由 John W. Mitchell
, Senior Correspondent | November 26, 2017
He cited several current imaging research projects targeting diagnostics and therapies for several diseases. These include: much earlier diagnosis of Alzheimer’s disease; prostate “lumpectomy” to better target tumors; the first FDA-approved high speed, high-resolution 3-D Gd MR for improved diagnosis and treatment tracking of invasive ductal cancer, which increased interpretation by 50 percent and reduced missed cancers by 39 percent; and direct radiologist-to-patient counseling based on sequential chest CTs over time, to motivate patients to make lifestyle changes to reduce the risk of adverse cardiac events (in consultation with a cardiologist).
He also presented a video clip of four experts in three cities – London, Mumbai and Atlanta – working together virtually in real time through the use of avatars to diagnose the results of a patient colonoscopy conducted in London.
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Such achievements, Pettigrew advocated, are all possible due to the rapidly evolving science of imaging deep-learning and artificial intelligence – or "radiomics." Radiomics is the emerging science where images are not merely pictures but data driven by the cross-disciplinary practices of radiology, pathology and genomics.
“Modern imaging is information science and will improve the value proposition of imaging,” said Pettigrew. “Tomorrow‘s radiologist will leverage AI.”
The final speaker at the opening session, Dr. Elias Zerhouni, a radiologist and biomedical engineer, is former Director of the National Institutes of Health and is currently president of global research and development at Sanofi.
“The leading edge of science … will be what is already here,” said Zerhouni. He explained that in 2003 he was asked to write a white paper about the future of imaging in 30 years. This seemed a monumental task that made him nervous, but when he looked back 30 years, such trends were already obvious and predictable.
He, too, said that imaging is more and more becoming interdisciplinary. He noted, for example, imaging recipients of Nobel awards have included 11 physicists, eight chemists and four physicians. He attributed this to the “tension between complexity and precision to unravel the … daunting complexity of biological systems in health and disease”. This, he added, is driven by the demand that precision medicine help control unsustainable health care costs.
According to Zerhouni, part of the solution is to eliminate the variation in the way radiologists practice medicine.