Differentiating between renal medullary and clear cell renal carcinoma with a machine learning radiomics approach
A radiomics-based machine learning model effectively differentiates between ccRCC and RMC. This tool can facilitate the radiologist's ability to suspicion and decrease the misdiagnosis rate of RMC.
Authors
- Kristy K. Brock (ORCID: https://orcid.org/0000-0001-9364-5040)
- Rahul A. Sheth (ORCID: https://orcid.org/0000-0001-9615-8985)
- Koustav Pal (ORCID: https://orcid.org/0000-0002-0777-2260)
- Bruno Calazans Odisio (ORCID: https://orcid.org/0000-0002-1771-8233)
- Nizar M. Tannir (ORCID: https://orcid.org/0000-0001-5559-8060)
- Rahim Jiwani (ORCID: https://orcid.org/0000-0002-1324-865X)
- Pavlos Msaouel (ORCID: https://orcid.org/0000-0001-6505-8308)
- Iwan Paolucci (ORCID: https://orcid.org/0000-0002-9393-3015)
- Daniel D. Shapiro
Institutions
- The University of Texas MD Anderson Cancer Center (US)
- University of Wisconsin–Madison (US)
Publication Details
- Journal
- The Oncologist
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1093/oncolo/oyae337
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- U.S. Department of Defense
- V Foundation for Cancer Research
- Gateway for Cancer Research
- Johnson Foundation
- Kidney Cancer Association
- Andrew Sabin Family Foundation
- University of Texas MD Anderson Cancer Center
- National Cancer Institute