Opportunistic Screening: Radiology Scientific Expert Panel

Abstract

Radiologic tests often contain rich imaging data not relevant to the clinical indication. Opportunistic screening refers to the practice of systematically leveraging these incidental imaging findings. Although opportunistic screening can apply to imaging modalities such as conventional radiography, US, and MRI, most attention to date has focused on body CT by using artificial intelligence (AI)-assisted methods. Body CT represents an ideal high-volume modality whereby a quantitative assessment of tissue composition (eg, bone, muscle, fat, and vascular calcium) can provide valuable risk stratification and help detect unsuspected presymptomatic disease. The emergence of "explainable" AI algorithms that fully automate these measurements could eventually lead to their routine clinical use. Potential barriers to widespread implementation of opportunistic CT screening include the need for buy-in from radiologists, referring providers, and patients. Standardization of acquiring and reporting measures is needed, in addition to expanded normative data according to age, sex, and race and ethnicity. Regulatory and reimbursement hurdles are not insurmountable but pose substantial challenges to commercialization and clinical use. Through demonstration of improved population health outcomes and cost-effectiveness, these opportunistic CT-based measures should be attractive to both payers and health care systems as value-based reimbursement models mature. If highly successful, opportunistic screening could eventually justify a practice of standalone "intended" CT screening.

Graphical abstract

Figure 1:

Case example of artificial intelligence (AI)–assisted cardiometabolic opportunistic screening in a 77-year-old woman with flank pain who underwent abdominal CT for urolithiasis evaluation. Automated CT-based body composition algorithms can be applied (prospectively or retrospectively) regardless of the clinical indication for imaging. Unenhanced transverse CT scans at the L1 (top, left) and L3 (bottom, left) vertebral levels and a coronal maximum intensity projection image for volumetric parameters (top, right) show the automatic segmentation of skeletal muscle, abdominal fat (visceral and subcutaneous), trabecular bone, aortic calcium, liver, and spleen (see color legend) provided by this particular research-based suite of AI tools. In this case, unsuspected osteoporosis (L1 trabecular attenuation <100 HU), sarcopenia (L3 muscle attenuation of 20 HU), and advanced atherosclerotic disease (aortic Agatston score of 5889) are evident when these tools are retrospectively applied (note that this scan was obtained more than a decade ago). Without prospective reporting, this patient went on to have an osteoporotic vertebral compression fracture, in addition to subsequent acute myocardial infarction that led to heart failure and death.

Figure 2:

Artificial intelligence (AI)–assisted cardiometabolic opportunistic screening at chest CT. (A) Unenhanced transverse CT scan at the T12 vertebral level and (B) coronal maximum intensity projection image with volumetric data show automatic segmentation and quantification of body composition data, similar to the abdominal example in Figure 1. (C) Unenhanced CT scan shows nongated coronary calcium segmentation and scoring. Other opportunistic cardiopulmonary measures are possible, including aortic calcific plaque.

Figure 3:

Examples of other automated opportunistic assessments in three different patients. (A) Axial unenhanced CT scan shows automated segmentation of the liver into Couinaud segments, which allows for assessment of segmental redistribution and can demonstrate unsuspected hepatic fibrosis. (B) Axial unenhanced CT scan shows automated segmentation of the pancreas, which can help screen for changes suggestive of underlying diabetes. (C) Axial unenhanced CT scan shows automated segmentation of the kidneys and renal calculi (right kidney), which allows for stone detection, as well as quantitative volumetric assessment of renal size and stone burden.