Non-invasive CT radiomic biomarkers predict microsatellite stability status in colorectal cancer: a multicenter validation study
Noninvasive CT-based radiomics predicted MSI in colorectal cancer, enhancing stratification. A seven-feature radiomic signature differentiated tumors with MSI from those with MSS in multicenter cohorts. Integrating radiomic and clinical data improved the algorithm's predictive performance.
Authors
- Ieva Kurilova
- Nino Bogveradze
- Federica Landolfi (ORCID: https://orcid.org/0000-0002-2208-2559)
- Giovanni Randon (ORCID: https://orcid.org/0000-0003-0244-445X)
- Filippo Pietrantonio (ORCID: https://orcid.org/0000-0002-8530-8420)
- Zuhir Bodalal (ORCID: https://orcid.org/0000-0002-2617-8128)
- Francesca Castagnoli (ORCID: https://orcid.org/0000-0003-2407-7451)
- Geerard L. Beets (ORCID: https://orcid.org/0000-0002-1671-9912)
- Eun Kyoung Hong (ORCID: https://orcid.org/0000-0002-5440-0451)
- Pétur Snæbjörnsson (ORCID: https://orcid.org/0000-0002-1086-8108)
- Stefano Trebeschi (ORCID: https://orcid.org/0000-0002-5714-289X)
- Jeong Min Lee (ORCID: https://orcid.org/0000-0003-0561-8777)
- Regina G. H. Beets‐Tan (ORCID: https://orcid.org/0000-0002-8533-5090)
Institutions
- Institute of Cancer Research (GB)
- University of Iceland (IS)
- University of Southern Denmark (DK)
- University of Milan (IT)
- Swedish American Hospital (US)
- Seoul National University Hospital (KR)
- The Netherlands Cancer Institute (NL)
- Maastricht University (NL)
- Oncode Institute (NL)
- Royal Marsden Hospital (GB)
- Fondazione IRCCS Istituto Nazionale dei Tumori (IT)
- Maastro Clinic (NL)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2024-08-26
- DOI
- https://doi.org/10.1186/s41747-024-00484-8
- Citations
- 17
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.87
Funders
- Nvidia