Predicting Grade and Patient Survival in Renal Cancer Using Machine Learning Analysis of Nucleolar Prominence
These findings suggest that incorporating refined criteria into the WHO/ISUP system could enhance its prognostic accuracy in future revisions.
Authors
- Y. I. Osmanov (ORCID: https://orcid.org/0000-0002-7269-4190)
- Елена Иванова (ORCID: https://orcid.org/0000-0002-5601-0465)
- Victor Grinin
- M O Astaeva (ORCID: https://orcid.org/0000-0002-7719-7898)
- А. В. Лычагин (ORCID: https://orcid.org/0000-0002-2202-8149)
- Ekaterina Rudenko
- Alesia Bakulina (ORCID: https://orcid.org/0000-0001-9885-9160)
- Maxim Balyasin (ORCID: https://orcid.org/0000-0002-3097-344X)
- Т. А. Демура (ORCID: https://orcid.org/0000-0002-6946-6146)
- Anna Timakova (ORCID: https://orcid.org/0000-0003-0269-4969)
- Alexey Fayzullin (ORCID: https://orcid.org/0000-0003-4137-8993)
- Peter S. Timashev (ORCID: https://orcid.org/0000-0001-7773-2435)
- Nina Shved
- Alexander M. Arutyunyan
- Dmitry Ermilov
- Ruslan Parchiev
- Dmitry Zhavoronkov
Institutions
- Skolkovo Institute of Science and Technology (RU)
- Peoples' Friendship University of Russia (RU)
- Sechenov University (RU)
- Qualcomm (United Kingdom) (GB)
- Skolkovo Moscow School of Management (RU)
- Research Center of Neurology (RU)
- Russian Scientific Center of Surgery (RU)
- Skolkovo Foundation (RU)
Publication Details
- Journal
- Cancer Medicine
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1002/cam4.71196
- Citations
- 1
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- Ministry of Science and Higher Education of the Russian Federation
- I.M. Sechenov First Moscow State Medical University