Supplementary Figures S1 - S12 from Machine Learning Predicts Hepatocellular Carcinoma Risk from Routine Clinical Data: A Large Population-Based Multicentric Study

Résumé fourni par la source

Supplementary Figure S1 shows participant inclusion and exclusion flowcharts for UKB and AOU, detailing data processing and cohort assembly steps. Supplementary Figure S2 shows age distributions at first HCC diagnosis in UKB and AOU and associations between metabolic biomarkers and HCC risk in PAR. Supplementary Figure S3 shows additional model performance metrics in UKB, including ROC curves, AUC comparisons across estimators, benchmark risk scores, and prediction score distributions. Supplementary Figure S4 shows the impact of missing-data thresholds on cohort size, disease prevalence, and model performance in UKB. Supplementary Figure S5 shows comparative performance of clinical models and AFP in the UKB proteomics subcohort using ROC and PRC analyses. Supplementary Figure S6 shows feature importance rankings for Models C and E across cohorts, including top-ranked features and aggregated feature group contributions. Supplementary Figure S7 shows calibration performance of the TOP15 model in the UKB All cohort before and after Platt scaling. Supplementary Figure S8 shows calibration performance of the TOP15 model in the UKB PAR cohort before and after Platt scaling. Supplementary Figure S9 shows additional external validation metrics in AOU, including prediction score distributions and confusion matrices across thresholds and sex strata. Supplementary Figure S10 shows the relationship between recall and NNS across thresholds in UKB and AOU for All and PAR cohorts. Supplementary Figure S11 shows calibration performance of the TOP15 model in the AOU All cohort before and after Platt scaling. Supplementary Figure S12 shows calibration performance of the TOP 15 model in the AOU PAR cohort before and after Platt scaling.

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