Application of machine learning for the differentiation of thymomas and thymic cysts using deep transfer learning: A multi‐center comparison of diagnostic performance based on different dimensional models

Abstract

Abstract Objective This study aimed to evaluate the feasibility and performance of deep transfer learning (DTL) networks with different types and dimensions in differentiating thymomas from thymic cysts in a retrospective cohort. Materials and Methods Based on chest‐enhanced computed tomography (CT), the region of interest was delineated, and the maximum cross section of the lesion was selected as the input image. Five convolutional neural networks (CNNs) and Vision Transformer (ViT) were used to construct a 2D DTL model. The 2D model constructed by the maximum section (n) and the upper and lower layers (n − 1, n + 1) of the lesion was used for feature extraction, and the features were selected. The remaining features were pre‐fused to construct a 2.5D model. The whole lesion image was selected for input and constructing a 3D model. Results In the 2D model, the area under curve (AUC) of Resnet50 was 0.950 in the training cohort and 0.907 in the internal validation cohort. In the 2.5D model, the AUCs of Vgg11 in the internal validation cohort and external validation cohort 1 were 0.937 and 0.965, respectively. The AUCs of Inception_v3 in the training cohort and external validation cohort 2 were 0.981 and 0.950, respectively. The AUC values of 3D_Resnet50 in the four cohorts were 0.987, 0.937, 0.938, and 0.905. Conclusions The DTL model based on multiple different dimensions can be used as a highly sensitive and specific tool for the non‐invasive differential diagnosis of thymomas and thymic cysts to assist clinicians in decision‐making.

Keywords

• automatic segmentation
• deep learning
• thymic cyst
• thymoma
• vision transformer