Guangjie Yang1, Pei Nie2, Lei Yan1, Mingxin Zhang3, Yangyang Wang1, Lianzi Zhao4, Mingyao Li5, Fei Xie3, Haizhu Xie6, Xianjun Li7, Fawei Xiang7, Nan Wang8, Nan Cheng9, Xia Zhao10, Ning Wang11, Yicong Wang12, Chengcheng Chen13, Canhua Yun14, Jingjing Cui15, Shaofeng Duan16, Ran Zhang17, Dapeng Hao18, Ximing Wang19, Zhenguang Wang20, Haitao Niu21. 1. Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China. 2. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China. 3. Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China. 4. Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China. 5. Department of Radiation Oncology, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, China. 6. Department of Radiology, Yantai Yuhuangding Hospital, The Affiliated Hospital of Qingdao University, Yantai, Shandong, China. 7. Department of Radiology, Weifang People's Hospital, Weifang, Shandong, China. 8. Department of Nuclear Medicine, Liaocheng People's Hospital, Liaocheng, Shandong, China. 9. Department of Medical Imaging, The Affiliated Hospital of Jining Medical College, Jining, Shandong, China. 10. Department of Radiology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China. 11. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China. 12. Department of Nuclear Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China. 13. Department of Radiology, Rizhao People's Hospital, Rizhao, Shandong, China. 14. Department of Nuclear Medicine, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China. 15. Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China. 16. GE Healthcare, Precision Health Institution, Shanghai, China. 17. Huiying Medical Technology Co. Ltd, Beijing, China. 18. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China. haodp_2009@163.com. 19. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China. wxming369@163.com. 20. Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China. doctorwzg2002@hotmail.com. 21. Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China. niuht0532@126.com.
Abstract
PURPOSE: Tumor heterogeneity, which is associated with poor outcomes, has not been exhibited in the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade and Necrosis (SSIGN) scores. Radiomics allows an in-depth characterization of heterogeneity across the tumor, but its incremental value to the existing prognostic models for clear cell renal cell carcinoma (ccRCC) outcome is unknown. The purpose of this study was to evaluate the association between the radiomics-based tumor heterogeneity and postoperative risk of recurrence in localized ccRCC, and to assess its incremental value to UISS and SSIGN. METHODS: A multicenter 866 ccRCC patients derived from 12 Chinese hospitals were studied. The endpoint was recurrence-free survival (RFS). A CT-based radiomics signature (RS) was developed and assessed in the whole cohort and in the subgroups stratified by UISS and SSIGN. Two combined nomograms, the R-UISS (combining RS and UISS) and R-SSIGN (combining RS and SSIGN), were developed. The incremental value of RS to UISS and SSIGN in RFS prediction was evaluated. R statistical software was used for statistics. RESULTS: Patients with low radiomics scores were 4.44 times more likely to experience recurrence than those with high radiomics scores (P<0.001). Stratified analysis suggested the association is significant among low- and intermediate-risk patients identified by UISS and SSIGN. The R-UISS and R-SSIGN showed better predictive capability than UISS and SSIGN did with higher C-indices (R-UISS vs. UISS, 0.74 vs. 0.64; R-SSIGN vs. SSIGN, 0.78 vs. 0.76) and higher clinical net benefit. CONCLUSIONS: The radiomics-based tumor heterogeneity can predict outcome and add incremental value to the existing prognostic models in localized ccRCC patients. Incorporating radiomics-based tumor heterogeneity in ccRCC prognostic models may provide the opportunity to better surveillance and adjuvant clinical trial design.
PURPOSE: Tumor heterogeneity, which is associated with poor outcomes, has not been exhibited in the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade and Necrosis (SSIGN) scores. Radiomics allows an in-depth characterization of heterogeneity across the tumor, but its incremental value to the existing prognostic models for clear cell renal cell carcinoma (ccRCC) outcome is unknown. The purpose of this study was to evaluate the association between the radiomics-based tumor heterogeneity and postoperative risk of recurrence in localized ccRCC, and to assess its incremental value to UISS and SSIGN. METHODS: A multicenter 866 ccRCC patients derived from 12 Chinese hospitals were studied. The endpoint was recurrence-free survival (RFS). A CT-based radiomics signature (RS) was developed and assessed in the whole cohort and in the subgroups stratified by UISS and SSIGN. Two combined nomograms, the R-UISS (combining RS and UISS) and R-SSIGN (combining RS and SSIGN), were developed. The incremental value of RS to UISS and SSIGN in RFS prediction was evaluated. R statistical software was used for statistics. RESULTS: Patients with low radiomics scores were 4.44 times more likely to experience recurrence than those with high radiomics scores (P<0.001). Stratified analysis suggested the association is significant among low- and intermediate-risk patients identified by UISS and SSIGN. The R-UISS and R-SSIGN showed better predictive capability than UISS and SSIGN did with higher C-indices (R-UISS vs. UISS, 0.74 vs. 0.64; R-SSIGN vs. SSIGN, 0.78 vs. 0.76) and higher clinical net benefit. CONCLUSIONS: The radiomics-based tumor heterogeneity can predict outcome and add incremental value to the existing prognostic models in localized ccRCC patients. Incorporating radiomics-based tumor heterogeneity in ccRCC prognostic models may provide the opportunity to better surveillance and adjuvant clinical trial design.