Sanjay Aneja, MD, associate professor of therapeutic radiology, biomedical engineering, and bioinformatics and data science, has been awarded a seven-year, $3.5 million R37 MERIT Award from the National Cancer Institute (NCI) to advance the development of artificial intelligence–derived imaging biomarkers for prostate cancer.
Prostate cancer is the second leading cause of cancer-related death among men in the United States. It is still difficult to accurately identify which patients with localized disease are most likely to have their cancer return after treatment.
To address this need, the Aneja Lab at Yale developed a deep learning–based MRI biomarker capable of identifying patients at the highest risk of disease recurrence. This non-invasive imaging biomarker has the potential to improve prostate cancer risk stratification, personalize treatment decisions, and reduce unnecessary therapy.
The R37 MERIT Award is one of the NCI’s most prestigious investigator awards. It provides seven years of stable funding to investigators who have demonstrated exceptional scientific productivity and are judged to be highly likely to continue making outstanding contributions to cancer research.
Over the next seven years, the Aneja Lab will validate its MRI biomarker using large multi-institutional patient cohorts as well as imaging collected from two recently completed randomized clinical trials. The team will also collaborate with industry partners to overcome key informatics barriers that have historically limited the clinical implementation of imaging biomarkers, with the goal of bringing these technologies into the clinic.
Aneja commented, “I am deeply grateful to the National Institutes of Health for supporting this work and to our outstanding multidisciplinary academic and industry collaborators whose expertise has been essential to this project. As a radiation oncologist caring for patients with prostate cancer every day, I have witnessed firsthand the challenges of personalizing treatment. Our goal is to harness advances in artificial intelligence to extract clinically meaningful information from routine medical images, providing physicians with a clearer understanding of each patient’s cancer and enabling more individualized treatment decisions.”