Prediction of renal cell carcinoma WHO/ISUP nuclear grade with quantitative analysis of perirenal fat combined with Mayo adhesive probability score
10.3969/j.issn.1002-1671.2025.11.014
- VernacularTitle:肾周脂肪定量分析联合梅奥粘连概率评分预测肾细胞癌WHO/ISUP核分级的研究
- Author:
Runhua LI
1
;
Xinguang XIAO
1
;
Ning ZHANG
1
;
Hongyang HAN
1
;
Yalong CHEN
1
;
Kun WANG
1
Author Information
1. 郑州大学附属郑州中心医院放射科,河南 郑州 450007
- Publication Type:Journal Article
- Keywords:
perirenal fat;
Mayo adhesive probability score;
renal cell carcinoma;
WHO/ISUP nuclear grade
- From:
Journal of Practical Radiology
2025;41(11):1825-1829
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of quantitative analysis of perirenal fat combined with Mayo adhesive probability(MAP)score in predicting the WHO/International Society of Urological Pathology(ISUP)nuclear grade of renal cell carcinoma(RCC).Methods The imaging data of 139 pathologically confirmed RCC patients were retrospectively analyzed.The patients were divided into low-grade group(grade Ⅰ-Ⅱ,n=112)and high-grade group(grade Ⅲ-Ⅳ,n=27)according to the WHO/ISUP nuclear grade.Spearman correlation analysis was used to assess the relationship between fat features and WHO/ISUP nuclear grade.The multivariate logistic regression model was used to detemine the related factors of high-grade RCC,and the area under the curve(AUC)of the receiver operating characteristic(ROC)curve was used to evaluate the diagnostic performance of each parameter.Results The AUC of perirenal adipose tissue(PAT)％alone for evaluating high-grade RCC was the highest,at 0.77[95％confidence interval(CI)0.69-0.83].The stepwise multivariate logistic regression model showed that perinephric fat stranding(PFS)[odds ratio(OR)=34.54,95％CI 7.60-156.87,P＜0.001],PAT％(OR=0.46,95％CI 0.32-0.66,P＜0.001),and tumor location(OR=0.26,95％CI 0.07-0.92,P=0.037)were related factors of high-grade RCC,with an AUC of 0.90(95％CI 0.84-0.94).Conclusion Quantitative analysis of perire-nal fat combined with MAP score can effectively predict the WHO/ISUP nuclear grade of RCC,providing a novel approach for per-sonalized treatment strategies to improve prognosis.
