ArticleBMC medical informatics and decision making2026
Liver cancer risk stratification using deep learning on nationwide longitudinal health screening data: a retrospective cohort study.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundCurrent 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.
methodsWe 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.
resultsOur 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.
conclusionsApplying 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 registrationNot applicable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.