Dynamic disease trajectories, not treatment timing, as a driver of outcomes in intrahepatic cholangiocarcinoma: A machine learning analysis.

Abstract

e16018 Background: Intrahepatic cholangiocarcinoma (iCCA) exhibits marked heterogeneity in outcomes that is incompletely explained by stage or treatment timing. Although treatment delay is frequently cited as a modifiable risk factor, its prognostic significance in real-world practice remains unclear. We applied machine learning (ML) to model iCCA as a dynamic disease process and to evaluate whether longitudinal disease trajectories better explain outcomes than treatment timing alone. Methods: We conducted a population-based retrospective analysis of adults diagnosed with iCCA using the Surveillance, Epidemiology, and End Results (SEER) database (2000–2022). Time from diagnosis to first cancer-directed therapy was categorized as ≤30 days, 31–60 days, or > 60 days. Associations between treatment delay, disease characteristics, and overall survival (OS) were assessed using multivariable logistic and Cox regression models. To capture real-world disease evolution, each patient’s clinical course was encoded as an ordered longitudinal sequence of diagnosis, treatment states (surgery, chemotherapy, radiation), and outcome. Unsupervised ML was used to identify latent disease-course trajectories. A supervised ML model using variables available at diagnosis was developed to predict membership in high-risk trajectories. Results: The cohort included 18,312 patients with iCCA. Treatment initiation beyond 30 days occurred in more than half of patients and was more common in localized than advanced disease. In adjusted survival analyses, shorter time to treatment was paradoxically associated with worse OS, consistent with confounding by disease severity rather than a protective effect of delay. Unsupervised ML identified four distinct and reproducible disease trajectories with markedly different outcomes. Median OS ranged from approximately 2 months in early-failure trajectories to approximately 15 months in favorable multimodality trajectories. Trajectory membership was not fully explained by stage or treatment timing. A supervised ML model predicted high-risk trajectory membership with good discrimination (AUC 0.82). Key predictors included tumor grade, age, tumor burden, and incomplete staging, whereas treatment timing had minimal influence. Conclusions: In iCCA, treatment delay reflects underlying disease biology and care prioritization rather than independently determining survival. ML-based trajectory modeling reveals clinically meaningful disease courses that better explain outcome heterogeneity than treatment timing or stage alone and enables early identification of patients at highest risk for unfavorable real-world outcomes.