Metabolic-dysfunction associated steatotic liver disease (MASLD) is a condition that can lead to serious liver problems like fibrosis, inflammation, cirrhosis, and liver cancer. Risk factors for MASLD include type 2 diabetes, metabolic syndrome, and genetic factors. It's the most common cause of chronic liver disease in the US and costs around $87 million annually. About 24%-30% of the US population is affected, and this number is expected to rise due to increasing rates of diabetes and obesity. MASLD can range from mild fatty liver to more severe conditions like metabolic dysfunction-associated steatohepatitis (MASH), which can lead to cirrhosis and liver cancer. One major concern is that ...

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Metabolic-dysfunction associated steatotic liver disease (MASLD) is a condition that can lead to serious liver problems like fibrosis, inflammation, cirrhosis, and liver cancer. Risk factors for MASLD include type 2 diabetes, metabolic syndrome, and genetic factors. It's the most common cause of chronic liver disease in the US and costs around $87 million annually. About 24%-30% of the US population is affected, and this number is expected to rise due to increasing rates of diabetes and obesity. MASLD can range from mild fatty liver to more severe conditions like metabolic dysfunction-associated steatohepatitis (MASH), which can lead to cirrhosis and liver cancer. One major concern is that some people with MASH may develop liver cancer without showing signs of cirrhosis or fibrosis, leading to late detection and poorer outcomes. Currently, there are no guidelines for monitoring MASLD patients who don't have cirrhosis. We aim to develop a method to identify high-risk MASLD patients who may develop liver cancer using a combination of imaging and genetic information. Our proposal is innovative because it focuses on developing a model to identify these high-risk patients, potentially leading to earlier detection and better outcomes. We believe that genetic factors play a significant role in MASLD and the development of liver cancer. By combining genetic markers found in blood with imaging features of the liver, we aim to create a tool that can identify MASLD patients at high risk of developing liver cancer. Our project will create a unique dataset of imaging and genetic information from MASLD patients in Texas, where obesity, MASLD, and liver cancer are prevalent. By identifying high-risk MASLD patients, we can potentially reduce the burden of MASLD-induced liver cancer and make monitoring efforts more cost-effective. We will focus on MASLD patients with an elevated risk of fibrosis to ensure the practical application of our model.

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