Digital and Biological Twins in Cholangiocarcinoma: From Translational Research to Precision Medicine—A Narrative Review

Background: Cholangiocarcinoma (CCA) is a highly heterogeneous malignancy with limited therapeutic options and poor prognosis. The increasing complexity of molecular stratification and treatment selection has stimulated interest in computational and biological modeling approaches for precision oncology. Objective. This narrative review aims to provide a comprehensive overview of digital twins (DTs), DT-enabling computational models, and biological twins (BTs) in CCA, discussing their applications, limitations, and potential integration within hybrid precision medicine frameworks. Methods: A narrative literature review was conducted. To inform the twin-focused sections, a structured PubMed search was performed using predefined keywords related to CCA and twin-related technologies, including organoids, xenografts, organ-on-chip systems. Particular attention was devoted to recent studies addressing computational modeling, patient-derived experimental systems, and translational applications. Results: DT development in CCA is supported by an ecosystem of DT-enabling technologies, including radiomics, artificial intelligence, multi-omics integration, and simulation-based models. However, fully realized medical DTs remain unavailable. BTs, including patient-derived organoids, xenografts, and microfluidic platforms, enable functional validation of therapeutic hypotheses but face challenges related to scalability, standardization, and clinical feasibility. Emerging hybrid DT-BT frameworks seek to combine computational prediction with biological validation through iterative feedback loops, potentially improving patient stratification and treatment personalization. Conclusions: DTs and BTs represent complementary components of an evolving precision oncology ecosystem in CCA. Although technical, biological, regulatory, and implementation challenges remain, the convergence of computational models, longitudinal molecular monitoring, and patient-derived systems may facilitate clinically actionable hybrid twin frameworks. Successful translation will require both technological innovation and healthcare-system improvements to precision medicine access.