Deep learning predicts microsatellite instability status in colorectal carcinoma in an ethnically heterogeneous population in South Africa
Following appropriate region-specific calibration, we have shown that this model could be employed to accurately prescreen for dMMR in CRC, thereby reducing the burden of downstream IHC and molecular testing in a resource-limited setting.
Authors
- Gregory Patrick Veldhuizen (ORCID: https://orcid.org/0000-0001-7364-8498)
- Michael Hoffmeister (ORCID: https://orcid.org/0000-0002-8307-3197)
- Alessandro Pietro Aldera (ORCID: https://orcid.org/0000-0002-9615-1692)
- Adam Boutall (ORCID: https://orcid.org/0000-0002-6413-5890)
- Raj Ramesar (ORCID: https://orcid.org/0000-0001-5688-1634)
- Komala Pillay (ORCID: https://orcid.org/0000-0003-1971-900X)
- Didem Çifçi (ORCID: https://orcid.org/0000-0002-2647-9959)
- Jakob Nikolas Kather (ORCID: https://orcid.org/0000-0002-3730-5348)
- Hermann Brenner (ORCID: https://orcid.org/0000-0002-6129-1572)
- Wan-Jung Tsai (ORCID: https://orcid.org/0000-0002-2584-9348)
Institutions
- National Health Laboratory Service (ZA)
- University of Cape Town (ZA)
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Groote Schuur Hospital (ZA)
- University of Chicago (US)
- National Center for Tumor Diseases (DE)
- Technische Universität Dresden (DE)
Publication Details
- Journal
- Journal of Clinical Pathology
- Published
- 2025-05-20
- DOI
- https://doi.org/10.1136/jcp-2025-210053
- Citations
- 1
- Primary Topic
- Genetic factors in colorectal cancer
- Type
- article
- Field-Weighted Citation Impact
- 1.47
Funders
- National Institute for Health and Care Research
- Deutsche Krebshilfe
- National Institutes of Health