Multimodal analysis of whole slide images in colorectal cancer - Jonnagaddala, Jitendra; Shulajkovska, Miljana; Gradišek, Anton; Jue, Toni Rose; Zhou, Qifeng; Guo, Yuzhi; Chayeb, Jamil Mahmoud El; Li, Ruijiang; Lipkova, Jana; Kather, Jakob Nikolas; Huang, Junzhou (npj digital medicine, 2025)
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.
Veröffentlicht: 24. November 2025
Gesehen am 29.01.2026