DeepFAN, a transformer-based model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multireader, multicase trial.
Authors
Affiliations (15)
Affiliations (15)
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Theranostics and Translational Research Center, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Artificial Intelligence Lab, Deepwise Healthcare, Beijing, China.
- Department of Radiology, Peking University People's Hospital, Beijing, China.
- Department of Epidemiology and Health Statistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- Department of Biostatistics, Peking University First Hospital, Beijing, China.
- Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Huangshi, China.
- Key Laboratory of Cerebrovascular Disease lmaging and Artificial Intelligence, Huangshi, China.
- Department of Radiology, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China.
- 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- Department of Medicine Imaging, School of Clinical Medicine, Southwest Medical University, Luzhou, China.
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. [email protected].
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. [email protected].
- Department of Radiology, Peking University People's Hospital, Beijing, China. [email protected].
- School of Computing and Data Science, The University of Hong Kong, Hong Kong, China. [email protected].
Abstract
The widespread adoption of computed tomography has increased the detection of lung nodules. However, deep learning methods for classification of benign and malignant nodules often fail to comprehensively integrate global and local features, and most of these methods have not been validated through clinical trials. Here we developed DeepFAN, a transformer-based model trained on more than 10,000 pathology-confirmed nodules, and conducted a multireader, multicase clinical trial (Chinese Clinical Trial Registry: ChiCTR2400084624) to evaluate its efficacy in assisting junior radiologists. DeepFAN achieved diagnostic area under the curve (AUC) values of 0.939 (95% CI 0.930-0.948) on an internal test set and 0.954 (95% CI 0.934-0.973) on a clinical trial dataset involving 400 cases across three independent medical institutions. Explainability analysis indicated higher contributions from global than local features. The average performance of 12 readers improved significantly: by 10.9% (95% CI 8.3-13.5%) for AUC, 10.0% (95% CI 8.9-11.1%) for accuracy, 7.6% (95% CI 6.1-9.2%) for sensitivity and 12.6% (95% CI 10.9-14.3%) for specificity (all P < 0.001). Nodule-level interreader diagnostic consistency improved from fair to moderate (overall κ: 0.313 versus 0.421; P = 0.019). These results indicate that DeepFAN can effectively assist junior radiologists and could help to homogenize diagnostic quality and reduce unnecessary follow-up of patients with indeterminate pulmonary nodules.