Construction and evaluation of an intelligent diagnostic model based on enhanced CT images and Swin Transformer network for T staging of esophageal cancer

Abstract

Objective To construct an intelligent diagnosis model for T stage of esophageal cancer based on the enhanced CT images and Swin Transformer network. Methods A total of 45 000 preoperative enhanced CT images of 150 patients with postoperative pathologically confirmed esophageal cancer in the Department of Thoracic Surgery of the First Affiliated Hospital of the Army Medical University and Shanxi Cancer Hospital from January 2018 to April 2022 were collected. Through automatic segmentation of the UperNet Swin network and calculation of tumor volume, ResNet50, Swin Transformer, and VIT were used to construct the intelligent diagnostic model for T staging of esophageal cancer. The model performance was evaluated using precision, recall, F1-score, specificity and negative predictive value (NPV) in an in-house dataset of 150 cases, and confusion matrix and ROC curves were depicted. Results Among the three diagnostic models for T staging of esophageal cancer, the Swin Transformer model combined with the features of tumor volume and pathological information had the best effect in staging diagnosis, with precision rates of 1.00, 0.67, 0.83, and 1.00 in T1~T4 stages (AUC=0.861). The precision rate of ResNet50 was 0.13, 0.27, 0.59 and 0.81(AUC=0.611), and that of VIT staging diagnosis model was 0.03, 0.14, 0.56 and 0.75 (AUC=0.542). Conclusion Compared with ResNet50 and VIT networks, Swin Transformer network allow intelligent diagnosis for T Staging of esophageal cancer to be employed with greater precision.

Keywords

• deep learning
• esophageal carcinoma
• enhanced ct
• swin transformer
• t staging of diagnosis