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

Background: Grading of meningiomas is important in the choice of the most effective treatment for each patient. Purpose: To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of the histopathological grading of meningiomas from MR images. Study Type: Retrospective. Population: In all, 117 meningioma-affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II, and 6 WHO Grade III. Field Strength/Sequence: 1.5 T, 3.0 T postcontrast enhanced T1 W (PCT1W), apparent diffusion coefficient (ADC) maps (b values of 0, 500, and 1000 s/mm2). Assessment: 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 PCT1W images separately. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to asses DCNN performance. Statistical Test: Leave-one-out cross-validation. Results: 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 T1W 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). Data Conclusion: DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT1W images. Level of evidence: 2 Technical Efficacy: Stage 2