Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma
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ORIGINAL ARTICLE – UROLOGIC ONCOLOGY
Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma Hongyu Zhou, MS1,2,4, Haixia Mao, MD3, Di Dong, PhD2,4, Mengjie Fang, MS2,4, Dongsheng Gu, MS2,4, Xueling Liu, MD3, Min Xu, MD3, Shudong Yang, MD5, Jian Zou, PhD6, Ruohan Yin, MD7, Hairong Zheng, PhD1,4, Jie Tian, PhD2,4,8,9, Changjie Pan, MD7, and Xiangming Fang, MD3 1
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China; 2CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; 3Department of Radiology, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, Jiangsu, China; 4University of Chinese Academy of Sciences, Beijing, China; 5Department of Pathology, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, Jiangsu, China; 6Center of Clinical Research, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, Jiangsu, China; 7Department of Radiology, Changzhou No. 2 People’s Hospital, Nanjing Medical University, Changzhou, Jiangsu, China; 8Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China; 9Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, China
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.
Hongyu Zhou, Haixia Mao, Di Dong, Changjie Pan have contributed equally to this work.
Electronic supplementary material The online version of this article (https://doi.org/10.1245/s10434-020-08255-6) contains supplementary material, which is available to authorized users. Society of Surgical Oncology 2020 First Received: 8 May 2019 H. Zheng, PhD e-mail: [email protected] J. Tian, PhD e-mail: [email protected] X. Fang, MD e-mail: [email protected]
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
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