The art of image interpretation is giving way to objective scientific quantification. If imaging is going to truly meet the requirements of evidence-based, personalized medicine, it will take more than subjective—and occasionally ambiguous—scan reads. Fortunately, a number of organizations are already hard at work designing processes to help leverage the next generation of quantitative imaging data.
Consider the example of a PET scan for lung cancer. Currently, clinical evaluation of such a study is not done quantitatively. Physicians will view the scan looking for “hot” areas that indicate tumor uptake of F-fluorodeoxyglucose (FDG) radiotracer. After treatment, if the scan appears “cooler,” it indicates a response to the treatment.
“But the issue may be, if those two PET images were taken on different instruments…or the technologist didn’t perform the scan at the same time with the same settings, the physician may be wrong about the patient’s response if the difference [in FDG uptake] is not large,” says Paula M. Jacobs, PhD, associate director of the National Cancer Institute’s (NCI’s) Cancer Imaging Program. A major difference in tumor response can be seen so long as a scanner is functioning at all, but when trying to evaluate subtle responses early in therapy, physicians need assurances that the scans they are evaluating are comparable.
Variability also clouds the recommendations of radiologists. A recent study of CT and MRI scans for pancreatic lesions found wide radiologist-to-radiologist variation in the proportion of cases recommended for follow-up imaging, from 10.5 percent to 76.9 percent (Radiology 2011;259:136-141). The authors attributed more than 80 percent of the variation to the personal opinions of the individual radiologist. An accompanying editorial warned that “variation in reporting can lead to confusing recommendations to referring physicians on the same patient, eroding referrer confidence and jeopardizing referrals.”
The search for standards
Standardized equipment and standardized quantitative measurements can more accurately guide decisions on additional imaging or the course of chemotherapy or radiation treatments, but they also have important benefits in the clinical trial setting. The size of clinical trials can be reduced if uncertainty in physical data collection is limited, which in turn drives down costs and time required for the trial.
To this end, Jacobs and colleagues within the Cancer Imaging Program, including Robert J. Nordstrom, PhD, are overseeing the Quantitative Imaging Network (QIN), a collection of research teams located primarily within NCI’s designated cancer centers. They are tasked with improving quantitative methods for the prediction and/or measurement of tumor response to therapies in the multi-center clinical trial setting, with the ultimate goal of supporting the role of quantitative imaging in clinical decision making.
First initiated in 2008, QIN has seen a lot of recent growth, expanding to 19 sites. It represents the first consensus effort to bring the oncology, radiology, medical physics, and informatics communities together to develop tools and methods for the trial setting, explains Laurence Clarke, PhD, branch chief of imaging technology development for the Cancer Imaging Program.
The research challenge is complex and multifaceted, requiring innovative solutions throughout the entire imaging process. This starts with data collection and analysis for all commercially supported imaging modalities. Clarke says there is a lot of variability in collecting data from advanced techniques such as diffusion weighted imaging (DWI) and PET/CT. To remedy this, QIN teams are working with specialized phantoms to characterize PET/CT and MRI systems to reverse engineer a solution that can correct for variability across the major scanner vendors and different clinical trial sites. “This is really a software tool to actually reduce measurement uncertainty across each commercial platform, and each model of that platform,” says Clarke. The imaging industry has shown an interest to adopt this solution for the clinical trial setting.
QIN researchers at the University of Iowa, Iowa City, received a NCI (R01) grant to work with vendors to develop a software tool to harmonize PET/CT data that is expected to be available in the next year or two, and they are using the QIN network as a test bed for these methods. Meanwhile, at the University of Michigan, Ann Arbor, the QIN team is doing