Using deep learning to distinguish malignant from benign parotid tumors on plain computed tomography images

Ziyang Hu; Baixin Wang; Xiao Pan; Dantong Cao; Antian Gao; Xudong Yang; Ying Chen; Zitong Lin

doi:10.3389/fonc.2022.919088

Using deep learning to distinguish malignant from benign parotid tumors on plain computed tomography images

Front Oncol. 2022 Aug 1:12:919088. doi: 10.3389/fonc.2022.919088. eCollection 2022.

Authors

Ziyang Hu¹, Baixin Wang², Xiao Pan¹, Dantong Cao¹, Antian Gao¹, Xudong Yang³, Ying Chen², Zitong Lin¹

Affiliations

¹ Department of Dentomaxillofacial Radiology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China.
² School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
³ Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China.

Abstract

Objectives: Evaluating the diagnostic efficiency of deep-learning models to distinguish malignant from benign parotid tumors on plain computed tomography (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 radiologists (A and B) and original CT images were classified by radiologist B. The diagnostic efficiencies of the three 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 were 90.8%, 91.3%, 90.4%, and 0.96, respectively. For the 283 tumors, the accuracy, sensitivity, and specificity of ResNet50 (after voting) were 92.3%, 93.5% and 91.2%, respectively.

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.

Keywords: computed tomography; convolutional neural network; deep learning; parotid tumor; residual neural network.