Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study.

<bold>Background: </bold>Grading of meningiomas is important in the choice of the most effective treatment for each patient.<bold>Purpose: </bold>To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of the histopathological grading of meningiomas from MR images.<bold>Study Type: </bold>Retrospective.<bold>Population: </bold>In all, 117 meningioma-affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II, and 6 WHO Grade III.<bold>Field Strength/sequence: </bold>1.5 T, 3.0 T postcontrast enhanced T1 W (PCT1 W), apparent diffusion coefficient (ADC) maps (b values of 0, 500, and 1000 s/mm2 ).<bold>Assessment: </bold>WHO Grade II and WHO Grade III meningiomas were considered a single category. The diagnostic accuracy of the pretrained Inception-V3 and AlexNet DCNNs was tested on ADC maps and PCT1 W images separately. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to asses DCNN performance.<bold>Statistical Test: </bold>Leave-one-out cross-validation.<bold>Results: </bold>The application of the Inception-V3 DCNN on ADC maps provided the best diagnostic accuracy results, with an AUC of 0.94 (95% confidence interval [CI], 0.88-0.98). Remarkably, only 1/38 WHO Grade II-III and 7/79 WHO Grade I lesions were misclassified by this model. The application of AlexNet on ADC maps had a low discriminating accuracy, with an AUC of 0.68 (95% CI, 0.59-0.76) and a high misclassification rate on both WHO Grade I and WHO Grade II-III cases. The discriminating accuracy of both DCNNs on postcontrast T1 W images was low, with Inception-V3 displaying an AUC of 0.68 (95% CI, 0.59-0.76) and AlexNet displaying an AUC of 0.55 (95% CI, 0.45-0.64).<bold>Data Conclusion: </bold>DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT1 W images.<bold>Level Of Evidence: </bold>2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1152-1159. [ABSTRACT FROM AUTHOR]