SCIENCE@home

CT-based variables of perirenal fat are risk factors for assessing pathological T-stage in patients with clear cell renal cell carcinoma

Hao Guo,Linlin Meng,Lingchen Zhu,Zhen Zhang,Yumei Zhang,Zehua Sun,Zhongyi Wang,Yang Chen,Jiakang Xu,Jiaqi Li,Heng Ma,Feng Li,Yongli Chu,Xinru Ba

Published 2026 in Annals medicus

ABSTRACT

Abstract Background Advances in the treatment of clear cell renal cell carcinoma (ccRCC) toward risk-based therapy intensity modulation have necessitated patient-specific assessments of preoperative T-stage. Methods A derivation cohort of 218 ccRCC patients with known pathological results and preoperative biomarker data was used to develop and validate a predictive model for preoperative T-stage. Perirenal fat characteristics were determined by the measurement or evaluation of preoperative CT images. Multivariate logistic regression was used to identify the predictive factors and to develop a predictive model, which was then internally validated using cross-validation and bootstrapping. Model discrimination was assessed using calibration plots. Finally, nomograms were both plotted to visualize model efficiency. Results A total of 218 patients (152 males and 66 females) with pathological tumor T-stage (151 low T-stage and 67 high T-stage) were enrolled in this study. Perinephric fat stranding (PFS), RENAL Nephrometry Score (RNS), and preoperative platelet (PLT) counts were independent predictors of a high ccRCC T-stage. The performance of the predictive model built on these variables notably surpasses that of radiologists (AUC: 0.867 vs 0.680; delong test, p < 0.001). Internal validation with a K-fold cross-validation (K = 10) and a bootstrap method showed good discrimination. The model also demonstrated proper calibration. According to the decision curve analysis, the model was found to be clinically useful (risk threshold probability: 6%–85%). Conclusions PFS, RNS, and preoperative PLT provided excellent predictions of a high pathological T-stage for ccRCC patients. This predictive model can serve as a reference to aid clinicians and surgeons in clinical decision-making.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-32 of 32 references · Page 1 of 1

CITED BY

  • No citing papers are available for this paper.

Showing 0-0 of 0 citing papers · Page 1 of 1