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

• URL: http://arxiv.org/abs/2405.04605v3
• Date: Wed, 23 Apr 2025 21:20:50 GMT
• Title: AI in Lung Health: Benchmarking Detection and Diagnostic Models Across Multiple CT Scan Datasets
• Authors: Fakrul Islam Tushar, Avivah Wang, Lavsen Dahal, Michael R. Harowicz, Kyle J. Lafata, Tina D. Tailor, Joseph Y. Lo,
• Abstract summary: Lung cancer remains the leading cause of cancer-related mortality worldwide.<n>With the growing integration of artificial intelligence into medical imaging, the development and evaluation of robust AI models require access to large, well-annotated datasets.<n>We benchmark deep learning models for both 3D nodule detection and lung cancer classification.
• Score: 0.33923727961771083
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Lung cancer remains the leading cause of cancer-related mortality worldwide, and early detection through low-dose computed tomography (LDCT) has shown significant promise in reducing death rates. With the growing integration of artificial intelligence (AI) into medical imaging, the development and evaluation of robust AI models require access to large, well-annotated datasets. In this study, we introduce the utility of Duke Lung Cancer Screening (DLCS) Dataset, the largest open-access LDCT dataset with over 2,000 scans and 3,000 expert-verified nodules. We benchmark deep learning models for both 3D nodule detection and lung cancer classification across internal and external datasets including LUNA16, LUNA25, and NLST-3D+. For detection, we develop two MONAI-based RetinaNet models (DLCSDmD and LUNA16-mD), evaluated using the Competition Performance Metric (CPM). For classification, we compare five models, including state-of-the-art pretrained models (Models Genesis, Med3D), a selfsupervised foundation model (FMCB), a randomly initialized ResNet50, and proposed a novel Strategic Warm-Start++ (SWS++) model. SWS++ uses curated candidate patches to pretrain a classification backbone within the same detection pipeline, enabling task-relevant feature learning. Our models demonstrated strong generalizability, with SWS++ achieving comparable or superior performance to existing foundational models across multiple datasets (AUC: 0.71 to 0.90). All code, models, and data are publicly released to promote reproducibility and collaboration. This work establishes a standardized benchmarking resource for lung cancer AI research, supporting future efforts in model development, validation, and clinical translation.

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