AUTHOR=Tang Jie , Wang Jinkui , Pan Xiudan , Liu Xiaozhu , Zhao Binyi 

TITLE=A Web-Based Prediction Model for Cancer-Specific Survival of Middle-Aged Patients With Non-metastatic Renal Cell Carcinoma: A Population-Based Study

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.822808

DOI=10.3389/fpubh.2022.822808

ISSN=2296-2565

ABSTRACT=Background: Renal cell carcinoma(RCC) is one of the most common cancers in middle-aged patients. Our aim was to establish a new nomogram for predicting cancer-specific survival(CSS) in middle-aged patients with non-metastatic renal cell carcinoma(nmRCC).
Methods: The clinicopathological information of all patients from 2010 to 2018 was downloaded from SEER database. These patients were randomly assigned to training set (70%) and validation set (30%). Univariate and multivariate COX regression analyses were used to identify independent risk factors for CSS in middle-aged patients with nmRCC in the training set. Based on these independent risk factors, a new nomogram was constructed to predict 1-, 3-, and 5-year CSS in middle-aged patients with nmRCC. Then, the consistency index (C-index), calibration curve, and Area under receiver operating curve(AUC) were used to validate the accuracy and discrimination of the model. Decision curve analysis (DCA) was used to validate the clinical application value of the model.
Results: A total of 27073 patients were included in the study. These patients were randomly divided into training set (N=18990) and validation set (N=8083). In the training set, univariate and multivariate Cox regression analysis indicated that age, sex, tumor histological grade, T stage, tumor size, and surgical method are independent risk factors for CSS of patients. A new nomogram was constructed to predict the 1-, 3-, and 5-year CSS of patients. The C-index of the training set and validation set were 0.818 (95% CI: 0.802-0.834) and 0.802 (95% CI: 0.777-0.827), respectively. The 1 -, 3 -, and 5-year AUC for the training set and validation set ranged from 77.7-80.0. The calibration curves of the training set and the validation set indicated that the predicted value is highly consistent with the actual observation value, indicating that the model has good accuracy. DCA also suggested that the model has potential clinical application value.
Conclusion: We have constructed a new nomogram to predict the CSS of middle-aged patients with nmRCC. This model has good accuracy and reliability and can assist doctors and patients in clinical decision-making.