Multimodal analysis of whole slide images in colorectal cancer
Multimodal models have enabled the integration of digital pathology, radiology, clinical information, and omics data to enhance Colorectal cancer (CRC) care. This systematic review critically appraises Multimodal digital pathology techniques applied in CRC, their performance, and contrasts them with foundation models. We identified and screened 1601 studies published between January 2014 and August 2024 using PubMed, Web of Science, Scopus, and IEEE Xplore (PROSPERO protocol: 635831). The quality and bias of the 22 eligible studies were assessed using the Newcastle-Ottawa Scale. Our findings suggest that majority of the studies integrated different modalities to enhance diagnostic accuracy and survival prediction. Various fusion techniques have been used to extract novel features. Most studies did not undertake external validation. Compared to unimodal models, multimodal approaches demonstrate superior performance but challenges remain, including constructing multimodal datasets, managing data heterogeneity, ensuring temporal alignment, determining modality weighting, and improving interpretability.
Authors
- Ruijiang Li (ORCID: https://orcid.org/0000-0002-0232-5998)
- Yuzhi Guo (ORCID: https://orcid.org/0000-0002-8993-1818)
- Jana Lipková (ORCID: https://orcid.org/0000-0001-8101-4794)
- Jitendra Jonnagaddala (ORCID: https://orcid.org/0000-0002-9912-2344)
- Toni Rose Jue (ORCID: https://orcid.org/0000-0002-8449-5973)
- Junzhou Huang (ORCID: https://orcid.org/0000-0002-9548-1227)
- Anton Gradišek (ORCID: https://orcid.org/0000-0001-6480-9587)
- Miljana Shulajkovska
- Jamil Mahmoud El Chayeb
- Qifeng Zhou
- Jakob Nikolas Kather
Institutions
- University of Leeds (GB)
- The University of Texas at Arlington (US)
- University of California, Irvine (US)
- Heidelberg University (DE)
- Shaikh Khalifa Medical City (AE)
- University Hospital Heidelberg (DE)
- Jožef Stefan Institute (SI)
- UNSW Sydney (AU)
- National Center for Tumor Diseases (DE)
- Jožef Stefan International Postgraduate School (SI)
- Irvine University (US)
- University Hospital Carl Gustav Carus (DE)
- Data61 (AU)
- Stanford University (US)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-11-24
- DOI
- https://doi.org/10.1038/s41746-025-02095-y
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Nvidia
- National Institute for Health and Care Research
- Department of Health and Social Care
- European Commission
- Deutscher Akademischer Austauschdienst
- Deutsche Forschungsgemeinschaft
- University of New South Wales
- Deutsche Krebshilfe
- Gemeinsame Bundesausschuss
- National Institutes of Health
- HORIZON EUROPE Framework Programme
- Medical Research Council
- National Health and Medical Research Council
- Leeds Biomedical Research Centre