Work

Development of a novel routine blood-based AI model for hepatocellular carcinoma screening—a territory-wide study

Year: 2025

Type: article

Abstract: Background Current guidelines for hepatocellular carcinoma (HCC) screening in patients with chronic liver disease (CLD) recommend ultrasound and/or α-fetoprotein (AFP) testing every 6 months. However, both methods exhibit low sensitivity for early-stage HCC. Building on our previous study that identified a novel blood test signature for HCC, this study aims to develop a routine blood-based artificial intelligence (AI) model for HCC detection. Patients and methods Records from 2000 to 2018 were retrieved from Hospital Authority Data Collaboration Laboratory, a territory-wide database. CLD (hepatitis/cirrhosis) patients with and without HCC were identified with International Classification of Diseases codes, anti-viral drug history, virology test, and radiology reports. Decompensated cases were excluded. Blood records within 1 month before the diagnosis of HCC and CLD patients were retrieved. Data from 2000 to 2015 were split into training and testing cohorts in an 80 : 20 ratio, while data from 2016 to 2018 were used as the validation set. Machine learning models were applied and their performances were compared with AFP. Results The cohort yielded >75 000 patients including 19 670 patients with HCC. Results from the test set demonstrated promising capabilities in excluding CLD patients, achieving a sensitivity of 80% and a specificity of 81% (area under the receiver operating characteristic curve=0.894) at a cut-off value of 0.43. The AI model outperformed AFP in early-stage detection, maintaining higher sensitivity across all Barcelona Clinic Liver Cancer stages. Conclusions This novel AI model, which uses only standard blood test results, shows strong potential for improving the detection of HCC, significantly outperforming the traditional alpha-fetoprotein (AFP) test. Its straightforward design offers a practical and widely accessible screening option that could be integrated into existing healthcare pathways to aid earlier diagnosis.

Source: ESMO Gastrointestinal Oncology

DOI: 10.1016/j.esmogo.2025.100241