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  • Title: Nomogram for predicting cancer-specific survival of patients with clear-cell renal cell carcinoma: a SEER-based population study.
    Author: Wang J, Wen Q, Wang X, Liu C, Zhao K, Li Y, Yang J, Liang X.
    Journal: Gen Physiol Biophys; 2022 Nov; 41(6):591-601. PubMed ID: 36454119.
    Abstract:
    This study was aimed to develop a nomogram for predicting the cancer-specific survival (CSS) of patients with clear-cell renal cell carcinoma (ccRCC). Based on the Surveillance, Epidemiology, and End Results (SEER) database, 24,477 patients diagnosed with ccRCC between 2010 and 2015 were collected. They were randomly divided into a training cohort (n = 17,133) and a validation cohort (n = 7,344). Univariate and multivariate Cox regression analyses were performed in the training cohort to identify independent prognostic factors for construction of nomogram. Then, the nomogram was used to predict the 3- and 5-year CSS. The performance of nomogram was evaluated by using concordance index (C-index), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA). Moreover, the nomogram and tumor node metastasis (TNM) staging system (AJCC 7th edition) were compared. Eleven variables were screened to develop the nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) and the calibration plots indicated satisfactory ability of the nomogram. Compared with the AJCC 7th edition of TNM stage, C-index, NRI, and IDI showed that the nomogram had improved performance. Furthermore, the 3- and 5-year DCA curves of nomogram yielded more net benefits than the AJCC 7th edition of TNM stage in both the training and validation sets. We developed and validated a nomogram for predicting the CSS of patients with ccRCC, which was more precise than the AJCC 7th edition of TNM staging system.
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