A Neural Network Model Based on Enhanced CT for Distinguishing ISUP Grade of Clear Cell Renal Cell Carcinoma
10.3971/j.issn.1000-8578.2021.20.0440
- VernacularTitle:基于增强CT构建鉴别肾透明细胞癌ISUP分级的神经网络模型
- Author:
Dong HAN
1
;
Xirong ZHANG
;
Yongjun JIA
;
Ge REN
;
Ruihua LYU
;
Linna SHI
;
Taiping HE
Author Information
1. Department of Medical Imaging, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China
- Publication Type:Research Article
- Keywords:
Multidetector computed tomography;
Kidney neoplasms;
Neoplasm grading;
Neural networks
- From:
Cancer Research on Prevention and Treatment
2021;48(1):55-59
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a neural network model based on enhanced CT for distinguishing ISUP grade of clear cell renal cell carcinoma (ccRCC). Methods We collected 131 cases of ccRCC, with 92 cases of low ISUP grade and 39 cases of high ISUP grade. Patients were divided into training set and validation set according to 5:5 stratified sampling. The enhanced CT images of each ccRCC patient were evaluated by the radiologist. Recursive feature elimination (RFE) was used to reduce the dimension of patients' general features and enhanced CT features, which was used for neural network modeling and validation. Results Patients' general features and enhanced CT features were verified by RFE method and then reduced to 14 features. The top 5 features were growth pattern, necrosis, enlargement of lymph nodes, tumor size and capsule. The AUC of the neural network model based on these 5 features in training set was 0.8844 (95%CI: 0.8062-0.9626), sensitivity was 89.47% and specificity was 82.61%; and those in validation set were 0.7924 (95%CI: 0.6567-0.9280), 75.00% and 86.96%, respectively. Conclusion The neural network model of ccRCC ISUP grade based on enhanced CT has relatively high diagnostic efficiency.
