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Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma

文献类型：期刊论文

作者Zhou, Hongyu7,8,9; Mao, Haixia1; Dong, Di7,8; Fang, Mengjie7,8; Gu, Dongsheng7,8; Liu, Xueling1; Xu, Min1; Yang, Shudong6; Zou, Jian5; Yin, Ruohan4
刊名ANNALS OF SURGICAL ONCOLOGY
出版日期2020-05-18
页码9
ISSN号1068-9265
DOI10.1245/s10434-020-08255-6
通讯作者Zheng, Hairong(hr.zheng@siat.ac.cn)
英文摘要Background and Purpose Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC. Methods Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models. Results The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data). Conclusion The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.
WOS关键词CLASSIFICATION ; DIAGNOSIS ; MODEL
资助项目National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2016YFC0103803] ; National Key R&D Program of China[2016YFC0103001] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[812716298] ; National Natural Science Foundation of China[81271629] ; National Natural Science Foundation of China[81227901] ; Beijing Natural Science Foundation[L182061] ; Wuxi Medical Innovation Team Program[CXTD002] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Youth Innovation Promotion Association CAS[2017175]
WOS研究方向Oncology ; Surgery
语种英语
出版者SPRINGER
WOS记录号WOS:000533809900002
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Wuxi Medical Innovation Team Program ; Bureau of International Cooperation of Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/39456]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Zheng, Hairong
作者单位1.Nanjing Med Univ, Wuxi Peoples Hosp, Dept Radiol, Wuxi, Jiangsu, Peoples R China
2.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Me, Sch Med, Beijing, Peoples R China
4.Nanjing Med Univ, Dept Radiol, Changzhou Peoples Hosp 2, Changzhou, Jiangsu, Peoples R China
5.Nanjing Med Univ, Wuxi Peoples Hosp, Ctr Clin Res, Wuxi, Jiangsu, Peoples R China
6.Nanjing Med Univ, Wuxi Peoples Hosp, Dept Pathol, Wuxi, Jiangsu, Peoples R China
7.Univ Chinese Acad Sci, Beijing, Peoples R China
8.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
9.Chinese Acad Sci, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Hongyu,Mao, Haixia,Dong, Di,et al. Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma[J]. ANNALS OF SURGICAL ONCOLOGY,2020:9.
APA Zhou, Hongyu.,Mao, Haixia.,Dong, Di.,Fang, Mengjie.,Gu, Dongsheng.,...&Fang, Xiangming.(2020).Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma.ANNALS OF SURGICAL ONCOLOGY,9.
MLA Zhou, Hongyu,et al."Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma".ANNALS OF SURGICAL ONCOLOGY (2020):9.

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来源：自动化研究所

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