Machine learning model predicts liver cancer risk with high accuracy
Machine learning model predicts liver cancer risk with high accuracy
Publish Date: 2026-03-26 23:05:00
Source Domain: www.news-medical.net
Bottom line: A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results predicted a patient’s risk of hepatocellular carcinoma (HCC), the most common type of liver cancer, with high accuracy.
Journal in which the study was published: Cancer Discovery, a journal of the American Association for Cancer Research (AACR)
Author: Carolin Schneider, MD, is co-senior and corresponding author and an assistant professor at RWTH Aachen University in Germany. Schneider co-led the study with Jakob Kather, MD, MSc, a professor of clinical artificial intelligence at the Technical University of Dresden, Germany.
Jan Clusmann, MD, is the first author of the study and a clinician-scientist at the Technical University of Dresden.
Background: Individuals who are considered to have an elevated risk for HCC may be eligible for imaging-based and blood-based cancer screening to enable early detection of the disease; however, current guidelines focus on a narrow, high-risk population and miss many at-risk individuals, explained Schneider.
“Screening is typically recommended for patients with confirmed liver cirrhosis or severe liver disease, since many cases of HCC occur in these patients, but there are many individuals with undiagnosed cirrhosis or other risk factors who might also benefit from liver cancer screening,” she said.
Additional factors that increase a patient’s risk for developing HCC include being male, smoking, and heavy alcohol consumption, among others, added Clusmann.
“With so many factors impacting risk, there is an urgent need for effective tools to help clinicians identify high-risk patients,” he said. “Machine learning tools that can simultaneously work with different types of clinical data could be particularly useful for this major clinical challenge.”
How the study was conducted: In this study, Clusmann, Kather, Schneider, and colleagues used data from the UK Biobank to develop machine…
