Using deep learning to distinguish malignant from benign parotid tumors on plain computed tomography images - Hu, Ziyang; Wang, Baixin; Pan, Xiao; Cao, Dantong; Gao, Antian; Yang, Xudong; Chen, Ying; Lin, Zitong (Frontiers in Oncology, 2022)
Objectives: Evaluating the diagnostic efficiency of deep-learning models to distinguish malignant from benign parotid tumors on plain CT images. Materials and Methods: The CT images of 283 patients with parotid tumors were enrolled and analyzed retrospectively. Of them, 150 were benign and 133 were malignant according to pathology results. A total of 917 regions of interest of parotid tumors were cropped (456 benign and 461 malignant). Three deep-learning networks (ResNet50, VGG16_bn and DenseNet169) were used for diagnosis (approximately 3:1 for training and testing). The diagnostic efficiencies (accuracy, sensitivity, specificity, and area under the curve (AUC)) of three networks were calculated and compared based on the 917 images. To simulate the process of human diagnosis, a voting model was developed at the end of the networks and the 283 tumors were classified as benign or malignant. Meanwhile, 917 tumor images were classified by two radiologist (A and B) and original CT images were classified by radiologist B. The diagnostic efficiencies of the 3 deep-learning network models (after voting) and the two radiologists were calculated. Results: For the 917 CT images, ResNet50 presented high accuracy and sensitivity for diagnosing malignant parotid tumors, the accuracy, sensitivity, specificity and AUC was 90.8%, 91.3%, 90.4%, and 0.96. For the 283 tumors, the accuracy, sensitivity and specificity of ResNet50 (after voting) was 92.3%, 93.5% and 91.2%. Conclusion: ResNet50 presented high sensitivity in distinguishing malignant from benign parotid tumors on plain CT images; this made it a promising auxiliary diagnostic method to screen malignant parotid tumors.