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| 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 | |
| 引用本文： | 韩冬,张喜荣,贾永军,任革,吕蕊花,史琳娜,贺太平. 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[J]. 肿瘤防治研究, 2021, 48(1): 55-59. DOI: 10.3971/j.issn.1000-8578.2021.20.0440 |
| 作者姓名： | 韩冬 张喜荣 贾永军 任革 吕蕊花 史琳娜 贺太平 |
| 作者单位： | 1. 712000 咸阳，陕西中医药大学附属医院医学影像科；2. 712000 咸阳，陕西中医药大学医学技术学院 |
| 基金项目： | 陕西中医药大学学科创新团队建设项目（2019-YS04） |
| 摘 要： | 目的 基于增强CT构建鉴别肾透明细胞癌(ccRCC)ISUP分级的神经网络模型.方法 收集本单位病理确诊的ccRCC患者131例,ISUP低级别92例、高级别39例.按5:5分层抽样将患者分为训练集和验证集.由影像科医师对ccRCC增强CT图像进行评价.对患者一般特征及增强CT特征采用递归特征消除(RFE)进行降维,用... |
| 关 键 词： | 多层螺旋计算机体层摄影术 肾肿瘤 肿瘤分级 神经网络 |
| 收稿时间： | 2020-04-29 |
A Neural Network Model Based on Enhanced CT for Distinguishing ISUP Grade of Clear Cell Renal Cell Carcinoma |
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| HAN Dong,ZHANG Xirong,JIA Yongjun,REN Ge,LYU Ruihua,SHI Linna,HE Taiping. A Neural Network Model Based on Enhanced CT for Distinguishing ISUP Grade of Clear Cell Renal Cell Carcinoma[J]. Cancer Research on Prevention and Treatment, 2021, 48(1): 55-59. DOI: 10.3971/j.issn.1000-8578.2021.20.0440 | |
| Authors: | HAN Dong ZHANG Xirong JIA Yongjun REN Ge LYU Ruihua SHI Linna HE Taiping |
| Affiliation: | 1. Department of Medical Imaging, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China; 2. Department of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang 712000, China |
| 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. |
| Keywords: | Multidetector computed tomography Kidney neoplasms Neoplasm grading Neural networks |
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