Today we have the capability—unparalleled in human history—to compile enormous amounts of information, then apply sophisticated analytic tools and artificial intelligence to help solve patients’ most vexing difficulties. Managing big data is essential to building specialized centers of excellence, and diagnostic imaging is a vital part of their success.
Centers of Excellence: The evolution of precision
Precision medicine is the natural evolution of medical practice. We’ve been attempting to achieve precision medicine for thousands of years. The ancient world labeled patients with "fever"; by the 19th century we could inform this with the germ theory of disease; now we can isolate the subspecies of bacteria and measure susceptibility to antibiotics. We are starting to understand genes that can tell us what the effectiveness of those antibiotics will be, how long to take them, and who will develop side effects.
Today, technology is hyper-accelerating advancements in medical diagnoses and treatments. With it, we can compile huge volumes of data on virtually every pathology and condition, measured and observed through every branch of medicine, from the time a patient first presents, through every step of treatment, and beyond.
The Miami Cancer Institute (MCI) is a magnet for applied data from local and regional cancer experts, including every medical discipline and every support service engaged in providing world-class cancer care to patients across the Southeastern U.S. as well as Latin America and the Caribbean. I’m proud to be one of over 70 radiologists at Radiology Associates of South Florida (RASF) who are actively contributing findings and exploring opportunities for imaging to deliver more accurate cancer diagnoses, helping MCI clinicians provide the most appropriate care plan for each patient.
Of course, the goal of centers of excellence is not realized simply through data collection and analysis. The potential is released through communication, both within the defined center of excellence, and ultimately with the entire global medical community. This seems a good time for me to suggest you join the discussion May 1, at a webinar roundtable featuring five colleagues and myself from RASF: Building a Cancer Care Center of Excellence in the Age of Precision Medicine.
New answers raise new questions
Every day I’m amazed by strides taken in the field of medicine. At the same time, the results of every study seem to raise new questions and new opportunities for discovery as we continue to hone the practice of precision medicine.
For example, The New England Journal of Medicine in April 2018 published a lung cancer study that demonstrated significantly longer and progression-free survival rates among patients who received immunotherapy and chemotherapy combined, versus those who received chemo alone. It’s an important study that certainly has the potential to elevate the standard of cancer care.
However, this study also reveals that, while the combination therapy increased overall survival rates, only half of those receiving the therapy actually responded to it, while the other half experienced little or no benefit. As a radiologist—and knowing the outcomes for each—I would be compelled to reexamine the imaging history of these patients. Perhaps we might unveil indicators—e.g. tumor shape, volume, growth rate, composition, location—that reveal early in the process which patients will respond well to this therapy, and which patients might best be treated through alternative methods.
Of course, it would be no small task for one rad to eyeball the complete diagnostic histories of the over 600 participants in this study—not to mention hundreds of new lung cancer cases diagnosed every day—to detect patterns or anomalies that correlate to patient outcomes.
Computers, on the other hand, are being be trained to do this quickly and efficiently, providing radiologists opportunities to see the big picture more clearly than ever before. Radiomics and artificial intelligence (AI) are two ways radiology will be able to continue to tailor diagnosis and therapy. Perhaps an AI engine will identify the patients who will benefit most from imaging and provide aids to diagnosis; radiomics of a lung nodule might give insight into which patients are most likely to harbor a cancer, or which cancer patients are most likely to benefit from a certain chemotherapy regimen.
Radiologists’ insights are essential to guiding centers of excellence to achieve their promise of personalized patient care.