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Predicting Grade and Patient Survival in Renal Cancer Using Machine Learning Analysis of Nucleolar Prominence

ABSTRACT Background Patients with clear cell renal cell carcinoma (ccRCC) often undergo organ resection, with treatment strategies based on recurrence risk. Current metastatic potential assessments rely on the WHO/ISUP grading system, which is subject to interobserver variability. Methods We develop...

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Principais autores: Elena Ivanova, Alexey Fayzullin, Victor Grinin, Dmitry Zhavoronkov, Dmitry Ermilov, Maxim Balyasin, Anna Timakova, Alesia Bakulina, Yusif Osmanov, Ekaterina Rudenko, Alexander Arutyunyan, Ruslan Parchiev, Nina Shved, Marina Astaeva, Aleksey Lychagin, Tatiana Demura, Peter Timashev
Format: Artigo
Sprog:Inglês
Udgivet: Wiley 2025-09-01
Serier:Cancer Medicine
Fag:
artificial intelligence
computational pathology
computer vision
digital pathology
renal cell carcinoma
Online adgang:https://doi.org/10.1002/cam4.71196
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