noninvasive determination of H3 K27M status for clinical decision-making.

Automated summary

A deep learning approach was developed to predict H3 K27M mutation in diffuse midline glioma using T2-weighted images. The segmentation performance and predictive accuracy of H3 K27M mutation status in both midline brain gliomas and spinal cord gliomas were evaluated. The method showed good predictive performance across different institutions.