Opportunistic imaging could revolutionize patient care

More than 80 million CT scans are performed each year in the U.S. alone. In fact, most Americans have had a CT scan by the time they reach age 60, and many will have had several scans.

While CT scans often provide key information for diagnosing a certain problem, our current approach leaves an immense amount of information unmined. Given the cost and radiation involved in CT scanning, we have an obligation not to leave any useful data on the metaphorical cutting room floor.

Using a CT scan to look for other potential problems is called opportunistic imaging, and it could revolutionize the way we care for our patients. Taking advantage of the previously unused information will allow us to identify a substantial number of patients at risk for adverse clinical events early, thereby improving outcomes and saving costs to our health care system.

The fact that CT scans could identify findings outside the area the clinician was concerned about has long been recognized. For example, a CT scan of the abdomen in the workup of, say, bloody urine, might demonstrate a liver cyst or an adrenal mass. While these serendipitous findings sometimes led to an early diagnosis of a cancer, they frequently resulted in additional workups that, in the end, identified the lesion as benign. These unintended findings were dubbed “incidentalomas” and helped create a sentiment among some clinicians that we should use the CT scan results only for their intended purposes and not get distracted by extraneous findings.

But technology and other advances in medicine are making this objection increasingly obsolete.

About two decades ago radiologists and others realized that the information on a CT scan could, quite intentionally, serve other clinical purposes. The concept of opportunistic CT imaging was born. One of the first uses was for the assessment of bone density to diagnose osteoporosis and to predict the risk of fractures. Osteoporosis is often called the “silent killer” because it is typically asymptomatic until a fracture occurs, and about one-third of patients who sustain a hip fracture from osteoporosis die within one year, according to the U.S. Preventive Services Task Force.

Yet disparities in access to screening remain, particularly among ethnic and racial minority groups. In one study, the likelihood of an African American woman at-risk for osteoporosis being referred for screening was 61% lower than that of a white woman of similar risk. However, African American women who sustain a fracture from osteoporosis have a one-third higher mortality than white women, highlighting the need for equity in access to osteoporosis screening.

This gap in screening could be narrowed substantially by opportunistic CT. CT scans that are performed for, say, cancer screening, evaluation for a kidney stone, or belly pain can be easily used to measure bone density, because an abdominal CT routinely also gives a view of the spine and pelvic bones. While in the past this required laborious and time-consuming methods, advances in machine learning and artificial intelligence have made it possible to automate the analysis of CT scans and quantify bone density, body composition, and vascular calcifications within seconds.

But osteoporosis screening isn’t the only potential benefit of opportunistic CT. It can also provide information about the presence of harmful fat depots and calcified plaques in the blood vessels, including in the coronary arteries. These parameters have been shown to predict hospital length of stay after surgery, risk of complications after Covid, and even risk of developing diabetes or a heart attack, and death. By assessing body composition on opportunistic CTs, clinicians can better tailor treatment plans to individual patients without additional tests. A recent cost-effective analysis of AI-based opportunistic CTs to detect the risk of heart disease and osteoporosis found that opportunistic imaging was able to save costs and improve clinical efficacy.

In fact, the FDA has already approved several AI algorithms that can be applied retrospectively or prospectively to routine CT scans, making possible large-scale population-based screening. Automatically generated fracture and/or cardiometabolic risk scores could provide objective information to guide clinical decision-making, thereby reducing disparities in access to screening and increasing the value of the scans.

While the potential benefits of opportunistic CT are high, there are also challenges that must be addressed. The major one concerns the accuracy of AI algorithms. If they are too sensitive, they can lead to overdiagnosis, unnecessary testing, and patient anxiety.

But as screening becomes more common, we will gather more data linking CT findings to important clinical outcomes, which should render the algorithms ever more accurate over the next years.

We also have to educate the referring providers who order CTs for a specific reason on how to deal with the additional data they did not ask for. For example, is a physician who orders a CT for belly pain and then receives information on osteoporosis, vascular calcification, or visceral adiposity required to follow up on these findings? How should they communicate these findings to their patient? These are knotty questions in an environment in which clinician burnout is high and time pressures are often overwhelming.

Despite these challenges, opportunistic CT can provide a more comprehensive picture of a patient’s health and improve the management of a range of conditions. As health systems face mounting financial pressures, more imaging studies are performed, and AI improves, opportunistic imaging has the potential to markedly improve the way medicine is practiced. As patients go through the trouble of obtaining radiologic studies and accept the financial risk and radiation exposure, don’t we have an obligation to use all the information contained in those studies to improve their health outcomes?

Miriam A. Bredella is professor of radiology at Harvard Medical School and vice chair of the Department of Radiology at the Massachusetts General Hospital. Robert M. Wachter is professor of medicine and chair of the Department of Medicine at the University of California, San Francisco.