Establishment of WHO/ISUP grading prediction model for renal clear cell carcinoma based on CT radiomics
10.3760/cma.j.issn.1000-6702.2019.12.003
- VernacularTitle: 基于CT影像组学构建肾透明细胞癌WHO/ISUP分级的预测模型
- Author:
Dong HAN
1
;
Taiping HE
1
;
Hongpei WU
2
;
Nan YU
1
;
Xirong ZHANG
1
;
Ge REN
1
;
Yong YU
1
Author Information
1. Department of Radiology, Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, Xianyang 712021, China
2. Department of Pathology, Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, Xianyang 712021, China
- Publication Type:Clinical Trail
- Keywords:
Carcinoma, renal cell;
Radiomics;
Pathological grading;
Predictive model
- From:
Chinese Journal of Urology
2019;40(12):889-894
- CountryChina
- Language:Chinese
-
Abstract:
Objective:A predictive model of WHO/ISUP grading of renal clear cell carcinoma was constructed based on CT radiomics.
Methods:The clinical data of 104 patients with ccRCC confirmed by operation or biopsy from March 2014 to December 2018 in the Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine were retrospectively analyzed. There were 70 males and 34 females, and the age was 61.2±11.7 years. The patients were randomly divided into development cohort (73 cases) and validation cohort (31 cases) by stratified sampling according to 7∶3 ratio. According to the WHO/ISUP pathological grading criteria of renal cancer in 2016, Ⅰ and Ⅱ were defined as low-grade group, Ⅲ and Ⅳ were defined as high-grade group. The radiomics features of ccRCC were calculated in cortical phase images of CT enhanced scanning. LASSO regression was used to reduce the radiomics feature dimensionality in the training group, and to establish radiomics risk scores. The binary logistic regression was used to build the prediction model, which was used in the validation group. Bootstrap method was used to validate the model of training and validation group. AUC, sensitivity and specificity were calculated respectively. Hosmer-Lemeshow goodness-of-fit test was used to evaluate model calibration degree.
Results:After dimensionality reduction, the radiomics risk score of ccRCC was established. The low and high-level risk scores of the training group were -2.49±1.73 and 1.23±2.17, with significant difference (t=-7.785, P < 0.01). The binary logistic regression multivariate analysis showed that the radiomics risk score was an independent risk factor in identifying low or high-grade ccRCC with odds ratio of (OR=3.576, 95%CI 1.964~6.513). The predictive model was Y=1/[1+ exp(-Z)], Z=1.274×radiomics risk score+ 0.072. The AUC of radiomics risk score in training group was 0.940 (95%CI 0.883-0.998) with 95.5% sensitivity and 88.2% specificity after internal verification by Bootstrap method, and good Hosmer-Lemeshow goodness-of-fit test (χ2=4.463, P>0.05). The low and high-level risk scores of the Validation group were -2.27±2.02 and 0.82±2.08, with significant difference (t=-3.832, P<0.01). The AUC in validation group was 0.859(95%CI 0.723-0.995) with 77.8% sensitivity and 81.8% specificity, and with good Hosmer-Lemeshow goodness-of-fit test (χ2=14.554, P=0.068) as well.
Conclusions:The prediction model based on CT radiomics has high accuracy in predicting high or low grade of ccRCC.
