Artificial Intelligence and Radiomics to Predict Hepatocellular Carcinoma Risk in Cirrhosis Patients
Subject: Artificial Intelligence and Radiomics to Predict Hepatocellular Carcinoma Risk in Cirrhosis PatientsSolicitation Number: 75N98026Q00779Notice Type: PRESOLNAICS: 611310Set-Aside: No Set aside usedNotice Published: 07-13-26Response Due: 07-23-26Agency: Department of Health and Human ServicesOffice: NATIONAL INSTITUTES OF HEALTHContact: LAW, JAMES B This email address is being protected from spambots. You need JavaScript enabled to view it.Office Address: BETHESDA, MD 20892Place of Performance: Bethesda, MD 20892
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The mission of the National Institute of Diabetes and Digestive and Kidney Diseases is to conduct and support medical research and research training and to disseminate science-based information on diabetes and other endocrine and metabolic diseases; digestive diseases, nutritional disorders, and obesity; and kidney, urologic, and hematologic diseases to improve people?s health and quality of life. The Liver Diseases Branch is conducting research using radiographic imaging and artificial intelligence to evaluate liver cancer risk in patients with advanced liver disease or cirrhosis. Funding for this effort was awarded through the Bench-to-Bedside and Back Award Program (Award Number 1517448). A portion of the awarded funding will be used to acquire a clinical patient cohort, associated radiographic imaging datasets, and de-identified radiology reports from the University of Pennsylvania. The research focuses on the use of imaging data and artificial intelligence to improve prediction of liver cancer risk, evaluate liver cancer recurrence following curative therapy, and support translational research related to liver disease and liver cancer. To support these research objectives, the University of Pennsylvania will provide a clinical cohort of patients who underwent liver transplantation for hepatocellular carcinoma, together with associated magnetic resonance imaging studies, radiology reports, and related clinical data necessary for research analysis and model development.