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 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 re

SUBMITTER: Ivanova E 

PROVIDER: S-EPMC12420676 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature