DeepFAN, a transformer-based deep learning model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multi-reader, multi-case trial
Organizations: 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, Hubei Province, China. · Key Laboratory of Cerebrovascular Disease lmaging and Artificial Intelligence, Huangshi，Hubei Province, China · Department of Radiology, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, Hubei Province, 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. · School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China.
Abstract
The widespread adoption of CT has notably increased the number of detected lung nodules. However, current deep learning methods for classifying benign and malignant nodules often fail to comprehensively integrate global and local features, and most of them have not been validated through clinical trials. To address this, we developed DeepFAN, a transformer-based model trained on over 10K pathology-confirmed nodules and further conducted a multi-reader, multi-case clinical trial to evaluate its efficacy in assisting junior radiologists. DeepFAN achieved diagnostic area under the curve (AUC) of 0.939 (95% CI 0.930-0.948) on an internal test set and 0.954 (95% CI 0.934-0.973) on the clinical trial dataset involving 400 cases across three independent medical institutions. Explainability analysis indicated higher contributions from global than local features. Twelve readers' average performance significantly improved by 10.9% (95% CI 8.3%-13.5%) in AUC, 10.0% (95% CI 8.9%-11.1%) in accuracy, 7.6% (95% CI 6.1%-9.2%) in sensitivity, and 12.6% (95% CI 10.9%-14.3%) in specificity (P<0.001 for all). Nodule-level inter-reader diagnostic consistency improved from fair to moderate (overall k: 0.313 vs. 0.421; P=0.019). In conclusion, DeepFAN effectively assisted junior radiologists and may help homogenize diagnostic quality and reduce unnecessary follow-up of indeterminate pulmonary nodules. Chinese Clinical Trial Registry: ChiCTR2400084624.