Risk Factors, Prognostic Factors, and Nomograms for Bone Metastasis in Patients with Newly Diagnosed Clear Cell Renal Cell Carcinoma: A Large Population-Based Study

Abstract

BackgroundThis study aimed to investigate risk factors and prognostic factors in patients with clear cell renal cell carcinoma (ccRCC) with bone metastasis (BM) and establish nomograms to provide a quantitative prediction of the risk of BM and survival probability.MethodsThe clinicopathological characteristics of patients with ccRCC between January 2010 and December 2015 were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Independent factors for BM in ccRCC patients were identified using univariate and multivariate logistic regression analyses. Prognostic factors for predicting cancer-specific death were evaluated using univariate and multivariate analyses based on a competing risk regression model. We then constructed a diagnostic nomogram and a prognostic nomogram. The two nomograms were evaluated using calibration curves, receiver operating characteristic curves, and decision curve analysis.ResultsOur study included 34,659 patients diagnosed with ccRCC in the SEER database, with 1,415 patients who presented with bone metastasis. Risk factors for BM in patients with ccRCC included age, stage T, stage N, brain metastasis, liver metastasis, lung metastasis, tumor size, and laterality. Independent prognostic factors for patients with ccRCC patients with BM were Fuhrman grade, tumor size, T stage, N stage, brain metastases, lung metastasis, and surgery. For the diagnostic nomogram, the area under the curve values in the training and testing cohorts were 0.863 (95% CI, 0.851–0.875) and 0.859 (95% CI, 0.839–0.878), respectively. In the prognostic cohort, the area under the curve values for 1-, 2-, and 3-year cancer-specific survival rates in the training cohort were 0.747, 0.774, and 0.780, respectively, and 0.671, 0.706, and 0.696, respectively, in the testing cohort. Through calibration curves and decision curve analyses, the nomograms displayed excellent performance.ConclusionsSeveral factors related to the development and prognosis of BM in patients with ccRCC were identified. The nomograms constructed in this study are expected to become effective and precise tools for clinicians to improve cancer management.

A dynamic visualization clinical tool constructed and validated based on the SEER database for screening the optimal surgical candidates for bone metastasis in primary kidney cancer

“…Out of the 1963 KCBM patients in this study, 693 (35.3%) had metastases in the lymphatic system at the time of initial diagnosis. Higher N stage implies more sites and number of lymph nodes invaded by the primary tumor, which undoubtedly further compromises the survival prognosis of KCBM patients 52 .…”

Distinct spatial landscapes in clear-cell renal cell carcinoma as revealed by whole transcriptome analysis

“… 19 Validated models available in clinical practice and machine learning approaches are being evaluated to predict disease recurrence and metastatic progression following surgical resection of ccRCC, but there is still a need for improvement from the integration of novel biological biomarkers. 20 , 21 In this sense, a comprehensive analysis of tumor immune infiltration and the tumor microenvironment can facilitate development of biomarkers in ccRCC. 22 , 23 …”

Individualized assessment predictive models for risk and overall survival in elderly patients of primary kidney cancer with bone metastases: A large population-based study

“…T stage often affect the prognosis of survival in elderly KCBM patients, which is understandable because higher T stage and may mean more vascular and tissue invasion. As time progresses, the division of tumor cells would get further out of control ( 34 ). All of these might promote the development of metastatic disease.…”