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Opportunistic Screening: Radiology Scientific Expert Panel.

Perry J Pickhardt¹, Ronald M Summers¹, John W Garrett¹

¹From the Departments of Radiology (P.J.P., J.W.G.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham, Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ (G.N.N.).

|May 23, 2023

Summary

Opportunistic screening uses incidental findings from radiologic tests, especially CT scans, for early disease detection. Artificial intelligence can automate measurements, but implementation requires addressing standardization, data, and regulatory challenges for improved population health.

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Radiologic tests yield incidental findings not related to the primary clinical indication.
Opportunistic screening leverages these incidental findings for broader health assessments.
Body computed tomography (CT) is a high-volume modality suitable for quantitative tissue analysis.

Purpose of the Study:

To explore the potential of opportunistic screening using incidental findings from radiologic tests.
To highlight the role of artificial intelligence (AI) in automating quantitative assessments for opportunistic screening.
To identify barriers and facilitators for the clinical implementation of opportunistic CT screening.

Main Methods:

Review of current practices and potential applications of opportunistic screening, focusing on body CT.
Discussion of AI-driven quantitative assessment of tissue composition (bone, muscle, fat, vascular calcium).
Analysis of potential barriers including stakeholder buy-in, standardization, normative data, and regulatory/reimbursement issues.

Main Results:

Body CT enables quantitative assessment of tissue composition for risk stratification and presymptomatic disease detection.
Explainable AI can automate these measurements, paving the way for clinical integration.
Widespread implementation faces challenges in standardization, data normalization, and regulatory/reimbursement frameworks.

Conclusions:

Opportunistic CT screening, powered by AI, offers a promising avenue for early disease detection and risk stratification.
Overcoming implementation barriers is crucial for realizing the potential of population health benefits and cost-effectiveness.
Successful opportunistic screening may eventually lead to the development of standalone, intended CT screening programs.