Survival nomogram for patients with bone metastatic renal cell carcinoma: A population-based study

Abstract

Purpose: Increased attention has been focused on the survival of renal cell carcinoma (RCC) patients with bone metastasis. This study proposed to establish and evaluate a nomogram for predicting the overall survival (OS) and cancer-specific survival (CSS) of RCC patients with bone metastasis. Materials and Methods: RCC patients with bone metastasis between 2010 and 2015 were captured from the surveillance, epidemiology and end results (SEER) database. Univariate and multivariate cox regressions were performed to assess the effects of clinical variables on OS and CSS. The nomogram based on the Cox hazards regression model was developed. Concordance index (C-index) and calibration curve were performed to evaluate the accuracy of nomogram models, receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were conducted to assess the predict performance. Results: A total of 2.471 eligible patients were enrolled in this study. The patients were assigned to primary (n=1.672) and validation (n=799) cohorts randomly. The 1-, 2-, and 3-year OS and CSS nomogram models were constructed based on age at diagnosis, sex, marital status, pathological grade, T-stage, N-stage, brain/liver/lung metastasis, surgery, radiotherapy and chemotherapy. The c for OS and CSS prediction was 0.730 (95% confidence interval [CI]: 0.719-0.741) and 0.714 (95%CI:0.702-0.726). The calibration curves showed significant agreement between nomogram models and actual observations. ROC and DCA indicated nomograms had better predict performance. Conclusions: The nomograms for predicting prognosis provided an accurate prediction of OS and CSS in RCC patients with bone metastasis, and contributed clinicians to optimize individualized treatment plans.

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

“…Interestingly, Becerra and colleagues (18) found that the mutated genes were not consistent in the primary tumor and metastatic samples, enlightening us that metastasis may possess genetic characteristics that are distinct from the primary one. Meanwhile, previous reports confirmed that BMs was an independent risk factor for mRCC prognosis (5,8,14,15). Thus, identifying patients at high risk for BMs provides a sturdy basis for guiding treatment (timing and procedures of surgery, chemotherapy, and radiation therapy).…”

“…In contrast to the high threshold of background knowledge required that clinicians need to obtain in order to use artificial intelligence, a simplicity and intuitiveness of nomogram can provide the same insightful analysis to help clinicians decide the clinical treatment. Several literatures have developed nomograms to predict prognosis targeting BMs from mRCC ( 14 , 15 ). However, the current study constructs the first predictive model to predict risk factors for BMs, facilitating clinicians in making individualized clinical decisions and assessing patients’ long-term prognosis.…”

Prognostic Value of the Systemic Inflammatory Response Index in Patients Undergoing Radical Cystectomy for Bladder Cancer: A Population-Based Study

“…In recent years, increasing studies revealed that nomograms as an extremely effective prediction model can predict the survival of cancer patients (32). In this study, the results of the ROC and DCA indicated that nomograms have a better predictive performance compared to the TNM-stage.…”

Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis

“…And CDK2 was associated in the P13K-AKT ( Li Z. et al, 2021 ) signaling pathway in LUAD, inducing cell proliferation ( Wu et al, 2020 ). Combing with clinical data, we constructed the forest map and Nomogram model ( Cao et al, 2021b ; Jang et al, 2021 ; Wang R. R. et al, 2021 ; Wang K. et al, 2021 ) of clinical characteristics. Through the features of patients, we can calculate the total score to evaluate the prognosis of patients.…”