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

• Published in: Cancer medicine (Malden, MA) Vol. 14; no. 17; pp. e71196 - n/a
• Main Authors: Ivanova, Elena, Fayzullin, Alexey, Grinin, Victor, Zhavoronkov, Dmitry, Ermilov, Dmitry, Balyasin, Maxim, Timakova, Anna, Bakulina, Alesia, Osmanov, Yusif, Rudenko, Ekaterina, Arutyunyan, Alexander, Parchiev, Ruslan, Shved, Nina, Astaeva, Marina, Lychagin, Aleksey, Demura, Tatiana, Timashev, Peter
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.09.2025; Wiley
• Subjects: Accuracy; Aged; Annotations; Artificial intelligence; Automation; Carcinoma, Renal Cell - mortality; Carcinoma, Renal Cell - pathology; Cell density; Cell Nucleolus - pathology; Classification; Clear cell-type renal cell carcinoma; computational pathology; Computer vision; Datasets; digital pathology; Female; Humans; Kidney cancer; Kidney Neoplasms - mortality; Kidney Neoplasms - pathology; Machine Learning; Male; Metastases; Middle Aged; Morphology; Neoplasm Grading; Nucleoli; Pathology; Prognosis; renal cell carcinoma; ROC Curve; Software; Russia; renal cell carcinoma; computational pathology; digital pathology; artificial intelligence; computer vision
• ISSN: 2045-7634; 2045-7634
• DOI: 10.1002/cam4.71196

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 developed an artificial intelligence (AI) model to classify cells according to contemporary grading rules and evaluated the prognostic significance of tumor cell profiles, particularly focusing on cells with prominent nucleoli. Results The model accurately distinguished low (G1/G2) and high (G3/G4) grades, achieving an area under the ROC curve of 0.79. Survival analysis identified four tissue patterns defined by total cell density and the proportion of cells with prominent nucleoli. The relative abundance of such cells had greater prognostic value than their mere presence, correlating with survival times ranging from 2.2 to over 6 years. Additionally, we confirmed that dystrophic changes and focal necrosis are linked to shorter survival. Conclusion These findings suggest that incorporating refined criteria into the WHO/ISUP system could enhance its prognostic accuracy in future revisions.