Automated vs. manual segmentation for small renal mass imaging
Automated segmentation of CT scans for patients with SRM was efficient, accurate, and acceptable in this study. This approach has the potential to greatly improve the clinical use of radiomics to assess medical images for these patients.
Authors
- Andrés Kohan (ORCID: https://orcid.org/0000-0003-3592-3000)
- Dominik Deniffel (ORCID: https://orcid.org/0000-0001-8200-8396)
- Kristen McAlpine (ORCID: https://orcid.org/0000-0003-3973-651X)
- Masoom A. Haider (ORCID: https://orcid.org/0000-0002-7165-8315)
- Satheesh Jeyaraj Krishna
- Emily Seto
- Girish Kulkarni
- Antonio Finelli
- Nikhil Mirajkar
Institutions
- University Health Network (CA)
- University of Toronto (CA)
- Toronto General Hospital (CA)
- Cambridge University Hospitals NHS Foundation Trust (GB)
- Princess Margaret Cancer Centre (CA)
- Sinai Health System (CA)
- Kantonsspital Frauenfeld (CH)
- Technical University of Munich (DE)
Publication Details
- Journal
- Canadian Urological Association Journal
- Published
- 2026-02-13
- DOI
- https://doi.org/10.5489/cuaj.9476
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Canadian Urological Association Scholarship Fund