Diagnostic and Prognostic Nomograms for Bone Metastasis in Patients With Newly Diagnosed Renal Cell Carcinoma
Background
Bone metastasis (BM) is one of the common sites of renal cell carcinoma (RCC), and patients with BM have a worse prognosis than those without it. We aimed to develop two nomograms to quantify the risk of BM and predict the prognosis of RCC patients with BM.
Methods
We reviewed patients with newly diagnosed RCC with BM in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Multivariate logistic regression analysis was used to determine independent predictors of BM in RCC patients. Univariate and multivariate Cox proportional hazards regression analysis was used to determine independent prognostic factors for BM in RCC patients. Diagnostic and prognostic nomograms were established and evaluated by calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).
Results
The study included 37554 patients newly diagnosed with RCC in the SEER database, 537 of whom were BM patients. Risk factors for BM in RCC patients included sex, tumor size, liver metastasis, lung metastasis, brain metastasis, N stage, T stage, histologic type, and grade. Independent prognostic factors for RCC with BM were grade, histologic type, N stage, surgery, brain metastasis, and lung metastasis. Calibration, ROC curve, and DCA showed that both diagnostic and prognostic nomograms showed good performance.
Conclusions
Diagnostic and prognostic nomograms were established to predict the risk of BM in RCC and the prognosis of RCC with BM, respectively. These nomograms strengthen each patient's prognosis-based decision making, which is of great significance in improving the prognosis of patients.
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Posted 21 Sep, 2020
Diagnostic and Prognostic Nomograms for Bone Metastasis in Patients With Newly Diagnosed Renal Cell Carcinoma
Posted 21 Sep, 2020
Background
Bone metastasis (BM) is one of the common sites of renal cell carcinoma (RCC), and patients with BM have a worse prognosis than those without it. We aimed to develop two nomograms to quantify the risk of BM and predict the prognosis of RCC patients with BM.
Methods
We reviewed patients with newly diagnosed RCC with BM in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Multivariate logistic regression analysis was used to determine independent predictors of BM in RCC patients. Univariate and multivariate Cox proportional hazards regression analysis was used to determine independent prognostic factors for BM in RCC patients. Diagnostic and prognostic nomograms were established and evaluated by calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).
Results
The study included 37554 patients newly diagnosed with RCC in the SEER database, 537 of whom were BM patients. Risk factors for BM in RCC patients included sex, tumor size, liver metastasis, lung metastasis, brain metastasis, N stage, T stage, histologic type, and grade. Independent prognostic factors for RCC with BM were grade, histologic type, N stage, surgery, brain metastasis, and lung metastasis. Calibration, ROC curve, and DCA showed that both diagnostic and prognostic nomograms showed good performance.
Conclusions
Diagnostic and prognostic nomograms were established to predict the risk of BM in RCC and the prognosis of RCC with BM, respectively. These nomograms strengthen each patient's prognosis-based decision making, which is of great significance in improving the prognosis of patients.
Figure 1
Figure 2
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Figure 4
Figure 5
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Figure 8
Figure 9
Figure 10