RSNA: Beyond pretty pictures--the case for quantitative image analysis

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CHICAGO--Image interpretation will need to include quantitative analysis in the future, according to a session on Nov. 30 at the 97th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA). Daniel C. Sullivan, MD, professor of radiology at Duke University in Durham, N.C., outlined the clinical need for quantitative imaging and identified key technical barriers during the presentation.

At the most basic level, medicine has become more of a science and less of an art. Imaging, which relies heavily on observation, must follow suit. Molecular medicine, evidence-based medicine and personalized medicine all require some form of quantitative or objective data to inform therapeutic decision making.

Sullivan shared a number of video interviews with clinicians who discussed the role of imaging and quantitative data in their practices. “CT reports are not useful for the prognosis or treatment of chronic obstructive pulmonary disease (COPD),” according to one pulmonologist and researcher. “We need more information to help us understand the prognosis and therapeutic [effect]. The pictures are beautiful, but the impact of CT on clinical care is not great.”

In contrast, showing a 1 percent change in lung density via imaging would be meaningful and useful for a physician treating a patient with COPD.

Another disease state where quantitative imaging may be helpful, said Sullivan, is cancer. The standard way of measuring tumor burden is the RECIST criteria, and traditionally oncologists have used a threshold of a 30 percent change as the determination for progression. However, the recent availability of expanded therapeutic alternatives may make a 10 percent threshold more clinically useful, said Sullivan.

Tumor measurements also may incorporate volume as well as diameter. “Until recently, algorithms to measure volume rather than diameter were not available. Data show that it may be useful to consider volume; recent clinical trials indicate volume may be more sensitive and specific.”

Researchers in the U.K., in fact, have applied quantitative measurements in its screening protocol for the management of lung nodules and uses volume doubling time, rather than diameter, to guide decision making.

Another disease that may be an ideal candidate for quantitative imaging is Alzheimer’s disease. “Identifying change in hippocampal volume may be more important that diagnosing disease,” said Sullivan. That’s because the hippocampus changes slowly with Alzheimer’s disease. An imaging biomarker that could show that the brain is not getting worse would be helpful in the development of therapies.

However, Sullivan noted that the development and application of quantitative imaging has been hampered by a disconnect among researchers, clinicians and technology. “We need data from clinical trials to show the value of quantitative data, which requires scanners that can provide these data with a high degree of accuracy and reproducibility.” However, according to vendors, customers aren’t asking for quantitative capabilities, so they aren’t investing in the development of these tools. But without these tools, physicians cannot experience quantitative imaging.

“It’s a chicken and egg development problem,” said Sullivan.

Sullivan concluded with a sneak peek for his vision of an imaging report in 2030. The report, he said, will include three quantitative measures: the probability of disease, response to current management and positive predictive value for a specific therapy.