The value of nomogram based on clinical features and CT radiomics in predicting the grade of clear cell renal cell carcinoma
10.19405/j.cnki.issn1000-1492.2025.06.022
- Author:
Hongqing Zhu
1
;
Tao Zhang
2
;
Kangchen Gu
1
;
Xian Wang
3
;
Song Guan
1
;
Yan Yan
1
;
Wenjun Yao
1
Author Information
1. Dept of Radiology , The Second Afiliated Hospital of Anhui Medical University , Hefei 230601
2. Dept of Urology , The Second Afiliated Hospital of Anhui Medical University , Hefei 230601
3. Dept of Pathology , The Second Afiliated Hospital of Anhui Medical University , Hefei 230601
- Publication Type:Journal Article
- Keywords:
nomogram;
radiomics;
clear cell renal cell carcinoma;
WHO/ISUP grade;
computed tomography
- From:
Acta Universitatis Medicinalis Anhui
2025;60(6):1127-1133
- CountryChina
- Language:Chinese
-
Abstract:
Objective :To explore the utility of a nomogram integrating contrast-enhanced CT radiomics with clinical features in the preoperative prediction of WHO/ISUP grade for clear cell renal cell carcinoma(ccRCC).
Methods:A total of 214 patients with pathologically proven ccRCC who underwent enhanced CT scan before surgery were retrospectively included. According to the WHO/ISUP grade system, the cases were classified into low-grade(grades Ⅰ-Ⅱ) and high-grade(grades Ⅲ-Ⅳ), and then randomly divided into training and test set with a ratio of 4 ∶1. Regions of interest were segmented from both unenhanced and three-phase enhanced images, and radiomic features were extracted. Feature selection and dimensionality reduction were performed using Spearman rank correlation coefficients and LASSO regression, followed by the construction of the radiomic model with the KNN algorithm. Clinical and semantic imaging features were selected through univariate and multivariate analyses, and a clinical model was developed using the KNN algorithm. The clinical and radiomics signatures were used to construct a combined model and a nomogram was developed. The ROC curve and delong test were used to evaluate the diagnostic performance of the model, while calibration and decision curve analyses assessed its accuracy and clinical applicability.
Results:8 clinical features and 11 radiomic features were selected. The combined model, integrating these clinical and radiomics signatures, exhibited robust predictive performance with AUC values of 0.887 in the training set and 0.800 in the test set. The calibration curve demonstrated good consistency between the nomogram model and actual outcomes, while decision curve analysis indicated a favorable net benefit for the nomogram.
Conclusion :The nomogram constructed by combining radiomics and clinical signatures can provide evidence for preoperative prediction of ccRCC grade and guide clinical decision-making.
- Full text:2026030317031943610基于临床和CT影像组学构建列线图在术前预测肾透明细胞癌分级中的价值_朱宏庆.pdf
