|Table of Contents|

CT combined with abdominal fat quantification indexes predicts the invasiveness of renal cell carcinoma

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:
2023 24
Page:
4596-4600
Research Field:
Publishing date:

Info

Title:
CT combined with abdominal fat quantification indexes predicts the invasiveness of renal cell carcinoma
Author(s):
XU Jiajun1; ZHANG Hu1; ZHANG Xiaojin1; YUAN Na2; WANG Jiawei3; HUANG Guoquan1
1.Department of Radiology;2.Department of Pathology;3.Department of Urology,the Second People's Hospital of Wuhu,Anhui Wuhu 241000,China.
Keywords:
tomography; X-ray computed; fat variables; renal cell carcinoma; invasiveness
PACS:
R737.11
DOI:
10.3969/j.issn.1672-4992.2023.24.021
Abstract:
Objective:To study the utility of CT combined with abdominal fat quantification indexes in predicting the invasiveness of renal cell carcinoma (RCC).Methods:122 patients with surgical pathologically confirmed RCC were divided into non-invasion group (73 cases) and invasion group (49 cases) according to pathological stage and grade.Univariate and multivariate Logistic regression analysis screened independent predictors of RCC invasiveness.A joint prediction model was constructed by nomogram,and their calibration,stability,and clinical net benefit were assessed.Results:Blurred margins (OR=3.815,P=0.012),size (OR=1.752,P=0.010),subcutaneous fat area/visceral fat area ratio (SVR) (OR=5.381,P=0.006) and RENAL score (OR=2.110,P＜0.001) were independent predictors of RCC invasiveness.The AUC of the nomogram was 0.902 (95%CI:0.842～0.962,P＜0.001),which was higher than that of each independent predictor (all P＜0.05).The calibration curve showed good calibration.The average AUC of 5-fold cross-verification was 0.899,which was consistent with the nomogram.The decision curve analysis (DCA) showed clinical benefit in the threshold range of 0.07～1.0.Conclusion:The nomogram constructed based on CT combined with abdominal fat quantification indexes has high accuracy in predicting RCC invasiveness and significant clinical benefit.

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安徽省卫健委自然科学基金项目（编号：AHWJ2022b100）
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