Gemma is Managing Editor at AIMed, with responsibility for engaging and growing the AIMed community and to highlight stories of health AI in action. An experienced science graduate with a background in veterinary and nonprofit sectors, she also volunteers as a Wish Granter for Make a Wish UK.
A study has shown that radiomics-based machine learning models may detect pancreatic cancer on prediagnostic CT scans substantially earlier than current methods for clinical diagnosis.
“Up to 40% of small pancreas cancers may not show up on standard imaging. As a result, the majority of patients present with advanced and incurable disease,” says Ajit Goenka, M.D., a Mayo Clinic diagnostic radiologist and the study’s senior author.
For this reason, Dr. Goenka and his colleagues looked to incorporate AI into radiological screening to detect pancreatic cancer at an earlier, more curable state. “We found that AI models can detect cancer from a normal-appearing pancreas on CTs several months prior to cancer symptoms, even when the disease was beyond the scope of perception of radiologists.”
Researchers computationally extracted the imaging signature of early cancer from CT scans. Prediagnostic CTs are CTs that were done for unrelated indications between three months and three years prior to cancer occurrence. They used an age-matched group of control subjects who did not develop pancreatic cancer during three years of follow-up. Expert radiologists then segmented the pancreas on CTs from both groups and computationally extracted and quantified the metrics of pancreas tissue heterogeneity. The researchers then built advanced machine learning models that could predict the future risk of pancreatic cancer at a median time of 386 days, a range of 97 to 1,092 days, prior to clinical diagnosis with accuracies that ranged from 94% to 98%.
“In comparison, radiologists were unable to reliably differentiate between patients who went on to develop cancer versus those who had normal pancreas,” says Sovan Mukherjee, Ph.D., a senior data science analyst in Dr. Goenka’s team and the study’s first author. “We also tested our AI models against variations in image noise, scanner models, image acquisition protocols and postprocessing parameters, and found them to be unaffected by these variations.”
Dr. Goenka says a large prospective clinical trial is underway to evaluate the impact of a pancreatic cancer screening strategy using CTs in 12,500 participants. The trial is being led by Suresh Chari, M.D., an emeritus Mayo Clinic gastroenterologist.