A Convolutional Neural Network Model for Distinguishing Hemangioblastomas From Other Cerebellar‐and‐Brainstem Tumors Using Contrast‐Enhanced MRI

Abstract

BackgroundHemangioblastoma (HB) is a highly vascularized tumor most commonly occurring in the posterior cranial fossa, requiring accurate preoperative diagnosis to avoid accidental intraoperative hemorrhage and even death.PurposeTo accurately distinguish HBs from other cerebellar‐and‐brainstem tumors using a convolutional neural network model based on a contrast‐enhanced brain MRI dataset.Study TypeRetrospective.PopulationFour hundred five patients (182 = HBs; 223 = other cerebellar‐and brainstem tumors): 305 cases for model training, and 100 for evaluation.Field Strength/Sequence3 T/contrast‐enhanced T1‐weighted imaging (T1WI + C).AssessmentA CNN‐based 2D classification network was trained by using sliced data along the z‐axis. To improve the performance of the network, we introduced demographic information, various data‐augmentation methods and an auxiliary task to segment tumor region. Then, this method was compared with the evaluations performed by experienced and intermediate‐level neuroradiologists, and the heatmap of deep feature, which indicates the contribution of each pixel to model prediction, was visualized by Grad‐CAM for analyzing the misclassified cases.Statistical TestsThe Pearson chi‐square test and an independent t‐test were used to test for distribution difference in age and sex. And the independent t‐test was exploited to evaluate the performance between experts and our proposed method. P value <0.05 was considered significant.ResultsThe trained network showed a higher accuracy for identifying HBs (accuracy = 0.902 ± 0.031, F1 = 0.891 ± 0.035, AUC = 0.926 ± 0.040) than experienced (accuracy = 0.887 ± 0.013, F1 = 0.868 ± 0.011, AUC = 0.881 ± 0.008) and intermediate‐level (accuracy = 0.827 ± 0.037, F1 = 0.768 ± 0.068, AUC = 0.810 ± 0.047) neuroradiologists. The recall values were 0.910 ± 0.050, 0.659 ± 0.084, and 0.828 ± 0.019 for the trained network, intermediate and experienced neuroradiologists, respectively. Additional ablation experiments verified the utility of the introduced demographic information, data augmentation, and the auxiliary‐segmentation task.Data ConclusionOur proposed method can successfully distinguish HBs from other cerebellar‐and‐brainstem tumors and showed diagnostic efficiency comparable to that of experienced neuroradiologists.Evidence Level3Technical EfficacyStage 2