AUTHOR=Dong Shengtao , Yang Hua , Tang Zhi-Ri , Ke Yuqi , Wang Haosheng , Li Wenle , Tian Kang 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.731905

DOI=10.3389/fonc.2021.731905

ISSN=2234-943X

ABSTRACT=Background: Bone is a common target of metastasis in renal cell carcinoma (RCC), and accurately predicting the risk of bone metastasis (BMs) facilitates risk stratification and precision medicine in RCC.
Methods: Patients diagnosed with RCC were extracted from the surveillance, epidemiology and end results (SEER) database to comprise the training group from 2010 to 2016, and the validation group was drawn from an academic medical center. Univariate and multivariate logistic regression analyses explored the statistical relationships between the included variables and BM. Statistically significant risk factors were applied to develop nomogram. Calibration plots and receiver operating characteristic (ROC) curves were used to verify the predictive performance of the nomogram. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of RCC with and without BMs. A convenient web calculator was provided for users via “shiny” package.
Results: A total of 9,698 patients were recruited in this study, of which 8,958 training group cases and 740 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage and sequence number. The calibration plots confirmed good agreement between the prediction model and the actual results. ROC curves and the area under curve (AUC) values indicated that the nomogram had more accurate predictive power compared to each individual variable.
Conclusions: Sex, pathological grade, T-stage, N-stage, and sequence number were confirmed to be highly correlated with the occurrence of BMs in RCC. The comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk RCC cases and provide personalized treatment options.