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

2026-03-26

AI Summary

This study addresses the challenge of effectively integrating global and local features in deep learning–based classification of benign and malignant pulmonary nodules, a task further complicated by the lack of clinical validation in existing approaches. To this end, we propose DeepFAN, a Transformer-based model trained on over 10,000 pathologically confirmed cases—the largest such dataset reported to date—and rigorously evaluated through a multicenter, multi-reader clinical trial. DeepFAN synergistically combines global and local imaging features and incorporates explainability analysis to assess feature contributions. The model achieves an internal test AUC of 0.939 and a clinical trial AUC of 0.954. When used as a diagnostic aid, it improves junior radiologists’ average AUC by 10.9%, with significant gains in sensitivity, specificity, and accuracy, and elevates inter-rater agreement from fair to moderate.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• pulmonary nodules
• deep learning
• clinical validation
• diagnostic consistency
• CT scans

Innovation

• transformer-based model
• multi-reader multi-case trial
• pulmonary nodule classification
• AI-radiologist collaboration
• explainable deep learning