AI in Lung Health: Benchmarking Detection and Diagnostic Models Across Multiple CT Scan Datasets

Background: Development of artificial intelligence (AI) models for lung cancer screening requires large, well-annotated low-dose computed tomography (CT) datasets and rigorous performance benchmarks. Purpose: To create a reproducible benchmarking resource leveraging the Duke Lung Cancer Screening (DLCS) and multiple public datasets to develop and evaluate models for nodule detection and classification. Materials & Methods: This retrospective study uses the DLCS dataset (1,613 patients; 2,487 nodules) and external datasets including LUNA16, LUNA25, and NLST-3D. For detection, MONAI RetinaNet models were trained on DLCS (DLCS-De) and LUNA16 (LUNA16-De) and evaluated using the Competition Performance Metric (CPM). For nodule-level classification, we compare five strategies: pretrained models (Models Genesis, Med3D), a self-supervised foundation model (FMCB), and ResNet50 with random initialization versus Strategic Warm-Start (ResNet50-SWS) pretrained with detection-derived candidate patches stratified by confidence. Results: For detection on the DLCS test set, DLCS-De achieved sensitivity 0.82 at 2 false positives/scan (CPM 0.63) versus LUNA16-De (0.62, CPM 0.45). For external validation on NLST-3D, DLCS-De (sensitivity 0.72, CPM 0.58) also outperformed LUNA16-De (sensitivity 0.64, CPM 0.49). For classification across multiple datasets, ResNet50-SWS attained AUCs of 0.71 (DLCS; 95% CI, 0.61-0.81), 0.90 (LUNA16; 0.87-0.93), 0.81 (NLST-3D; 0.79-0.82), and 0.80 (LUNA25; 0.78-0.82), matching or exceeding pretrained/self-supervised baselines. Performance differences reflected dataset label standards. Conclusion: This work establishes a standardized benchmarking resource for lung cancer AI research, supporting model development, validation, and translation. All code, models, and data are publicly released to promote reproducibility.