Development and Validation of a Machine Learning-Based Model Used for Predicting Hepatocellular Carcinoma Risk in Patients with Hepatitis B-Related Cirrhosis: A Retrospective Study
Bibliographic record
Abstract
Object: Our objective was to estimate the 5-year cumulative risk of HCC in patients with HBC by utilizing an artificial neural network (ANN). Methods: We conducted this study with 1589 patients hospitalized at Beijing Ditan Hospital of Capital Medical University and People's Liberation Army Fifth Medical Center. The training cohort consisted of 913 subjects from Beijing Ditan Hospital of Capital Medical University, while the validation cohort comprised 676 subjects from People's Liberation Army Fifth Medical Center. Through univariate analysis, we identified factors that independently influenced the occurrence of HCC, which were then used to develop the ANN model. To evaluate the ANN model, we assessed its predictive accuracy, discriminative ability, and clinical net benefit using metrics such as the area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration curves. Results: In total, we included nine independent risk factors in the development of the ANN model. Remarkably, the AUC of the ANN model was 0.880, significantly outperforming the AUC values of other existing models including mPAGE-B (0.719) (95% CI 0.670-0.768), PAGE-B (0. 710) (95% CI 0.660-0.759), FIB-4 (0.693) (95% CI 0.640-0.745), and Toronto hepatoma risk index (THRI) (0.705) (95% CI 0.654-0.756) (p<0.001 for all). The ANN model effectively stratified patients into low, medium, and high-risk groups based on their 5-year In the training cohort, the positive predictive value (PPV) for low-risk patients was 26.2% (95% CI 25.0-27.4), and the negative predictive value (NPV) was 98.7% (95% CI 95.2-99.7). For high-risk patients, the PPV was 54.7% (95% CI 48.6-60.7), and the NPV was 91.6% (95% CI 89.4-93.4). These findings were validated in the independent validation cohort. Conclusion: The ANNs model has good individualized prediction performance and may be helpful to evaluate the probability of the 5-year risk of HCC in patients with HBC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".