This imaging technique is analogous to ultrasound: The macula is scanned with a beam of focused laser light, and the amount of reflected light coming back at each point is measured and recorded. The resulting stream of data is computationally converted into an extremely high-resolution, three-dimensional image.

“Right now, a patient who goes into the ophthalmologist’s office typically gets an SD-OCT scan anyway,” said the study’s senior author, Daniel Rubin, MD, assistant professor of radiology and of biomedical informatics. “Our technique involves no new procedures in the doctor’s office — patients get the same care they’ve been getting anyway. We’ve simply added on a computerized image-processing step that analyzes not only that scan but any previous ones available from that same patient’s earlier visits.”

Generating a risk score

From this computerized analysis, the investigators are able to generate a risk score: a number that predicts a patient’s likelihood of progressing to the wet stage within one year, three years or five years. The likelihood of progression within one year is most relevant, because it translates into a concrete recommendation: how soon to schedule the patient’s next office visit.

Until now, attempts to predict AMD progression have relied on eye doctors examining color photographs of the retina taken in their offices. There is no way to translate that information into risk scores. The high-resolution imaging technique, Rubin said, provides much richer detail. “You can almost see individual cells,” he said. Plus, it is far more amenable to digital analysis. Previously proposed predictive models have shown some accuracy over long periods of time, but none has been adequately accurate over the shorter, one-year time frame that’s relevant to making decisions about office-visit frequency, Rubin said.

You can almost see individual cells.

In the study, the Stanford team analyzed data from 2,146 scans of 330 eyes in 244 patients seen at Stanford Health Care over a five-year period. They found that certain key features in the images, such as the area and height of drusen, the amount of reflectivity at the macular surface and the degree of change in these features over time, could be weighted to generate a patient’s risk score. Patients were followed for as long as four years, and predictions of the model were compared with actual instances of progression to wet AMD. The model accurately predicted every occurrence of progression to the wet stage within a year. About 40 percent of the time when the model did predict progression to wet AMD within a year, the prediction was not borne out.

“No test gets it right 100 percent of the time,” Rubin said. “You can tweak the model to trade off the risk of telling someone they will progress when they actually won’t against the risk of telling them they won’t progress when they actually will. With AMD you really don’t want any false negatives, so you tune the model accordingly. The downside is that some patients will wind up being told to come in sooner than, in fact, they probably need to. But that’s nothing compared with the downside of a patient at high risk for progression’s not coming in soon enough.”

Larger studies needed

Rubin emphasized that this proof-of-principle study needs to be followed up by a larger study, ideally using data gathered from patients seen at other institutions. He and his associates have now embarked on such a study.

The study’s lead author is Luis de Sisternes, PhD, a postdoctoral scholar in radiology. Other Stanford co-authors are Robert Tibshirani, PhD, professor of health research and policy and of statistics; Theodore Leng, MD, clinical assistant professor of ophthalmology; and former postdoctoral scholar Noah Simon, PhD, now at the University of Washington.

The work was supported by grants from Stanford Bio-X and Spectrum-Stanford Predictives and Diagnostics Accelerator.

Stanford’s Department of Radiology also supported this work.