BACKGROUND AND AIMS
Cholangiocarcinoma (CCA) is an aggressive cancer with rising incidence and mortality worldwide. Chronic liver disease (CLD) is a well-recognized risk factor, but its influence on tumor presentation and clinical outcomes remains unclear. We aimed to compare the clinical course of CCA in patients with and without CLD.
METHODS
We retrospectively analyzed 3,743 patients diagnosed with CCA between 2010 and 2024 across international centers. CLD was defined by documented primary sclerosing cholangitis, cirrhosis, viral hepatitis, or other chronic liver disorders; remaining patients were classified as non-CLD. Demographic, clinical, biochemical, treatment, and survival features were compared.
RESULTS
Among the CCA cohort, 993 patients had CLD. Compared with non-CLD patients (n=2,750), those with CLD were more frequently male (67% vs. 53%) and younger (median age 63 vs. 66 years). CLD-CCA patients more often presented with intrahepatic tumors (64% vs. 42%), better performance status (ECOG 0: 53% vs. 35%), lower CA19.9 levels (56 vs. 135 U/mL), and earlier-stage disease (localized: 57% vs. 43%; metastatic: 23% vs. 31%). In propensity score-matched analyses, patients with prior CLD were diagnosed at earlier CCA stages than non-CLD controls. Consequently, curative-intent tumor surgery was performed more frequently in CLD patients (60% vs. 48%), resulting into longer median overall survival (mOS 12.2 vs. 11.1 months; HR 0.88, 95%CI 0.80-0.98) and higher 5-year survival (OR 1.70, 95%CI 1.37-2.11), particularly in intrahepatic CCA (mOS: 14.2 vs. 11.1 months; HR 0.77, 95%CI 0.68-0.87; 5-year survival OR 2.19, 95%CI 1.60-3.01). Treatment responses across modalities were comparable between groups.
CONCLUSION
Pre-existing CLD is associated with earlier-stage CCA diagnosis and improved survival, supporting the implementation of structured surveillance strategies in high-risk CLD populations.
IMPACT AND IMPLICATIONS
This international multicenter study show that pre-existing CLD is associated with earlier-stage CCA diagnosis, likely due to closer clinical surveillance, greater eligibility for curative-intent surgery, and improved survival. Treatment responses were similar regardless of CLD status. These findings support established surveillance in high-risk groups and highlight the need to optimize strategies for selected moderate-to-high risk CLD populations, alongside prospective evaluation of their clinical utility, cost-effectiveness, and potential refinement through more accurate non-invasive biomarkers.
L. Izquierdo-Sánchez, J. Narbaiza, Julen Martin-Robles et al.· Journal of Hepatology· 0 citations
Summary Background The poor prognosis of cholangiocarcinoma (CCA) is largely driven by rapid, asymptomatic disease progression, which usually results in a late diagnosis in the absence of established screening strategies. An early, cost-effective, and universally applicable risk assessment strategy would therefore be valuable. Methods We developed machine learning (ML) models on prospective, multimodal data from 487,495 UK Biobank (UKB) participants, of whom 649 developed CCA during follow-up. Data from England (80%) were utilised for ML development via five-fold cross-validation, and then all models were tested on withheld data from Scotland, Wales, and Newcastle (20%). Iterative ablation studies reduced inputs from >150 features across demographic data, lifestyle, health records, blood parameters, genomics, and metabolomics to models built on five and ten routinely available clinical parameters. These were externally validated in the Penn Medicine Biobank (PMBB; n = 2638; 28 CCA), All of Us Research Program (AOU; n = 330,433; 362 CCA), Japan Medical Data Centre Claims Database (JMDC; n = 8,425,522; 723 CCA) and TriNetX (n = 728,886; 1592 CCA). Findings We show that ML models integrating biliary-disease associated health records and Gamma glutamyltransferase can stratify risk of future CCA. Evaluation on the UKB test set as well as three independent cohorts revealed robust performance and generalisability across ethnicities. We achieved AUROCs of 0.71 [95% CI: 0.703–0.711], 0.77 [95% CI: 0.764–0.778 ], 0.796 [95% CI: 0.795–0.798] and 0.8 [95% CI: 0.794–0.805] for UKB, PMBB, AOU, and JMDC respectively, with respective AUPRCs of 0.014 [95% CI: 0.009–0.018], 0.042 [95% CI: 0.037–0.048], 0.038 [95% CI: 0.033–0.042] and 0.001 [95% CI: 0.001–0.001]. In AOU, application of the Youden J-optimised threshold yielded a number needed to screen of 79. Separate models for intra- and extrahepatic CCA did not improve performance. In line with the pathophysiology, performance declined for longer intervals between assessment and event. A group-level analysis in the TriNetX cohort revealed hazard ratios of up to 82.5 [95% CI: 26.4–257.96]. We provide extensive interpretability results and release all source codes used to develop the presented models. Interpretation We provide a comprehensive framework for early CCA risk stratification in the general population, identifying key predictors, and demonstrating the potential of data-driven models in personalised screening for hepatobiliary cancer. Funding German Cancer Aid (grant #70115730), Junior Principal Investigator Fellowship programme of RWTH Aachen Excellence strategy.
Felix van Haag, J. Clusmann, Paul-Henry Koop et al.· EBioMedicine· 0 citations
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