PMID: 40231168 https://pubmed.ncbi.nlm.nih.gov/40231168/

Abstract

BACKGROUND: Chronic hepatitis B (CHB) infection is the major risk factor for hepatocellular carcinoma (HCC).

OBJECTIVE: To develop machine-learning models for predicting an individual risk of HCC development in CHB-infected patients.

METHODS: Machine learning models were constructed using features from follow-up visits of CHB patients to predict the diagnosis of HCC development within 6 months after each index follow-up. We developed 4 model variants using all features, with alpha fetoprotein (AFP) ( ) and without AFP ( ); and selected features, with AFP ( ) and without AFP ( ). Performance was evaluated using 10-fold cross-validation on a derivation cohort and further validated on an independent cohort.

RESULTS: In the derivation cohort of 2,382 patients, of whom 117 developed HCC, achieved higher sensitivity (0.634, 95% confidence interval [CI]: 0.559-0.708) and specificity (0.836; 0.830-0.842) than (sensitivity 0.553; 0.476-0.630 and specificity 0.786; 0.779-0.792). also achieved higher sensitivity (0.683; 0.611-0.755 vs. 0.658; 0.585-0.732) and specificity (0.756; 0.749-0.763 vs. 0.744; 0.737-0.751) than . Performance of and were tested in another cohort of 162 patients in which 57 patients developed HCC. achieved sensitivity and specificity of 0.634 (0.522-0.746) and 0.657 (0.615-0.699), while sensitivity and specificity of were 0.690 (0.583-0.798) and 0.651 (0.609-0.693), respectively.

CONCLUSION: The machine learning models demonstrate good performance for predicting short-term risk for HCC development and may potentially be used for tailoring surveillance interval for CHB patients.