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

• URL: http://arxiv.org/abs/2405.04605v4
• Date: Wed, 29 Oct 2025 02:33:21 GMT
• Title: AI in Lung Health: Benchmarking Detection and Diagnostic Models Across Multiple CT Scan Datasets
• Authors: Fakrul Islam Tushar, Avivah Wang, Lavsen Dahal, Ehsan Samei, Michael R. Harowicz, Jayashree Kalpathy-Cramer, Kyle J. Lafata, Tina D. Tailor, Cynthia Rudin, Joseph Y. Lo,
• Abstract summary: Development of artificial intelligence (AI) models for lung cancer screening requires large, well-annotated low-dose computed tomography (CT) datasets and rigorous performance benchmarks.<n>This study uses the DLCS dataset (1,613 patients; 2,487 nodules) and external datasets including LUNA16, LUNA25, and NLST-3D.<n>For detection, MONAI RetinaNet models were trained on DLCS (DLCS-De) and LUNA16 (LUNA16-De) and evaluated using the Competition Performance Metric (CPM)
• Score: 16.77038992770942
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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