Hongyu Zhou1,2,3, Haixia Mao4, Di Dong2,3, Changjie Pan5, Mengjie Fang2,3, Dongsheng Gu2,3, Xueling Liu4, Min Xu4, Shudong Yang6, Jian Zou7, Ruohan Yin5, Hairong Zheng8,9, Jie Tian10,11,12,13, Xiangming Fang14. 1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China. 2. CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. 3. University of Chinese Academy of Sciences, Beijing, China. 4. Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, Jiangsu, China. 5. Department of Radiology, Changzhou No. 2 People's Hospital, Nanjing Medical University, Changzhou, Jiangsu, China. 6. Department of Pathology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, Jiangsu, China. 7. Center of Clinical Research, Wuxi People's Hospital, Nanjing Medical University, Wuxi, Jiangsu, China. 8. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China. hr.zheng@siat.ac.cn. 9. University of Chinese Academy of Sciences, Beijing, China. hr.zheng@siat.ac.cn. 10. CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. jie.tian@ia.ac.cn. 11. University of Chinese Academy of Sciences, Beijing, China. jie.tian@ia.ac.cn. 12. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China. jie.tian@ia.ac.cn. 13. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China. jie.tian@ia.ac.cn. 14. Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, Jiangsu, China. xiangming_fang@njmu.edu.cn.
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.
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.
Authors: Natalie L Demirjian; Bino A Varghese; Steven Y Cen; Darryl H Hwang; Manju Aron; Imran Siddiqui; Brandon K K Fields; Xiaomeng Lei; Felix Y Yap; Marielena Rivas; Sharath S Reddy; Haris Zahoor; Derek H Liu; Mihir Desai; Suhn K Rhie; Inderbir S Gill; Vinay Duddalwar Journal: Eur Radiol Date: 2021-11-10 Impact factor: 5.315
Authors: Claudia-Gabriela Moldovanu; Bianca Boca; Andrei Lebovici; Attila Tamas-Szora; Diana Sorina Feier; Nicolae Crisan; Iulia Andras; Mircea Marian Buruian Journal: J Pers Med Date: 2020-12-23