MRI-based texture analysis for breast cancer subtype classification in a multi-ethnic population
These findings highlight the role of MRI-based texture features and advanced ML in enhancing breast cancer diagnosis, offering a non-invasive tool for personalised treatment planning while complementing existing clinical workflows.
Authors
- Song‐Quan Ong (ORCID: https://orcid.org/0000-0003-2869-4000)
- Jeannie Hsiu Ding Wong (ORCID: https://orcid.org/0000-0001-8080-1294)
- Marlina Tanty Ramli (ORCID: https://orcid.org/0000-0001-6365-2004)
- Nazimah Ab Mumin (ORCID: https://orcid.org/0000-0001-8720-5700)
- Chuin‐Hen Liew (ORCID: https://orcid.org/0000-0001-8775-0303)
- Kartini Rahmat (ORCID: https://orcid.org/0000-0001-8513-9076)
- Kwan Hoong Ng (ORCID: https://orcid.org/0000-0003-1383-8614)
Institutions
- University Malaya Medical Centre (MY)
- Universiti of Malaysia Sabah (MY)
- University of Malaya (MY)
- Hospital Tuanku Ja’afar (MY)
- Hospital Sungai Buloh (MY)
- UCSI University (MY)
- Universiti Teknologi MARA (MY)
Publication Details
- Journal
- Magnetic Resonance Materials in Physics Biology and Medicine
- Published
- 2025-08-12
- DOI
- https://doi.org/10.1007/s10334-025-01285-9
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Kementerian Sains, Teknologi dan Inovasi
- Universiti Teknologi MARA