To construct an intelligent segmentation and T-stage diagnostic model for esophageal cancer based on the DAEUnet and ConvNeXt networks using transfer learning.

Dicom raw data from 126 patients diagnosed with esophageal cancer between January 2018 and April 2022 were collected, including 100 cases from Department of Thoracic Surgery at the First Affiliated Hospital of Army Medical University and 26 cases from the Department of Thoracic Surgery at Shanxi Cancer Hospital. After data augmentation, a total of 60275 images were obtained. The DAEUnet esophageal cancer intelligent segmentation network was built, and on this basis, 3 classification networks, ConvNeXt, Swin Transformer, and ResNet were constructed for T-stage diagnosis of esophageal cancer.

The Dice similarity coefficient (DSC) for esophageal cancer intelligent segmentation using the DAEUnet network was 0.82, and the DSC value of the esophagus, aorta, normal esophagus, mediastinal lymph nodes, and heart was 72.4%, 87.5%, 79.3%, 60.5% and 96.8%, respectively. Among the 3 T-stage diagnosis models for esophageal cancer, the ConvNeXt model performed the best, with a precision value for T1~T4 stages of 0.65, 0.727, 0.889 and 0.92, respectively, and an AUC value of 0.892, which were superior to the ResNet and Swin Transformer networks.

The proposed DAEUnet and ConvNeXt-based intelligent segmentation and T-stage diagnosis model for esophageal cancer improves T-stage accuracy and treatment efficiency.