Genomics and integrative clinical data machine learning scoring model to ascertain likely Lynch syndrome patients
Simultaneous scoring of LS clinicopathological and somatic genomics data can improve prediction and ascertainment for likely-LS from all CRC cases. This approach can increase accuracy while reducing the reliance on expensive direct germline testing for all CRC patients, making LS screening more accessible and cost-effective, especially in resource-limited settings.
Authors
- Ramadhani Chambuso (ORCID: https://orcid.org/0000-0001-8794-604X)
- Armin Deffur (ORCID: https://orcid.org/0000-0001-8917-3132)
- Douglas J. Perkins (ORCID: https://orcid.org/0000-0001-9390-6255)
- Alessandro Pietro Aldera (ORCID: https://orcid.org/0000-0002-9615-1692)
- Raj Ramesar (ORCID: https://orcid.org/0000-0001-5688-1634)
- Takudzwa Nyasha Musarurwa
- Hayli Geffen
Institutions
- University of Cape Town (ZA)
- University of New Mexico (US)
- International Geographical Union (ZA)
- Wellcome Centre for Infectious Diseases Research in Africa (ZA)
Publication Details
- Journal
- BJC Reports
- Published
- 2025-05-05
- DOI
- https://doi.org/10.1038/s44276-025-00140-7
- Primary Topic
- Genetic factors in colorectal cancer
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Hebei North University
- National Institutes of Health