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 T W (PCT W), apparent diffusion coefficient (ADC) maps (b values of 0, 500, and 1000 s/mm ).

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 PCT W 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 T 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).

Data Conclusion: DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT W images.

Level Of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1152-1159.