Yan Zhu1, Yao-Rui Zhao2,3, Xian-Fa Yang1, Mao-Ti Wei4, Yuan-Jie Niu3, Ji-Wu Chang3, Ai-Xiang Wang3, Xuan Liang5. 1. Graduate School of Tianjin Medical University, Tianjin, China. 2. Department of Urology, Characteristic Medical Center of People's Armed Police, Tianjin, China. 3. Tianjin Institute of Urology, Second Hospital of Tianjin Medical University, Tianjin, China. 4. Department of Epidemiology, Logistics University of Chinese People's Armed Police Force, Tianjin, China. 5. School of Public Health, Tianjin Medical University, Tianjin, China.
Abstract
OBJECTIVES: To develop a predictive model for the oncological outcomes of clear cell renal cell carcinoma in a Chinese population. METHODS: A retrospective study of 1108 patients with clear cell renal cell carcinoma who underwent nephrectomy or partial nephrectomy between January 2006 and December 2013 was carried out. Recurrence-free survival was calculated using Kaplan-Meier analysis. Differences between the groups were compared using the log-rank test. Cox proportional hazard regression was used to test associations between features and outcomes. The discriminative ability of the models was validated using Harrell's concordance index and bootstrapping. RESULTS: Overall, 942 patients who met the inclusion criteria had been followed. The median follow-up period was 72 months (range 1-143 months). Multivariate analysis showed that age, Eastern Cooperative Oncology Group performance status, preoperative platelet count, neutrophil-to-lymphocyte ratio, tumor size, 2010 tumor stage (pT3 and pT4) and Fuhrman nuclear grade were independent risk factors affecting recurrence-free survival in clear cell renal cell carcinoma patients (P < 0.05). These factors were assigned to develop a new model. The patients were divided into three groups based on the risk of recurrence. The difference among the prognoses of patients in the three groups was statistically significant (P < 0.05). The concordance index for our new model and that for Leibovich's 2018 model were 0.791 and 0.750, respectively. CONCLUSIONS: In the present study, the new model has a higher concordance index than does Leibovich's 2018 model of clear cell renal cell carcinoma in the Asian population, with no added pain for patients. This new model might be an appropriate risk stratification tool for clinical work.
OBJECTIVES: To develop a predictive model for the oncological outcomes of clear cell renal cell carcinoma in a Chinese population. METHODS: A retrospective study of 1108 patients with clear cell renal cell carcinoma who underwent nephrectomy or partial nephrectomy between January 2006 and December 2013 was carried out. Recurrence-free survival was calculated using Kaplan-Meier analysis. Differences between the groups were compared using the log-rank test. Cox proportional hazard regression was used to test associations between features and outcomes. The discriminative ability of the models was validated using Harrell's concordance index and bootstrapping. RESULTS: Overall, 942 patients who met the inclusion criteria had been followed. The median follow-up period was 72 months (range 1-143 months). Multivariate analysis showed that age, Eastern Cooperative Oncology Group performance status, preoperative platelet count, neutrophil-to-lymphocyte ratio, tumor size, 2010 tumor stage (pT3 and pT4) and Fuhrman nuclear grade were independent risk factors affecting recurrence-free survival in clear cell renal cell carcinomapatients (P < 0.05). These factors were assigned to develop a new model. The patients were divided into three groups based on the risk of recurrence. The difference among the prognoses of patients in the three groups was statistically significant (P < 0.05). The concordance index for our new model and that for Leibovich's 2018 model were 0.791 and 0.750, respectively. CONCLUSIONS: In the present study, the new model has a higher concordance index than does Leibovich's 2018 model of clear cell renal cell carcinoma in the Asian population, with no added pain for patients. This new model might be an appropriate risk stratification tool for clinical work.