Digital and Biological Twins in Cholangiocarcinoma: From Translational Research to Precision Medicine

1. Introduction

Cholangiocarcinoma (CCA) is a heterogeneous group of malignancies originating from the biliary tract epithelium and is the second most common primary liver cancer after hepatocellular carcinoma (HCC). The incidence and mortality of CCA, especially intrahepatic cholangiocarcinoma (iCCA), have risen worldwide over the past two decades, reflecting complex environmental, infectious, and metabolic risk factors that remain incompletely understood (Valle et al., 2021). Notably, epidemiologic data may underestimate the true burden of disease due to coding and classification ambiguities within the International Classification of Diseases (ICD) system.

The clinical presentation of CCA is often insidious, with late-stage diagnosis limiting curative options. The heterogeneity of CCA is not limited to anatomical location, since iCCA, perihilar (pCCA), and distal (dCCA) subtypes show distinct biological and clinical behaviors. Underlying this diversity are complex molecular profiles and multiple cells of origin, including hepatic progenitor cells, hepatocytes, and peribiliary glands (PBGs), whose role in disease initiation and progression is increasingly recognized (Guest et al., 2025). Despite emerging molecular targets such as IDH1/2 mutations and FGFR2 fusions, the clinical application of targeted therapies remains limited by tumor heterogeneity and the lack of robust predictive biomarkers (Ellis et al., 2025; Tesini et al., 2025).

In this context, the integration of advanced computational models (digital twins) with patient-derived biological systems (biological twins) has been proposed as a transformative approach to personalize patient care. Digital twins (DTs) are in silico frameworks representing dynamic and virtual replicas of patients that integrate multi-omic, imaging, clinical, and environmental data to simulate disease progression and predict therapeutic response (Sadée et al., 2025). Biological twins (BTs), including patient-derived organoids (PDOs) and xenografts (PDXs), provide ex vivo platforms to experimentally validate treatment hypotheses. The synergy of DT and BT promises to accelerate precision medicine by bridging the gap between in silico prediction and biological reality.

Despite extensive literature exploring the application of DTs and BTs in oncology and translational medicine (Olawade et al., 2026; Giansanti and Morelli, 2025; Ștefănigă et al., 2024), a disease-specific synthesis within the context of cholangiocarcinoma is still missing.

Figure 1. Digital and Biological Twins of Cholangiocarcinoma.

Figure 1. Digital and Biological Twins of Cholangiocarcinoma.

A recent perspective by Miedel et al. (2025) proposed the concept of “integrated patient digital and biomimetic twins” for metabolic dysfunction-associated steatotic liver disease (MASLD), demonstrating the growing interest in twin-based approaches for liver diseases. However, no review has yet extended this paradigm to CCA, a disease whose molecular heterogeneity and limited therapeutic landscape make it particularly suited for such an integrative framework. This narrative review aims to fill this gap by synthesizing the current knowledge on CCA’s clinical and molecular landscape, framing the existing evidence and advances in the integration of digital and BTs for translational and clinical research on CCA, and exploring the future challenges and opportunities of this rapidly evolving field.

2. Cholangiocarcinoma: Clinical and Molecular Complexity

CCA is anatomically classified into intrahepatic (iCCA), perihilar (pCCA), and distal (dCCA) subtypes, each arising from distinct biliary segments and associated with unique pathophysiological and molecular characteristics (Valle et al., 2021). The etiology of CCA is multifactorial, involving chronic biliary inflammation like primary sclerosing cholangitis, liver fluke infection, viral hepatitis, metabolic disorders, and environmental carcinogens. PSC is particularly associated with a high risk of CCA development, with disease progression characterized by epithelial injury, fibrosis, and peribiliary gland hyperplasia (Bowlus et al., 2023).

Histologically, iCCA has been reclassified into small and large bile duct subtypes based on tumor morphology, cell of origin, and molecular alterations. The large bile duct subtype shares features with pCCA and dCCA, typically exhibiting mucinous, gland-forming adenocarcinoma and often linked to chronic cholangitis, hepatolithiasis, or liver fluke infection. Conversely, the small bile duct subtype often presents as mass-forming tumors, frequently associated with chronic viral hepatitis and metabolic syndrome, and shows distinct molecular profiles including IDH1/2 mutations and FGFR2 fusions (Ellis et al., 2025; Bekaii-Saab et al., 2021).

Peribiliary glands (PBGs), epithelial structures lining large bile ducts, are now understood to harbor biliary tree stem/progenitor cells (BTSCs) capable of regeneration and repair (Cardinale et al., 2024). In PSC, PBG hyperplasia and mucinous metaplasia correlate with fibrosis and disease severity, driven by hedgehog signaling pathways that promote epithelial-to-mesenchymal transition (Carpino et al., 2019; Carpino et al., 2015). The field cancerization concept in PSC postulates that widespread epithelial alterations predispose to multifocal carcinogenesis within the biliary tree, paralleling mechanisms observed in inflammatory bowel disease-associated colorectal cancer. Recent morphomolecular analyses have further clarified that CCA may arise from multiple cells of origin — including hepatocytes, hepatic progenitor cells, and PBG-resident BTSCs — with each giving rise to tumors with distinct histological and molecular features (Guest et al., 2025).

At the molecular level, CCA harbors a diverse spectrum of genetic alterations, including single nucleotide variants (SNVs), copy number alterations, and chromosomal rearrangements. IDH1/2 mutations and FGFR2 fusions are most prevalent in iCCA and represent actionable targets with approved inhibitors (Ellis et al., 2025; Bowlus et al., 2023). In contrast, p/dCCA and gallbladder carcinoma rarely harbor these mutations but may exhibit HER2 amplification or overexpression, offering alternative therapeutic avenues (Tesini et al., 2025).

Tumor heterogeneity extends beyond genetics to include the tumor microenvironment, immune infiltrate, and epigenetic landscape, which collectively influence tumor progression and therapeutic response. Immune checkpoint molecules such as PD-L1 are variably expressed across CCA subtypes, with implications for immunotherapy efficacy. Tumor mutational burden (TMB) and microsatellite instability (MSI) define small subsets of patients who may benefit from immune checkpoint blockade (Kelley et al., 2023; NCCN, 2025).

3. The Clinical Imperative for Precision Medicine

Outcomes for CCA patients remain poor, with 5-year survival rates below 20% in most series (Valle et al., 2021). The clinical management of CCA is challenged by late diagnosis, anatomical complexity, and limited systemic options. Surgical resection offers potential cure but is feasible in a minority of patients; liver transplantation remains controversial and is generally restricted to highly selected cases (Bowlus et al., 2023).

The first-line treatment landscape has been transformed by the TOPAZ-1 trial, which demonstrated that durvalumab plus gemcitabine-cisplatin significantly improved overall survival compared with chemotherapy alone (HR 0.76; 95% CI 0.64–0.91), with a 36-month OS rate of 14.6% vs. 6.9% (Oh et al., 2025; Oh et al., 2024). Similarly, the KEYNOTE-966 trial showed that pembrolizumab plus gemcitabine-cisplatin improved OS (HR 0.83; 95% CI 0.72–0.95) (Kelley et al., 2023). These regimens now constitute the standard of care for advanced biliary tract cancer (NCCN, 2025).

Molecular profiling has opened new therapeutic opportunities but also revealed significant inter- and intra-tumoral heterogeneity, complicating treatment selection. Clinical trials with targeted agents against IDH1 (ivosidenib), FGFR2 (pemigatinib, futibatinib), BRAF V600E (dabrafenib/trametinib), and HER2 (zanidatamab, trastuzumab deruxtecan) have shown encouraging results in molecularly selected subgroups (Ellis et al., 2025; Tesini et al., 2025; NCCN, 2025). However, the ANITA study — the largest real-world Italian dataset on molecular profiling in CCA (621 patients from 10 centers) — demonstrated that while extended molecular profiling was performed in 79.9% of patients and targeted therapies significantly improved OS (HR 0.49; 95% CI 0.28–0.86), only 18.7% of patients with ESCAT I–III alterations actually received matched therapy (Genovesi et al., 2026). This gap between molecular profiling availability and therapeutic access underscores the urgent need for strategies that accelerate and optimize treatment selection.

The Rome Trial further demonstrated the feasibility of integrating comprehensive molecular profiling within multidisciplinary tumor boards: among 303 patients discussed at the molecular tumor board, 71% received a specific therapeutic or diagnostic indication from extended NGS profiling (Cardinale et al., 2022, ASCO abstract 3087).

Radiologic and histologic biomarkers also contribute to risk stratification and treatment planning. Radiomic analyses can noninvasively predict genetic subtypes and tumor aggressiveness, supporting their incorporation into clinical workflows (Xu et al., 2025; Alidina et al., 2026). Histological subtype classification, perineural invasion, and microenvironmental features are increasingly recognized as prognostic indicators.

4. Digital and Biological Twins: Concepts and Integration

The concept of a DT originates from engineering, with early precursors traceable to NASA’s Apollo program in the 1960s, where physical replicas of spacecraft were used for ground-based simulation (Dihan et al., 2024). The term “digital twin” was subsequently formalized in the manufacturing domain, where a virtual replica of a physical system enables simulation and optimization of products and processes (Segovia and Garcia-Alfaro, 2022; Jiang et al., 2021). In medicine, DTs represent an emerging technology where comprehensive, multidimensional patient data — such as genomics, imaging, clinical records, or environmental exposures — are integrated into dynamic computational models that simulate disease trajectories and predict treatment responses. A recent Lancet Digital Health paper by Sadée et al. (2025) proposed five key components of the medical DT: the patient, data connection, patient-in-silico, interface, and twin synchronization, providing a useful conceptual framework for clinical implementation.

BTs complement this framework by providing patient-specific in vitro/ex vivo systems, including organ-on-chips (OoC), microphysiological systems (MPS), patient-derived organoids (PDOs) and xenografts (PDXs), and primary cell lines, which recapitulate tumor biology and drug sensitivity. BTs enable experimental validation of hypotheses generated by DT simulations, creating a feedback loop that enhances model fidelity and predictive accuracy (Miedel et al., 2025).

The integration of digital and BTs embodies the principles of value-based healthcare (VBH) and the “5P” medicine framework — predictive, preventive, personalized, participatory, and precision — which collectively aim to shift healthcare from reactive, population-based approaches toward proactive, individualized strategies [ref]. This approach allows for individualized treatment design, early diagnosis, and dynamic monitoring of disease evolution.

Emerging translational infrastructures, such as CHOLANGIO-PLATFORM, exemplify this paradigm by leveraging multi-source and multidisciplinary data streams, including clinical, molecular, imaging, and pathological data, to iteratively develop and refine integrated DT and BT models in CCA. In parallel, clinical initiatives such as the Rome Trial have demonstrated that the integration of comprehensive molecular profiling within multidisciplinary tumor boards can significantly improve patient outcomes (Cardinale et al., 2022, ASCO abstract 3087).

A comprehensive technical description of digital and BT models, including their theoretical foundations and implementation frameworks, lies beyond the scope of this review and has been extensively discussed elsewhere (Olawade et al., 2026; Giansanti and Morelli, 2025; Ștefănigă et al., 2024; Sadée et al., 2025; Miedel et al., 2025). Here, we focus instead on their translational applications, emphasizing how these complementary systems can contribute to improving and personalizing therapeutic strategies in CCA.

5. Digital Twins in Cholangiocarcinoma: From Predictive Modeling to Actionable Simulations

5.1. Current Landscape and Quantitative Benchmarks

The development of DTs in CCA is rooted in an extensive and rapidly expanding ecosystem of predictive models that are progressively capturing the multidimensional complexity of the disease. Although these approaches do not yet fully embody dynamic, patient-specific digital replicas, they represent a crucial phase in which the field is establishing the quantitative, computational, and data integration frameworks necessary for future DT implementation.

Radiomics and machine learning models have emerged as central pillars of this effort. Two recent systematic reviews and meta-analyses provide quantitative benchmarks for the field. Xu et al. (2025) analyzed 58 studies encompassing 12,903 patients and reported that combined radiomic-clinical models achieved pooled C-indices of 0.85–0.91 for iCCA detection, with deep learning-based models reaching a C-index of 0.924. Alidina et al. (2026) pooled 20 studies (8,746 participants) and reported a pooled sensitivity of 0.77 (95% CI 0.69–0.84) and specificity of 0.88 (95% CI 0.78–0.94) for differentiating iCCA from non-iCCA hepatic lesions, with CT-based models showing the highest diagnostic performance. These benchmarks demonstrate that imaging-based AI models have reached clinically meaningful accuracy, though both meta-analyses highlighted concerns regarding retrospective design, small cohorts, and limited external validation.

Beyond diagnosis, a substantial body of literature has focused on preoperative characterization of tumor aggressiveness. Multiple independent models have successfully predicted microvascular invasion, perineural invasion, and lymph node metastasis using imaging and clinical data (Miao, 2025; Liu, 2025). These capabilities directly inform surgical decision-making and patient selection, highlighting the immediate translational value of these approaches.

Prognostic modeling represents another highly developed area, with numerous studies integrating radiomics, clinicopathological features, and increasingly multi-omics data to predict overall survival, recurrence-free survival, and early recurrence (Deng, 2024;; Zhang, 2025; Fu, 2025; Liu, 2025; Wang, 2025). Notably, several models have moved toward explainability and clinical usability through nomograms and interpretable machine learning frameworks. A 2025 ASCO abstract described a transformer-based model integrating MRI, pathology, and proteomics for iCCA prognosis prediction, achieving an AUC of 0.867 in external validation — an example of the kind of multimodal integration that approaches DT functionality (ASCO 2025, abstract e16328).

The integration of molecular data is further strengthening these predictive systems. Multi-omics-based models incorporating genomic, transcriptomic, and epigenetic features have demonstrated improved prognostic and therapeutic prediction performance (Jia, 2025; Yu, 2025; Shen, 2024; Fu, 2024; Meng, 2025; Cao, 2025). In parallel, studies exploring metabolomics and lipidomics or specific biological signatures such as apoptosis-related genes and immune-related markers (Shen, 2024; Yang, 2024) are expanding the biological depth of these models.

In parallel, large-scale integrative efforts based on registry data and multi-institutional cohorts are contributing to a more standardized and generalizable understanding of disease behavior (Carpino, 2026). These initiatives provide the data diversity, volume, and quality required for robust model training, external validation, and cross-population applicability — essential prerequisites for the development of clinically meaningful digital twins in CCA.

5.2. Toward Functional Digital Twins: Simulation and Mechanistic Modeling

While predictive modeling constitutes the dominant paradigm, a subset of studies is beginning to introduce functional elements that move closer to the core concept of DTs as simulation platforms. These approaches represent an important transition from descriptive analytics to intervention-aware modeling.

One notable example is the development of biophysical simulation frameworks that model treatment delivery and effects. The work by Lu (2024) demonstrates how computational modeling can simulate nanoparticle distribution and temperature dynamics in magnetic hyperthermia therapy, effectively enabling the optimization of treatment parameters within a patient-specific anatomical context. This represents a clear step toward in silico experimentation, a defining feature of DTs.

Similarly, pharmacokinetic (PK) and pharmacodynamic (PD) modeling approaches are beginning to capture inter-patient variability in drug exposure and response. The population PK/PD model developed by Saeheng (2024) illustrates how dosing strategies can be optimized based on patient-specific characteristics, providing a quantitative framework for personalized therapy planning. These models introduce a temporal and mechanistic dimension that goes beyond static prediction, aligning more closely with the dynamic nature of DTs.

Beyond purely radiological or clinicopathological variables, several recent approaches integrate immune-related signatures and tumor microenvironment characteristics, enabling the prediction of immunotherapy response and the reconstruction of the tumor immune landscape (Chen, 2024; Cao, 2025). These developments reflect a critical shift from descriptive modeling toward biologically grounded representations of disease.

5.3. Digital Twins as an Evolving Continuum

Despite the rapid expansion of computational modeling and data integration approaches, only a few studies to date in CCA fully satisfy the formal definition of a digital twin as a dynamically updated, patient-specific system capable of simulating therapeutic interventions in real time. Rather, the current landscape is characterized by a constellation of high-resolution predictive and mechanistic models that collectively approximate, but do not yet constitute, true digital twins.

However, this landscape should be viewed not as a field limited by incomplete implementations, but as a continuum of innovation that is steadily progressing toward increasingly sophisticated and clinically relevant systems. Predictive models already deliver tangible clinical value in diagnosis, risk stratification, and prognostication, as demonstrated across a wide range of studies and confirmed by meta-analytic benchmarks (Xu et al., 2025; Alidina et al., 2026). The emergence of simulation-based and mechanistic models indicates that the field is beginning to incorporate the dynamic and interventional capabilities that define true DTs. Even in their current form, DT-like models could influence clinical decision-making and improve patient stratification. As they continue to incorporate simulation, longitudinal data, and biological complexity, they are likely to evolve into powerful tools capable of guiding personalized therapy in real time.

In this context, DTs in CCA should be understood not as a distant objective, but as an emerging reality, progressively taking shape through the convergence of predictive modeling, data integration, and computational innovation.

6. Biological Twins in Cholangiocarcinoma: Enabling Functional Precision Oncology

6.1. Conceptual Framework and Translational Relevance

The concept of BTs in CCA has emerged as a critical complement to molecular profiling and computational modeling, providing experimental systems that recapitulate patient-specific tumor biology. These platforms enable direct, functional interrogation of therapeutic hypotheses and represent a necessary bridge between predictive insights and clinical actionability. While genomic and transcriptomic analyses identify potential vulnerabilities, BTs allow researchers to test whether these vulnerabilities translate into effective treatment strategies in a physiologically relevant context.

Recent literature has increasingly framed these models as central components of the translational pipeline. Krendl et al. (2025) emphasize their indispensable role in preclinical drug discovery, while Lederer et al. (2025) highlight how patient-derived organoids can capture complex biological interactions, including those involving the microbiota. Montagner et al. (2025) further position three-dimensional systems as evolving platforms that progressively approximate in vivo tumor complexity. Two recent comprehensive reviews on biliary organoids — by Peng et al. (2025) and Chaudari et al. (2025) — provide detailed overviews of construction methods (matrix-independent, matrix-dependent, and tissue engineering-based strategies), signaling pathways driving biliary differentiation, and emerging clinical applications. Collectively, these perspectives underscore a shift in how biological models are conceptualized: from static representations of disease to dynamic systems capable of informing therapeutic decision-making.

6.2. Patient-Derived Xenografts: In Vivo Fidelity and Therapeutic Exploration

PDX remain the most established and biologically faithful form of BTs in CCA. By preserving tumor architecture and, to some extent, intra-tumoral heterogeneity, PDX models provide a robust platform for in vivo evaluation of therapeutic strategies. Their use has expanded significantly in recent years, moving beyond descriptive studies toward more functionally oriented applications.

A substantial body of work demonstrates the utility of PDX models in addressing therapeutic resistance, a major clinical challenge in CCA. Several studies have identified strategies capable of restoring sensitivity to standard chemotherapy, such as the use of doxycycline to reverse gemcitabine resistance (Massa, 2025) or curcumin-based approaches targeting metabolic dependencies (Thongpon, 2024). Similarly, inhibition of drug efflux mechanisms, including MRP3, has been shown to enhance the efficacy of cytotoxic agents (Asensio, 2024). These findings illustrate how BTs can uncover actionable strategies that are not readily predictable from molecular data alone.

PDX models have also been instrumental in evaluating targeted therapies and combinatorial approaches. Inhibition of FGFR signaling has revealed metabolic vulnerabilities ,while targeting CD73 has been shown to enhance responses to immune checkpoint blockade (Sun, 2024). More complex therapeutic regimens, such as the integration of PARG inhibitors with chemotherapy and immunotherapy, further highlight the capacity of these systems to model clinically relevant treatment strategies (Yu, 2025). In line with these applications, additional studies have further demonstrated the utility of PDX models for the in vivo assessment of therapeutic candidates and treatment responses (Dokduang, 2025). In addition, the testing of repurposed drugs and novel compounds, including ceritinib, cannabidiol, magnolol, and ferroptosis-inducing agents, demonstrates the versatility of PDX platforms in expanding the therapeutic landscape.

Beyond drug efficacy, PDX models have been used to explore innovative delivery strategies, such as ultrasound-mediated drug penetration (Hong, 2025) and mitochondria-targeted nanocarriers (Duan, 2024), thereby extending their relevance to pharmacological optimization. However, a key limitation of traditional PDX systems remains the lack of a functional immune compartment. Recent efforts to address this gap, including orthotopic implantation and the development of platforms for tumor-infiltrating lymphocyte (TIL) therapies (Wittling, 2025), are beginning to enhance their applicability in immuno-oncology. Studies targeting immune checkpoints such as TIGIT (Tang, 2025), as well as combinatorial approaches involving PD-1 blockade in combination with chemotherapy and targeted agents (Yu, 2025; Sun, 2024), reflect a growing interest in leveraging BTs to better understand and predict immunotherapeutic responses.

6.3. Organoids and Ex Vivo Systems: Scalability, Clinical Adaptability, and Practical Limitations

While PDX models offer high biological fidelity, their limited scalability and long turnaround times constrain their direct clinical applicability. PDOs have therefore emerged as a complementary platform, offering a more rapid and flexible system for drug testing. PDOs retain key molecular and phenotypic characteristics of the original tumor while enabling parallelized screening of multiple therapeutic options within clinically relevant timeframes (Hu et al., 2025; Peng et al., 2025).

Recent studies have demonstrated the potential of organoids to identify actionable signaling pathways, such as the IL-6/JAK/STAT3 axis (Boden, 2025), and to support drug discovery across complex disease contexts, including combined hepatocellular-cholangiocarcinoma (Gao, 2024). Importantly, the field is moving beyond simple epithelial models toward more comprehensive systems that incorporate elements of the tumor microenvironment. Co-culture approaches integrating tumor-associated macrophages exemplify this shift, enabling the study of immune-tumor interactions that are critical for therapeutic response (Guo, 2024). In parallel, emerging evidence suggests that PDOs can also be leveraged to investigate interactions between tumor cells and microbiota, further expanding their biological relevance (Lederer, 2025).

However, a critical and often underappreciated challenge is the variable success rate of tumor-enriched organoid establishment. While some series report success rates exceeding 70% for hepatobiliary and pancreatic cancers (Hu et al., 2025), a 2024 ASCO abstract from Memorial Sloan Kettering reported a tumor-enriched organoid success rate of only 14.6% (11/75) for CCA specifically, with a median time to tumor enrichment of approximately 16 weeks (Harding et al., 2024, ASCO abstract 533). This discrepancy likely reflects the difficulty of maintaining tumor cell dominance over normal cholangiocyte outgrowth in organoid culture, a challenge that is particularly pronounced in CCA given its desmoplastic stroma and relatively lower mutational burden compared with other gastrointestinal malignancies. The prolonged timeline and low success rate raise important questions about the feasibility of organoid-based personalized medicine for CCA in its current form, and underscore the need for optimized culture protocols and enrichment strategies.

These developments significantly enhance the translational potential of organoid-based BTs, positioning them as promising tools for real-time therapeutic stratification. However, challenges related to standardization, reproducibility, and the faithful representation of tumor heterogeneity remain areas of active investigation (Montagner, 2025; Jacobs et al., 2025).

6.4. Microfluidic and Organ-on-Chip Platforms: Toward Integrated Physiological Modeling

Microfluidic and organ-on-chip technologies represent a further evolution of the BT paradigm, aiming to recreate tissue architecture and physiological dynamics within controlled experimental environments. These systems enable a more integrated assessment of therapeutic effects by incorporating factors such as fluid flow, spatial organization, and multi-tissue interactions.

Foundational work by Du et al. (2020) established the bile duct-on-a-chip, which phenocopied the tubular architecture of the bile duct in three dimensions, demonstrated organ-level barrier functions including tight junction formation and mechanosensitivity, and enabled proof-of-concept toxicity studies with biliary toxins. This platform provided the first biliary-specific microfluidic system and laid the groundwork for subsequent CCA-focused applications.

In CCA specifically, organ-on-chip platforms have been developed to support personalized drug testing, allowing for the evaluation of treatment responses in a three-dimensional, dynamically regulated context (Polidoro, 2024). A very recent advance by Xie et al. (2026) describes a hypoxic microfluidic organoid-on-a-chip system that incorporates patient-derived CCA organoids within a controlled hypoxic microenvironment (O₂ 2.5%), faithfully recapitulating the hypoxic tumor microenvironment that drives drug resistance. This platform demonstrated that hypoxia-activatable nanodrugs could effectively reverse hypoxia-induced gemcitabine resistance, illustrating the potential of microfluidic systems to model clinically relevant resistance mechanisms and evaluate novel therapeutic strategies.

More advanced multi-organ systems extend this capability by enabling simultaneous assessment of drug efficacy and systemic toxicity, including effects on liver and kidney function (Liu, 2025). This dual evaluation is particularly relevant in oncology, where the therapeutic window is often narrow and patient-specific.

By bridging the gap between reductionist in vitro models and complex in vivo systems, microfluidic platforms offer a promising avenue for enhancing the predictive accuracy of BTs. Their integration into translational workflows may facilitate more informed therapeutic decision-making, particularly when combined with other modeling approaches.

6.5. Complementary In Vitro Models and Emerging Applications

At a more reductionist level, patient-derived cell lines and engineered resistance models continue to play an essential role in the BT ecosystem. The establishment of novel CCA cell lines provides reproducible platforms for mechanistic studies and high-throughput drug screening (Xu, 2024; Bai, 2024; Dou, 2025), while resistant models enable systematic exploration of therapeutic vulnerabilities (Delgado-Calvo, 2025). Although these systems lack the structural and microenvironmental complexity of higher-order models, they serve as critical building blocks within multi-scale experimental pipelines.

Emerging technologies are further expanding the scope of BTs. Biosensor-based approaches, for example, have demonstrated the potential to monitor molecular markers such as miR-29a, opening new possibilities for dynamic assessment of tumor behavior and treatment response (Hao, 2025). While still in early stages, such innovations suggest a future in which BTs may evolve from static testing platforms into adaptive systems capable of providing continuous functional feedback.

6.6. Outlook: Integrating Biological Twins Into Precision Oncology Workflows

Overall, BTs in CCA are transitioning from predominantly exploratory tools to increasingly functional platforms with direct translational relevance. Their primary strength lies in enabling experimental validation of patient-specific therapeutic strategies, thereby addressing a key limitation of purely data-driven approaches. By allowing the testing of drug combinations, the investigation of resistance mechanisms, and the evaluation of toxicity, these models provide a level of insight that is difficult to achieve through computational methods alone (Krendl et al., 2025).

Despite these advances, significant challenges remain, including issues related to scalability, standardization, and integration into clinical practice. PDX models are constrained by time and cost, while organoids face variable success rates and require further validation to ensure robustness and reproducibility (Harding et al., 2024, ASCO abstract 533). Microfluidic systems, though promising, remain largely in the proof-of-concept stage. Nevertheless, the convergence of these platforms into integrated, multi-scale pipelines, potentially in combination with digital twins, points toward a future in which biological and computational models operate synergistically.

In this context, BTs should be regarded not as standalone tools but as essential components of a broader precision oncology ecosystem. Their continued development and integration will be crucial for translating molecular and computational insights into actionable therapeutic strategies, ultimately improving outcomes for patients with CCA.

7. Hybrid Approaches in Cholangiocarcinoma: Converging Digital and Biological Twins Toward Actionable Precision Oncology

7.1. Bridging Prediction and Experimentation: The Emergence of Hybrid Twin Frameworks

While DTs and BTs have largely evolved along parallel trajectories, an increasing number of studies in CCA are beginning to converge these domains into integrated, hybrid frameworks. These approaches represent a critical conceptual and translational inflection point: rather than relying solely on predictive modeling or experimental validation, they establish iterative loops in which computational insights inform biological testing, and experimental results refine computational models.

This convergence is particularly significant in the context of CCA, a disease characterized by marked inter-patient heterogeneity and limited therapeutic options. In such a setting, purely predictive models risk remaining associative, while biological systems alone may lack scalability and timeliness. Hybrid approaches address these limitations by combining the breadth of data-driven inference with the depth of functional validation, thereby moving closer to the operational definition of actionable precision oncology. The concept closely parallels the “integrated patient digital and biomimetic twins” framework recently proposed by Miedel et al. (2025) for MASLD, in which patient digital twins (computational models built from clinomics and multi-omics data) are iteratively coupled with patient biomimetic twins (patient-derived organoids or iPSC-derived organ models) to test predictions experimentally.

Several recent studies exemplify this emerging paradigm in CCA. Integrative frameworks that combine multi-omics profiling with patient-derived models are increasingly used to prioritize therapeutic targets and validate them experimentally. For instance, works integrating genomic and transcriptomic analyses with organoid or xenograft systems demonstrate how computational identification of signaling dependencies can be translated into functional drug testing, effectively closing the loop between prediction and intervention. In this context, studies such as those by Mun (2025) and Ji (2025) illustrate how computational stratification of patients can guide downstream biological experimentation, enabling a more efficient and hypothesis-driven use of preclinical models.

7.2. Data-Informed Biological Modeling: Guiding Experimental Design Through Computational Insights

A first layer of integration is represented by the use of digital models to inform the design and prioritization of biological experiments. In this configuration, machine learning, radiomics, and multi-omics analyses are employed to identify high-risk patient subsets, predict therapeutic vulnerabilities, or infer resistance mechanisms, which are then interrogated in biological systems such as PDOs or PDX models.

For example, multi-omics-based stratification approaches (Jia, 2025; Yu, 2025; Cao, 2025), supported by broader integrative efforts (Gao, 2023), not only improve prognostic accuracy but also generate biologically meaningful hypotheses regarding pathway activation and therapeutic sensitivity. When coupled with patient-derived models, as shown in integrative functional studies (Cho, 2023), these insights enable targeted drug testing in systems that retain patient-specific characteristics. Similarly, computational analyses of resistance mechanisms, such as those described for FGFR-targeted therapies (Goyal, 2025) and supported by mechanistic investigations (Kendre, 2023; Bei, 2023), can be functionally validated in PDX or PDO platforms, allowing researchers to explore alternative treatment strategies in a controlled setting (Ellis et al., 2025).

Radiomics-based models also contribute to this integration by providing non-invasive, spatially resolved information that can guide tissue sampling and model generation. For instance, imaging-derived predictions of tumor aggressiveness or microenvironmental features (Miao, 2025; Guo, 2025; Qi, 2025) may inform the selection of representative tumor regions for organoid derivation or xenograft implantation, thereby enhancing the fidelity and relevance of BTs.

In this sense, DTs, or, more precisely, DT precursors, act as “hypothesis generators”, narrowing the experimental search space and increasing the efficiency of biological validation. This data-informed approach is particularly valuable in CCA, where limited tissue availability and model generation constraints necessitate strategic prioritization.

7.3. Biology-Informed Computational Refinement: Closing the Loop

The integration of biological and digital twins is not unidirectional. A second, equally important layer involves the use of experimental data to refine and enhance computational models. BTs provide high-resolution functional readouts of drug response, resistance evolution, and microenvironmental interactions, which can be fed back into digital frameworks to improve their predictive accuracy and mechanistic grounding.

For example, drug response data generated from PDO or PDX platforms can be used to train or recalibrate machine learning models, transforming them from purely correlative predictors into systems that incorporate functional evidence. Similarly, observations of resistance mechanisms emerging in biological systems, such as adaptive pathway activation or metabolic reprogramming, can inform the development of more sophisticated computational models capable of simulating dynamic treatment responses.

Microfluidic and organ-on-chip systems further enhance this feedback loop by providing temporally resolved data on drug efficacy and toxicity under physiologically relevant conditions (Polidoro, 2024; Liu, 2025; Du et al., 2020; Xie et al., 2026). These platforms enable the generation of rich, multidimensional datasets that capture not only endpoint responses but also dynamic processes, such as drug penetration, cellular adaptation, and inter-tissue interactions. When integrated into computational pipelines, such data can support the development of truly dynamic digital twins capable of simulating treatment trajectories over time.

This iterative exchange between digital and biological domains represents a key step toward the realization of functional twin systems, in which prediction and experimentation continuously inform each other.

7.4. Toward Clinically Actionable Hybrid Twins: Opportunities and Challenges

The ultimate promise of hybrid approaches lies in their potential to support real-time, patient-specific therapeutic decision-making. In a fully realized framework, patient data would be used to generate a digital representation capable of predicting candidate therapies, which would then be functionally tested in BTs. The results of these experiments would, in turn, refine the digital model, creating a closed-loop system capable of converging on optimal treatment strategies.

Early elements of this vision are already visible. Studies combining computational stratification with organoid-based drug screening demonstrate the feasibility of generating patient-specific therapeutic insights within clinically relevant timeframes, particularly when molecular profiling is directly coupled with functional validation in patient-derived systems (Boden, 2025; Gao, 2024; Montagner, 2025; Peng et al., 2025; Hu et al., 2025). Similarly, the integration of PK/PD modeling (Saeheng, 2024) with experimental validation platforms suggests a pathway toward optimizing not only drug selection but also dosing and scheduling.

However, significant challenges remain. The integration of heterogeneous data types, ranging from imaging and multi-omics to functional assay results, requires robust computational infrastructures and standardized workflows. Moreover, aligning the timelines of digital analysis and biological experimentation with clinical decision-making remains a critical hurdle, particularly for time-sensitive conditions such as advanced CCA. The variable success rates of organoid establishment — as low as 14.6% for CCA in some series (Harding et al., 2024, ASCO abstract 533) — further constrain the practical applicability of hybrid frameworks that depend on rapid BT generation. Issues related to reproducibility, scalability, and regulatory validation must also be addressed before hybrid twin systems can be widely adopted in clinical practice.

7.5. A Forward-Looking Perspective: Hybrid Twins as the Foundation of Next-Generation Precision Oncology

Despite these challenges, the convergence of DTs and BTs represents one of the most promising directions in CCA research. Rather than viewing these approaches as competing or sequential, hybrid frameworks highlight their complementary nature: digital models provide scalability, integrative capacity, and predictive power, while biological systems offer functional validation and mechanistic insight (Sadée et al., 2025; Miedel et al., 2025).

In this context, hybrid twins can be conceptualized as dynamic, multi-layered systems that integrate data, computation, and experimentation into a unified translational pipeline. Such systems have the potential to move beyond static predictions and toward adaptive, continuously updated representations of patient-specific disease. This paradigm aligns closely with the broader vision of precision oncology as a learning system, in which each patient contributes to the refinement of both models and therapies.

Ultimately, the development of hybrid twin approaches may redefine how therapeutic decisions are made in CCA. By enabling the iterative integration of prediction and validation, these systems offer a pathway toward truly actionable precision medicine, in which treatment strategies are not only informed by data but actively tested and optimized in patient-specific models before clinical implementation.

8. Challenges and Future Perspectives

Despite the conceptual maturity and growing translational momentum of DTs and BTs in CCA, their integration into routine clinical workflows remains constrained by a set of interdependent challenges spanning data infrastructure, model development, validation, and clinical adoption.

8.1. Technical and Computational Barriers

A fundamental limitation arises from the intrinsic characteristics of biomedical data in CCA, which is typically fragmented, heterogeneous, and sparsely sampled. The rarity of the disease, coupled with the limited availability of longitudinal and multi-omics datasets, significantly constrains the development of robust patient-specific models. In current practice, imaging, molecular, and clinical data are acquired across different platforms and timepoints, often lacking standardization and completeness, thereby complicating their integration into coherent digital representations.

These issues are well illustrated in the rapidly expanding field of radiomics and AI in CCA, where numerous studies have demonstrated promising performance in tasks such as differential diagnosis, prediction of lymph node metastasis, and early recurrence. However, as highlighted by recent meta-analyses (Xu et al., 2025; Alidina et al., 2026), most of these models remain limited by retrospective design, small cohorts, and lack of external validation, preventing their translation into clinical decision-making. The absence of standardized imaging protocols and feature extraction pipelines further contributes to variability and limits reproducibility across institutions (Zerunian, 2025).

Beyond data harmonization, a major challenge lies in capturing the dynamic nature of CCA biology. Tumor progression is governed by complex processes, including clonal evolution, microenvironmental interactions, and therapy-induced selective pressures. Current AI-driven models are predominantly static and correlative, whereas clinically meaningful DTs require the integration of temporal and mechanistic dimensions. Hybrid modeling strategies that combine data-driven approaches with mechanistic frameworks, such as PK/PD modeling, are therefore essential to approximate disease dynamics.

In addition, the lack of interoperability across models remains a critical bottleneck. Most existing tools are developed as task-specific solutions, rather than as modular components of an integrated system. This fragmentation limits scalability and prevents the construction of comprehensive DT architectures capable of incorporating imaging, molecular, and clinical data in a unified and continuously updated framework.

Finally, computational constraints must be considered in the context of clinical applicability. For DTs to inform therapeutic decisions, particularly in advanced disease settings, model outputs must be generated within clinically actionable timeframes. Achieving this balance between model complexity and computational efficiency remains a key technical challenge.

8.2. Regulatory and Ethical Considerations

The clinical deployment of DTs and BTs is further complicated by an evolving and fragmented regulatory landscape. In Europe, these systems intersect with multiple frameworks, including the General Data Protection Regulation (GDPR), the European Health Data Space, the AI Act, the Medical Device Regulation, the Clinical Trial Regulation, and cybersecurity directives. This overlap creates uncertainty regarding the classification and validation requirements of DT systems, particularly when they function as clinical decision support tools.

A major unresolved issue concerns the generation of robust clinical evidence. While many AI-based models in CCA report high predictive accuracy, their real-world applicability remains uncertain due to limited prospective validation and poor generalizability. A recent meta-analysis of CT-based AI models for predicting early recurrence in CCA highlighted substantial heterogeneity across studies and emphasized concerns regarding reproducibility, dataset bias, and performance variability across populations (Chen, 2025). These findings underscore the need for standardized validation pipelines and prospective clinical trials assessing not only predictive performance but also impact on clinical outcomes.

Data governance represents an additional critical dimension. The development of high-fidelity DTs requires access to large-scale, high-resolution patient data, raising important concerns regarding privacy, consent, and data ownership. Privacy-preserving approaches, such as federated learning, offer potential solutions but require substantial infrastructural and organizational investment.

Ethical considerations also extend to issues of bias and equity. AI models trained on non-representative datasets may inadvertently propagate disparities in healthcare delivery, a risk that is particularly relevant in cholangiocarcinoma given its geographic and etiological heterogeneity. Addressing these challenges requires deliberate efforts to ensure diversity and representativeness in training datasets.

Moreover, intellectual property management for data and AI models requires clear policies to promote innovation while protecting stakeholders’ rights.

8.3. Trust, Education, and Adoption

Beyond technical and regulatory barriers, the successful integration of DTs and BTs into clinical practice ultimately depends on their acceptance by clinicians, which is closely linked to issues of interpretability and trust.

A central limitation of many high-performing AI models lies in their lack of transparency. Deep learning systems, in particular, often function as “black boxes”, providing predictions without a clear explanation of the underlying decision-making process. In the context of CCA, where therapeutic decisions are complex and often irreversible, this lack of interpretability represents a significant barrier to clinical adoption. Even in domains such as radiomics, where AI models have shown strong predictive performance, their translation into practice has been hindered by limited explainability and lack of standardized reporting (Zerunian, 2025).

Explainable artificial intelligence (XAI) has emerged as a critical strategy to address this gap. By enabling the identification of features driving model predictions, XAI approaches can bridge the gap between computational outputs and clinical reasoning. In imaging-based models, for instance, visualization techniques can highlight tumor regions contributing to classification, while feature attribution methods can quantify the influence of specific clinical or molecular variables on predicted outcomes. These capabilities are particularly relevant in CCA, where integrating radiological, molecular, and clinical features is essential for accurate patient stratification.

However, interpretability alone is insufficient to ensure adoption. The broader framework of trustworthy AI emphasizes additional dimensions, including robustness, reproducibility, fairness, and uncertainty quantification. This is especially important in CCA, where model performance may vary significantly across populations and clinical settings, as highlighted by variability in AI-based recurrence prediction studies (Chen, 2025). Communicating uncertainty and defining the limits of model applicability are therefore essential components of clinically usable systems.

From a translational perspective, the presentation of model outputs is equally critical. Clinicians require interpretable and actionable information, rather than abstract probabilistic predictions. The integration of DT outputs into structured decision-support tools, such as risk scores, nomograms, or tumor board interfaces — as exemplified by the Rome Trial’s molecular tumor board model (Cardinale et al., 2022, ASCO abstract 3087) — represents a key step toward practical implementation.

Ultimately, fostering trust will also require targeted educational initiatives and the development of interdisciplinary expertise. Clinicians must be equipped not only to use these tools, but also to critically interpret their outputs and understand their limitations, ensuring that DTs and BTs function as decision-support systems rather than replacements for clinical judgment.

9. Epistemological and Ethical Reflections

The emergence of DTs and BTs in CCA reflects a broader shift in the epistemological foundations of medicine, from population-based inference toward individualized and dynamically updated models of disease.

In this context, DTs should not be regarded as objective representations of biological reality, but as probabilistic constructs shaped by data, assumptions, and modeling choices. This perspective is particularly relevant in CCA, where biological heterogeneity and data limitations introduce significant uncertainty. For example, a DT built on the assumption that PBG-derived BTSCs are the primary cell of origin for large bile duct iCCA (Carpino et al., 2019; Cardinale et al., 2024) may generate systematically different therapeutic predictions than one incorporating hepatocyte-derived carcinogenesis pathways (Guest et al., 2025) — yet both may be valid for different patient subsets. Recognizing the contingent nature of these models is essential to avoid over-reliance on computational outputs and to preserve the central role of clinical expertise.

At the same time, twin-based approaches offer a unique opportunity to formalize and quantify uncertainty. Unlike traditional clinical frameworks, which often rely on implicit assumptions, these systems can explicitly model variability and simulate alternative therapeutic scenarios. This capability has important implications for decision-making in CCA, where treatment options are limited and outcomes are highly variable. The ANITA study’s finding that only 18.7% of molecularly eligible patients received matched therapy (Genovesi et al., 2026) illustrates how even well-characterized molecular data fails to translate into clinical action without appropriate decision-support infrastructure — a gap that hybrid twin systems could help address.

Ethical considerations are closely intertwined with these epistemological aspects. The reliance on data-driven models introduces risks related to bias, representativeness, and equity, particularly in the context of a disease with marked geographic heterogeneity — CCA driven by liver fluke infection in Southeast Asia differs fundamentally from PSC-associated CCA in Northern Europe, and models trained predominantly on one population may perform poorly in the other (Valle et al., 2021). Ensuring fairness in model development and deployment is therefore a critical priority.

This consideration also invites a broader reflection on the anthropological framework within which these technologies are embedded. As recently articulated in the Encyclical Magnifica Humanitas of Pope Leo XIV, the contemporary expansion of artificial intelligence and digital infrastructures risks reinforcing a “technocratic paradigm,” in which efficiency, control, and optimization become dominant criteria for evaluating human activity. In the context of precision oncology, this perspective resonates with the increasing reliance on computational representations of patients, which—if uncritically adopted—may privilege functional abstraction over a more integral understanding of the human person. From this standpoint, digital twins should not be understood as neutral or exhaustive representations of biological reality, but as situated and interpretative constructs, whose use requires continuous epistemological and ethical vigilance. Their responsible integration into clinical practice therefore depends on preserving the primacy of human judgment, safeguarding the dignity and relational dimension of the patient, and ensuring that technological innovation remains oriented toward person-centered and equitable care rather than purely technical performance. Building on this perspective, twin-based approaches offer a unique opportunity to formalize and quantify uncertainty.

More broadly, the integration of DTs and BTs into clinical workflows can be seen as a step toward a learning healthcare system, in which each patient contributes to the continuous refinement of models and therapeutic strategies. In this paradigm, knowledge is not static but evolves dynamically through the iterative interaction between data, models, and clinical practice. Transparency in model design, data provenance, and limitations is paramount for trust and ethical governance.

10. Conclusion

DTs and BTs represent a transformative and rapidly evolving paradigm in CCA research and clinical care. By integrating multidimensional patient data into computational models and complementing them with functional validation in patient-derived systems, these approaches enable a more comprehensive and actionable understanding of disease biology.

Although fully realized twin systems capable of real-time clinical decision support are not yet part of standard practice, many of their foundational components are already in place. Radiomics and AI-based models are increasingly used for diagnosis, prognostication, and risk stratification — with meta-analytic evidence now confirming pooled C-indices of 0.85–0.91 for combined radiomic-clinical models in iCCA (Xu et al., 2025). BTs provide platforms for functional validation and therapeutic exploration, though practical challenges such as variable organoid success rates — as low as 14.6% for CCA (Harding et al., 2024, ASCO abstract 533) — and the limited scalability of PDX models must be addressed. As highlighted by recent evidence, the translation of these approaches into clinical practice remains limited by issues of validation, reproducibility, and generalizability (Xu et al., 2025; Alidina et al., 2026).

The convergence of DTs and BTs into hybrid frameworks represents a critical step toward operational precision oncology that could improve the outcome of such a challenging disease. This concept, which parallels the “integrated patient digital and biomimetic twins” framework recently proposed for MASLD (Miedel et al., 2025), is particularly well-suited to CCA given the disease’s molecular heterogeneity, limited therapeutic options, and the growing gap between molecular profiling capability and therapeutic access (Genovesi et al., 2026). Achieving this vision will require advances in data integration, regulatory alignment, and model interpretability, with particular emphasis on the development of explainable and trustworthy AI systems.

Ultimately, the clinical value of DTs and BTs will depend not on their technological sophistication alone, but on their ability to improve patient outcomes in real-world settings. If successfully implemented, these systems have the potential to enhance treatment selection, reduce therapeutic inefficiency, and contribute to a more adaptive and patient-centered model of care in cholangiocarcinoma.