H3K27M-mutant associated brainstem glioma (BSG) carries a very poor prognosis. We aimed to predict H3K27M mutation status by amide proton transfer weighted (APTw) imaging and radiomic features. MethodsEighty-one BSG patients with APTw imaging at 3T MRI and known H3K27M status were retrospectively studied. APTw values (mean, median and max) and radiomic features within manually delineated 3D tumor masks were extracted. Comparison of APTw measures between H3K27M-mutant and wildtype groups was conducted by two-sample Student's T/Mann-Whitney U test and receiver operating characteristic curve (ROC) analysis. H3K27M-mutant prediction using APTw-derived radiomics was conducted using a machine-learning algorithm in randomly selected train (n=64) and test (n=17) sets. Sensitivity analysis with additional random splits of train and test sets, 2D tumor masks and other classifiers were conducted. Finally, a prospective cohort including 29 BSG patients was acquired for validation of the radiomics algorithm. ResultsBSG patients with H3K27M-mutant were younger and had higher max APTw values than those with wildtype. APTw-derived radiomic measures reflecting tumor heterogeneity could predict H3K27M mutation status with an accuracy of 0.88, sensitivity of 0.92 and specificity of 0.80 in the test set.Sensitivity analysis confirmed the predictive ability (accuracy range: 0.71-0.94). In the independent prospective validation cohort, the algorithm reached an accuracy of 0.86, sensitivity of 0.88 and specificity of 0.85 for predicting H3K27M-mutation status. ConclusionBSG patients with H3K27M-mutant had higher max APTw values than those with wildtype. APTwderived radiomics could accurately predict a H3K27M-mutant status in BSG patients.

Dice scores of 0.77, 0.80, 0.50 and 0.58 were obtained based on the segmentation of spinal cord lesions for astrocytoma, ependymoma, multiple sclerosis and neuromyelitis optica spectrum disorders (NMOSD), respectively, against manual labels.Accuracies of 96%, 82% and 79% were obtained for the classifications of tumor vs. demyelinating lesion, astrocytoma vs. ependymoma, and multiple sclerosis vs. NMOSD, respectively.In radiologically difficult cases, an accuracy of 79-95% was still achieved by the classifier.

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Purpose H3K27M-mutant associated brainstem glioma (BSG) carries a very poor prognosis. We aimed to predict H3K27M mutation status by amide proton transfer weighted (APTw) imaging and radiomic features. Methods Eighty-one BSG patients with APTw imaging at 3T MRI and known H3K27M status were retrospectively studied. APTw values (mean, median and max) and radiomic features within manually delineated 3D tumor masks were extracted. Comparison of APTw measures between H3K27M-mutant and wildtype groups was conducted by two-sample Student’s T/Mann-Whitney U test and receiver operating characteristic curve (ROC) analysis. H3K27M-mutant prediction using APTw-derived radiomics was conducted using a machine-learning algorithm in randomly selected train (n=64) and test (n=17) sets. Sensitivity analysis with additional random splits of train and test sets, 2D tumor masks and other classifiers were conducted. Finally, a prospective cohort including 29 BSG patients was acquired for validation of the radiomics algorithm. Results BSG patients with H3K27M-mutant were younger and had higher max APTw values than those with wildtype. APTw-derived radiomic measures reflecting tumor heterogeneity could predict H3K27M mutation status with an accuracy of 0.88, sensitivity of 0.92 and specificity of 0.80 in the test set. Sensitivity analysis confirmed the predictive ability (accuracy range: 0.71-0.94). In the independent prospective validation cohort, the algorithm reached an accuracy of 0.86, sensitivity of 0.88 and specificity of 0.85 for predicting H3K27M-mutation status. Conclusion BSG patients with H3K27M-mutant had higher max APTw values than those with wildtype. APTw-derived radiomics could accurately predict a H3K27M-mutant status in BSG patients.