Background: Current liver cancer screening in Korea focuses on viral hepatitis or cirrhosis, despite rising risks from metabolic and alcohol-related liver disease. We aimed to develop a deep learning model that leverages routinely collected national screening and claims data to predict liver cancer risk without requiring additional diagnostic tests.
Methods: We conducted a retrospective cohort study of 3,962,209 adults aged 50-69 years who participated in the Korean National Health Screening program between 2010 and 2015, with follow-up until December 31, 2021. A total of 12,401 liver cancer cases were identified. Using data from three biennial screenings over 6 years, we developed a one-dimensional convolutional neural network model to predict 5-year liver cancer risk. The cohort was randomly divided at the patient level into training (80%) and testing (20%) sets. Predictors included demographic, clinical, behavioral, anthropometric, and laboratory features. Model performance was compared with logistic regression, extreme gradient boosting, multilayer perceptron, and current national surveillance criteria, assessed by the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Interpretability was examined using SHapley values and Cox regression, and sensitivity analyses evaluated the impact of screening timing.
Results: Our model achieved an AUROC of 0.810 (95% CI, 0.802-0.818) and an AUPRC of 0.029 (95% CI, 0.026-0.034), with a sensitivity of 0.736 (95% CI, 0.720-0.753), clearly outperforming the current national criteria which showed an AUROC of 0.552 (95% CI, 0.546-0.558), an AUPRC of 0.007 (95% CI, 0.006-0.008), and a sensitivity of only 0.112 (95% CI, 0.100-0.125). The top-risk quintile accounted for 65% of incident liver cancer cases and had a 27-fold higher hazard compared to the lowest-risk group. Major predictors included age, viral hepatitis, family history of liver cancer, cholesterol levels, alcohol consumption, and metabolic factors. Sensitivity analyses demonstrated that incorporating all three screening time points yielded the highest overall performance.
Conclusions: Applying a deep learning model to routinely collected national screening data improved liver cancer risk stratification and enabled early identification of high-risk individuals, including those without prior liver disease. This approach supports scalable, policy-relevant screening strategies within existing public health infrastructure.
Trial registration: Not applicable.
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