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Review
. 2025 Aug 25;11(9):96.
doi: 10.3390/tomography11090096.

Leveraging Multimodal Foundation Models in Biliary Tract Cancer Research

Yashbir Singh  1 , Jesper B Andersen  2 , Quincy A Hathaway  3 , Diana V Vera-Garcia  4 , Varekan Keishing  1 , Sudhakar K Venkatesh  1 , Sara Salehi  1 , Davide Povero  5 , Michael B Wallace  4 , Gregory J Gores  5 , Yujia Wei  1 , Natally Horvat  1 , Bradley J Erickson  1 , Emilio Quaia  6
Affiliations
Review

Leveraging Multimodal Foundation Models in Biliary Tract Cancer Research

Yashbir Singh et al. Tomography. .

Abstract

This review explores how multimodal foundation models (MFMs) are transforming biliary tract cancer (BTC) research. BTCs are aggressive malignancies with poor prognosis, presenting unique challenges due to difficult diagnostic methods, molecular complexity, and rarity. Importantly, intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and distal bile duct cholangiocarcinoma (dCCA) represent fundamentally distinct clinical entities, with iCCA presenting as mass-forming lesions amenable to biopsy and targeted therapies, while pCCA manifests as infiltrative bile duct lesions with challenging diagnosis and primarily palliative management approaches. MFMs offer potential to advance research by integrating radiological images, histopathology, multi-omics profiles, and clinical data into unified computational frameworks, with applications tailored to these distinct BTC subtypes. Key applications include enhanced biomarker discovery that identifies previously unrecognizable cross-modal patterns, potential for improving currently limited diagnostic accuracy-though validation in BTC-specific cohorts remains essential-accelerated drug repurposing, and advanced patient stratification for personalized treatment. Despite promising results, challenges such as data scarcity, high computational demands, and clinical workflow integration remain to be addressed. Future research should focus on standardized data protocols, architectural innovations, and prospective validation studies. The integration of artificial intelligence (AI)-based methodologies offers new solutions for these historically challenging malignancies. However, current evidence for BTC-specific applications remains largely theoretical, with most studies limited to proof-of-concept designs or related cancer types. Comprehensive clinical validation studies and prospective trials demonstrating patient benefit are essential prerequisites for clinical implementation. The timeline for evidence-based clinical adoption likely extends 7-10 years, contingent on successful completion of validation studies addressing current evidence gaps.

Keywords: artificial intelligence; biliary tract cancer; biomarkers; cholangiocarcinoma; drug repurposing; multimodal foundation models; precision oncology.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Multimodal foundation models (MFM) in biliary tract cancer (BTC) research.
Figure 2
Figure 2
Multimodal foundation model architecture for the BTC analysis.
Figure 3
Figure 3
Multimodal biomarker discovery in BTC.
Figure 4
Figure 4
Challenges and future directions in multimodel AI for BTC Management.
See this image and copyright information in PMC

References

    1. Banales J.M., Marin J.J.G., Lamarca A., Rodrigues P.M., Khan S.A., Roberts L.R., Cardinale V., Carpino G., Andersen J.B., Braconi C., et al. Cholangiocarcinoma 2020: The next horizon in mechanisms and management. Nat. Rev. Gastroenterol. Hepatol. 2020;17:557–588. doi: 10.1038/s41575-020-0310-z. - DOI - PMC - PubMed
    1. Valle J.W., Kelley R.K., Nervi B., Oh D.Y., Zhu A.X. Biliary tract cancer. Lancet. 2021;397:428–444. doi: 10.1016/S0140-6736(21)00153-7. - DOI - PubMed
    1. Pellino A., Loupakis F., Cadamuro M., Dadduzio V., Fassan M., Guido M., Cillo U., Indraccolo S., Fabris L. Precision medicine in cholangiocarcinoma. Transl. Gastroenterol. Hepatol. 2018;3:40. doi: 10.21037/tgh.2018.07.02. - DOI - PMC - PubMed
    1. Nakamura H., Arai Y., Totoki Y., Shirota T., Elzawahry A., Kato M., Hama N., Hosoda F., Urushidate T., Ohashi S., et al. Genomic spectra of biliary tract cancer. Nat. Genet. 2015;47:1003–1010. doi: 10.1038/ng.3375. - DOI - PubMed
    1. Li C., Gan Z., Yang Z., Yang J., Li L., Wang L., Gao J. Multimodal Foundation Models: From Specialists to General-Purpose Assistants. arXiv. 2023 doi: 10.48550/arXiv.2309.10020.2309.10020 - DOI

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