Many nations around the world are now implementing CT-based lung cancer screening. Growing evidence led the United States to require insurance coverage for LCS in high-risk individuals. Current CT scanners can obtain vast amounts of anatomic and quantitative information from the viscera of the chest cavity, and it has become evident that the CT images obtained from LCS contain additional health information, including information that enables the early detection of other major tobacco-associated diseases, such as coronary artery disease and emphysema. Chest CT screening is now being integrated with the use of AI tools, and such tools will be essential to organize and manage the complex screening workflow required to efficiently deliver this rapidly expanding service. A threat to realizing the health benefits of chest CT screening is the difficulty in aggregating sufficient numbers of CT images and clinical follow-up data for research purposes. Enabling access to clinical imaging and outcome data as a public utility may be essential in addressing bottlenecks to innovation to early chest disease management. The collections of chest CT images with clinical data that are being accrued for routine screening care could be repurposed with web-based strategies at low cost to enable a new range of strategic analyses and rapid AI tool development.
Mulshine et al. (Tue,) studied this question.
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