Establishment of nuclear grade prediction model for T1 clear cell renal cell carcinoma based on CT features and radiomics
10.3760/cma.j.cn112152-20240615-00257
- VernacularTitle:基于CT征象和影像组学特征构建T1期肾透明细胞癌核分级预测模型
- Author:
Caiyong ZHAO
1
;
Chao CHEN
;
Weiwei LI
;
Jie WANG
;
Rumeng ZHENG
;
Feng CUI
Author Information
1. 杭州市中医院影像科，杭州310007
- Publication Type:Journal Article
- Keywords:
Clear cell carcinoma of kidney;
Pathological grade;
Computed tomography;
Radiomics;
Nomogram
- From:
Chinese Journal of Oncology
2025;47(2):168-174
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the clinical value of the prediction models constructed by CT based imaging features and radiomics for World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading in pre-operative patients with T1 clear cell renal cell carcinoma (ccRCC).Methods:Ninety patients with ccRCC diagnosed at Hangzhou Hospital of Traditional Chinese Medicine from January 2016 to December 2023 were enrolled as the training set, and 43 patients diagnosed at the Sir Run Run Shaw Hospital from January 2017 to December 2018 were enrolled as the external validation set. According to the WHO/ISUP grading system, grades Ⅰ and Ⅱ were defined as the low grade group, and grades Ⅲ and Ⅳ were defined as the high grade group. In the training set, 64 patients were in the low grade group and 26 patients in the high grade group. In the external validation set, 33 patients were in the low grade group and 10 patients in the high grade group. The multivariate logistic regression was used to establish an imaging factor model based on CT imaging features in the training set. The 3-dimensional regions of interest were manually contoured at the cortical phase of enhanced CT, and the radiomics features were extracted. Linear correlation between features and L1 regularization were used for feature selection, and then linear support vector classification was used to construct the radiomics model. After that, a combined diagnostic model of nomogram combining the radiomics score and imaging factors was constructed using multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve was used to evaluate the effectiveness of each model. The Delong test was used for comparison of the areas under the ROC curve.Results:The imaging factor model, the radiomics model, and the combined diagnostic model of nomogram were successfully constructed to predict the WHO/ ISUP grading in stage T1 ccRCC. The AUC value of the imaging factor model in the training and validation sets was 0.742 (95% CI: 0.623-0.860) and 0.664 (95% CI: 0.448-0.879), respectively. The AUC values of the radiomics model in the two sets were 0.914 (95% CI: 0.844-0.983) and 0.879 (95% CI: 0.718-1.000), and of the combined diagnostic model of nomogram in the two sets were 0.929 (95% CI: 0.858-0.999) and 0.882 (95% CI: 0.710-1.000), respectively. The AUCs of the radiomics model and combined diagnostic model of nomogram were significantly higher than that of the imaging factor model (both P＜0.05). There was no statistical difference in the AUCs between the combined diagnostic model of nomogram and the radiomics model (both P＞0.05). Conclusion:The CT-based radiomics model and combined diagnostic model of nomogram incorporating radiomics signature and imaging features showed favorable predictive efficacy for the preoperative prediction of WHO/ISUP grading in stage T1 ccRCC.
