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

BackgroundBone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.MethodsPatients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and E...

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Autores principales:	Shengtao Dong, Hua Yang, Zhi-Ri Tang, Yuqi Ke, Haosheng Wang, Wenle Li, Kang Tian
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	renal cell carcinoma bone metastasis nomogram web calculator predictive model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/131e0da47be742b3ad480754d20df11d
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spelling	oai:doaj.org-article:131e0da47be742b3ad480754d20df11d2021-12-01T01:28:03ZDevelopment and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer2234-943X10.3389/fonc.2021.731905https://doaj.org/article/131e0da47be742b3ad480754d20df11d2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.731905/fullhttps://doaj.org/toc/2234-943XBackgroundBone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.MethodsPatients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our 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 a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package.ResultsA total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs.ConclusionsThe comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.Shengtao DongShengtao DongHua YangZhi-Ri TangYuqi KeHaosheng WangWenle LiWenle LiKang TianFrontiers Media S.A.articlerenal cell carcinomabone metastasisnomogramweb calculatorpredictive modelNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	renal cell carcinoma bone metastasis nomogram web calculator predictive model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	renal cell carcinoma bone metastasis nomogram web calculator predictive model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Shengtao Dong Shengtao Dong Hua Yang Zhi-Ri Tang Yuqi Ke Haosheng Wang Wenle Li Wenle Li Kang Tian Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer
description	BackgroundBone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.MethodsPatients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our 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 a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package.ResultsA total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs.ConclusionsThe comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.
format	article
author	Shengtao Dong Shengtao Dong Hua Yang Zhi-Ri Tang Yuqi Ke Haosheng Wang Wenle Li Wenle Li Kang Tian
author_facet	Shengtao Dong Shengtao Dong Hua Yang Zhi-Ri Tang Yuqi Ke Haosheng Wang Wenle Li Wenle Li Kang Tian
author_sort	Shengtao Dong
title	Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer
title_short	Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer
title_full	Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer
title_fullStr	Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer
title_full_unstemmed	Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer
title_sort	development and validation of a predictive model to evaluate the risk of bone metastasis in kidney cancer
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/131e0da47be742b3ad480754d20df11d
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Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

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