A vision transformer‐based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma

Abstract Background Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; however, the ability of chest computed tomography (CT) imaging to detect LNM status is limited. Purpose This study aimed to develop and validate a vision transformer‐based deep transfer learning nomogram for predicting LNM in lung adenocarcinoma patients using preoperative unenhanced chest CT imaging. Methods This study included 528 patients with lung adenocarcinoma who were randomly divided into training and validation cohorts at a 7:3 ratio. The pretrained vision transformer (ViT) was utilized to extract deep transfer learning (DTL) feature, and logistic regression was employed to construct a ViT‐based DTL model. Subsequently, the model was compared with six classical convolutional neural network (CNN) models. Finally, the ViT‐based DTL signature was combined with independent clinical predictors to construct a ViT‐based deep transfer learning nomogram (DTLN). Results The ViT‐based DTL model showed good performance, with an area under the curve (AUC) of 0.821 (95% CI, 0.775–0.867) in the training cohort and 0.825 (95% CI, 0.758–0.891) in the validation cohort. The ViT‐based DTL model demonstrated comparable performance to classical CNN models in predicting LNM, and the ViT‐based DTL signature was then used to construct ViT‐based DTLN with independent clinical predictors such as tumor maximum diameter, location, and density. The DTLN achieved the best predictive performance, with AUCs of 0.865 (95% CI, 0.827–0.903) and 0.894 (95% CI, 0845–0942), respectively, surpassing both the clinical factor model and the ViT‐based DTL model ( p < 0.001). Conclusion This study developed a new DTL model based on ViT to predict LNM status in lung adenocarcinoma patients and revealed that the performance of the ViT‐based DTL model was comparable to that of classical CNN models, confirming that ViT was viable for deep learning tasks involving medical images. The ViT‐based DTLN performed exceptionally well and can assist clinicians and radiologists in making accurate judgments and formulating appropriate treatment plans.