CASIA OpenIR
Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma
Zhou, Hongyu1,2,4; Mao, Haixia3; Dong, Di2,4; Fang, Mengjie2,4; Gu, Dongsheng2,4; Liu, Xueling3; Xu, Min3; Yang, Shudong5; Zou, Jian6; Yin, Ruohan7; Zheng, Hairong1,4; Tian, Jie2,4,8,9; Pan, Changjie7; Fang, Xiangming3; Zhou, Hongyu10,11,13; Mao, Haixia12; Dong, Di11,13; Fang, Mengjie11,13; Gu, Dongsheng11,13; Liu, Xueling12; Xu, Min12; Yang, Shudong14; Zou, Jian15; Yin, Ruohan16; Zheng, Hairong10,13; Tian, Jie11,13,17,18; Pan, Changjie16; Fang, Xiangming12
Source PublicationANNALS OF SURGICAL ONCOLOGY
ISSN1068-9265
2020-05-18
Volume27Issue:10Pages:4057–4065
Abstract

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.

KeywordClear Cell Renal Cell Carcinoma
DOI10.1245/s10434-020-08255-6
WOS KeywordCLASSIFICATION ; DIAGNOSIS ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaOncology ; Surgery
WOS SubjectOncology ; Surgery
WOS IDWOS:000533809900002
PublisherSPRINGER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39457
Collection中国科学院自动化研究所
Corresponding AuthorZheng, Hairong; Zheng, Hairong
Affiliation1.Chinese Acad Sci, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Nanjing Med Univ, Wuxi Peoples Hosp, Dept Radiol, Wuxi, Jiangsu, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Nanjing Med Univ, Wuxi Peoples Hosp, Dept Pathol, Wuxi, Jiangsu, Peoples R China
6.Nanjing Med Univ, Wuxi Peoples Hosp, Ctr Clin Res, Wuxi, Jiangsu, Peoples R China
7.Nanjing Med Univ, Dept Radiol, Changzhou Peoples Hosp 2, Changzhou, Jiangsu, Peoples R China
8.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Me, Sch Med, Beijing, Peoples R China
9.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
10.Chinese Acad Sci, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
11.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
12.Nanjing Med Univ, Wuxi Peoples Hosp, Dept Radiol, Wuxi, Jiangsu, Peoples R China
13.Univ Chinese Acad Sci, Beijing, Peoples R China
14.Nanjing Med Univ, Wuxi Peoples Hosp, Dept Pathol, Wuxi, Jiangsu, Peoples R China
15.Nanjing Med Univ, Wuxi Peoples Hosp, Ctr Clin Res, Wuxi, Jiangsu, Peoples R China
16.Nanjing Med Univ, Dept Radiol, Changzhou Peoples Hosp 2, Changzhou, Jiangsu, Peoples R China
17.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Me, Sch Med, Beijing, Peoples R China
18.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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,2020,27(10):4057–4065.
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,27(10),4057–4065.
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 27.10(2020):4057–4065.
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