Transformer-Based Preoperative Invasiveness Identification and Postoperative Recurrence Risk Stratification in Stage T1N0M0 Lung Adenocarcinoma: A Multi-Center Cohort Study.
Authors
Affiliations (15)
Affiliations (15)
- Department of Respiratory and Critical Care Medicine Hubei Province Clinical Research Center for Major Respiratory Disease Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
- Department of Respiratory and Critical Care Medicine Zhongnan Hospital of Wuhan University Wuhan China.
- Department of Respiratory and Critical Care Medicine China-Japan Friendship Hospital Beijing China.
- Hubei Key Laboratory of Optical Information and Pattern Recognition School of Optical Information and Energy Engineering Wuhan Institute of Technology Wuhan China.
- Department of Respiratory and Critical Care Medicine Shiyan Key Laboratory of Major Chronic Respiratory Disease, Taihe Hospital Hubei University of Medicine Shiyan China.
- Department of Pathology Renmin Hospital of Wuhan University Wuhan China.
- Department of Respiratory and Critical Care Medicine, Xiangyang Central Hospital Affiliated Hospital of Hubei University of Arts and Science Xiangyang China.
- Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital Zhengzhou University Zhengzhou China.
- Department of Respirology Guangzhou Institute of Respiratory Health The First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Diseases Guangzhou China.
- Hubei Province Key Laboratory of Biological Targeted Therapy, MOE Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China.
- Hubei Province Engineering Research Center for Tumor-Targeted Biochemotherapy, Union Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China.
- Department of Radiology Renmin Hospital of Wuhan University Wuhan China.
- Institute of Research and Clinical Innovations Neusoft Medical Systems Co. Ltd Wuhan China.
- Department of Radiology Union Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China.
- Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan China.
Abstract
Surgery is the primary curative treatment for early-stage lung adenocarcinoma (LUAD), with accurate identification of tumor invasiveness critical for determining pulmonary resection extent. Due to significant variability in postoperative recurrence among early-stage LUAD patients with aggressive tumors, a more effective risk stratification system is urgently needed to improve prognosis. This study developed the T1LUT system, a transformer-based tool for T1N0M0 LUAD management, covering preoperative tumor invasiveness identification and postoperative recurrence risk stratification. Preoperatively, a 3D Vision Transformer (ViT) with extra learnable tokens analyzes CT images. It outperforms 3D ViT with only CLS tokens, 2D ViT, and 3D ResNet across three independent test centers (Center D: 0.896 vs. 0.892, 0.879, 0.836; Center E: 0.906 vs. 0.837, 0.837, 0.835; Center F: 0.884 vs. 0.884, 0.881, 0.861), achieving higher mean AUROC to accurately assess invasiveness and guide surgical resection extent. Postoperatively, the multimodal IAC-PR model integrates CT images, reports, pathological data, and clinical/demographic information. It stratifies invasive adenocarcinoma (IAC) patients into low-, medium-, or high-risk groups with an AUROC of 0.807 in testing-significantly better than single/partial-modal models. Validated via large-scale multicenter research, this system overcomes traditional assessment limitations, offering a robust tool for precise early LUAD treatment and improved prognosis.