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April 7, 2026DiagnosticsOpen Access

Peritumoral Fat Radiomics for Dual Prediction of TNM Stage and Histological Grade in Clear Cell Renal Cell Carcinoma: Discovery of Target-Specific Optimal Imaging Distances

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Authors

AMAbdulrahman Al MoptiAAAbdulsalam A. AlqahtaniAAAli H. D. Alshehri

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Overview

Multi-cohort retrospective study demonstrates accurate prediction of TNM stage and histological grade in clear cell renal cell carcinoma, suggesting distinct microenvironmental factors.

Key Points

  • This research aims to determine if perirenal fat features can predict TNM stage and histological grade in clear cell renal cell carcinoma.
  • Included 474 ccRCC patients from three independent datasets.
  • Performed automated segmentation to delineate tumors and kidneys.
  • Generated concentric PRF regions at 1–10 mm radial distances.
  • Extracted 1409 radiomic features and used various machine learning classifiers.
  • Optimized models through hyperparameter tuning and cross-cohort validation.
  • Optimal peritumoral distances for predictions were 4 mm for TNM staging and 10 mm for histological grading.
  • The integrated model for TNM staging achieved an AUC of 0.829 with sensitivity of 80.2% and specificity of 67.8%.
  • For grading, the combined model achieved an AUC of 0.780 with sensitivity of 79.7% and specificity of 63.3%.
  • Both models significantly outperformed single-compartment models.

Cite This Study

Mopti et al. (2026) studied this question.

synapsesocial.com/papers/69d49fc5b33cc4c35a228492https://doi.org/10.3390/diagnostics16071099
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1CT-Based Peritumoral and Perirenal Fat Radiomics in Renal Cell Carcinoma: A Systematic Review and Meta-Analysis of Grade, Stage, and Adherent Perinephric Fat Prediction2026
  2. 2Multiphase CT-Based Tumor and Peritumoral Radiomics for Characterization of Clear Cell Renal Cell Carcinoma2026
  3. 3CT-based subregional and peritumoral radiomics for predicting pathological T stage of clear cell renal cell carcinoma: an exploratory study of biological mechanisms2026 · 1 citations
  4. 4Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma: Integrating Texture Features with Clinical Predictors2024 · 2 citations
  5. 5Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma2024 · 29 citations