AI model can accurately predict liver cancer risk, study suggests

• A machine learning model accurately predicted the risk of hepatocellular carcinoma (HCC) using routine clinical data.
• The model outperformed existing liver cancer risk tools by identifying more true cases while reducing false positives.
• The study suggests that adding complex data, such as genomics, did not improve performance, indicating that simple, widely available clinical data are sufficient for effective risk prediction.
• The tool could help clinicians detect at-risk individuals earlier, including those without diagnosed liver disease, potentially improving screening and patient outcomes if further validated.

Liver cancer is the sixth leading cause of cancer death in the United States. Hepatocellular carcinoma (HCC) is the most common type of liver cancer in adults, accounting for the majority of cases. It typically occurs in those with chronic liver disease resulting from hepatitis or cirrhosis.

It is not uncommon for people to receive a late-stage diagnosis of HCC. This is because it is usually asymptomatic in early stages. Current screening guidelines primarily focus on individuals with existing chronic liver disease.

However, roughly 20% of HCC cases may develop in those without any evidence of liver disease. Thus, these individuals are also at risk of a late diagnosis due to not meeting the criteria for surveillance.

Early diagnosis of HCC is essential, as many who receive a late diagnosis may not be suitable for current treatment options.

There is growing interest in the potential application of artificial intelligence (AI) for the early detection of HCC. Now, a new study, published in Cancer Discovery, suggests that a machine learning tool is capable of predicting HCC risk with high accuracy.