The comparison of deep learning and radiomics in the prediction of polymyositis.
Authors
Affiliations (3)
Affiliations (3)
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College Affiliated to Huazhong University of Science and Technology, Wuhan, China.
- Department of Radiology, Hubei No. 3 People's Hospital of Jianghan University, Wuhan, China.
Abstract
T2 weighted magnetic resonance imaging has become a commonly used noninvasive examination method for the diagnosis of Polymyositis (PM). The data regarding the comparison of deep learning and radiomics in the diagnosis of PM is still lacking. This study investigates the feasibility of 3D convolutional neural network (CNN) in the prediction of PM, with comparison to radiomics. A total of 120 patients (with 60 PM) were from center A, and 30 (with 15 PM) were from B, and 46 (with 23 PM) were from C. The data from center A was used as training data, and data from B as validation data, and data from C as external test data. The magnetic resonance radiomics features of rectus femoris were obtained for all cases. The maximum correlation minimum redundancy and least absolute shrinkage and selection operator regression were used before establishing a radiomics score model. A 3D CNN classification model was trained with "monai" based on 150 data with labels. A 3D Unet segmentation model was also trained with "monai" based on 196 original data and their segmentation of rectus femoris. The accuracy on the external test data was compared between 2 methods by using the paired chi-square test. PM and non-PM cases did not differ in age or gender (P > .05). The 3D CNN classification model achieved accuracy of 97% in validation data. The sensitivity, specificity, accuracy and positive predictive value of the 3D CNN classification model in the external test data were 96% (22/23), 91% (21/23), 93% (43/46), and 92% (22/24), respectively. The radiomics score achieved accuracy of 90% in the validation data. The sensitivity, specificity, accuracy, and positive predictive value of the radiomics score in the external test data were 70% (16/23), 65% (15/23), 67% (31/46), and 67% (16/24), respectively, significantly lower than that of CNN model (P = .035). The 3D segmentation model for rectus femoris on T2 weighted magnetic resonance images was obtained with dice similarity coefficient of 0.71. 3D CNN model is not inferior to radiomics score in the prediction of PM. The combination of deep learning and radiomics is recommended for the evaluation of PM in future clinical practice.