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. 2025 Jan;52(1):375-387.
doi: 10.1002/mp.17414. Epub 2024 Sep 28.

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

Chuanyu Chen  1 , Yi Luo  1 , Qiuyang Hou  1 , Jun Qiu  1 , Shuya Yuan  1 , Kexue Deng  1
Affiliations

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

Chuanyu Chen et al. Med Phys. 2025 Jan.

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.

Keywords: deep learning; lung adenocarcinoma; lymph node metastasis; nomogram; vision transformer.

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