Validation of the GRade, Age, Nodes and Tumor (GRANT) score within the Surveillance Epidemiology and End Results (SEER) database: A new tool to predict survival in surgically treated renal cell carcinoma patients

Buti, Sebastiano; Karakiewicz, Pierre I.; Bersanelli, Melissa; Capitanio, Umberto; Tian, Zhe; Cortellini, Alessio; Taguchi, Satoru; Briganti, Alberto; Montorsi, Francesco; Leonardi, Francesco; Bandini, Marco

doi:10.1038/s41598-019-49250-6

Cited by 17 publications

(14 citation statements)

References 25 publications

(33 reference statements)

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“…Women more frequently use healthcare system, including the scheduled abdominal radiological examination, resulting in early detection, which may further contribute to the difference of BMs in RCC (24). Variables including pathological grade, T-stage, and N-stage associated with patient overall survival were also shown to be related to BMs (19,(24)(25)(26). A possible explication for the higher risk of BMs in mRCC patients with high-level pathological grade and advanced T-stage and N-stage could be the possibility to possess drastic aggressiveness.…”

Section: Discussionmentioning

confidence: 98%

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

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 98%

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

et al. 2021

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“…The most common pathological type (85%) of RCC is clear cell carcinoma [1,2]. It is important to accurately predict the survival of RCC patients; however, few efficient predictive tools are available [3][4][5], particularly for patients with metastatic RCC [6] (which remains incurable). Traditionally, immunochemotherapy has been used to treat nonmetastatic RCC, but the response rate was rarely more than 15%.…”

Section: Introductionmentioning

confidence: 99%

A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases

Sheng

et al. 2021

BioMed Research International

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Background. The optimal tool for predicting the survival of renal cell carcinoma (RCC) patients with lung metastases remains controversial. Methods. We selected patients diagnosed with RCC and lung metastases, from 2010 to 2015, from the Surveillance, Epidemiology, and End Results (SEER) database. After the selection of inclusion criteria and exclusion criterion, the rest of the patients were incorporated into model analysis. Least absolute shrinkage and selection operator (LASSO) regression was used to select the most important features for construction of a nomogram predicting cancer-specific survival. A calibration plot and the concordance index ( C -index) were used to estimate nomogram efficacy in a validation cohort. The association between important factors selected by LASSO regression, and prognosis was assessed by the Kaplan-Meier (KM) survival curve. The receiver operating characteristic (ROC) curves were drawn to compare sensitivity and specificity between the nomogram we built and the TNM stage-based model. Results. A total of 1,369 patients met the inclusion criteria, but not the exclusion criteria. The LASSO regression model reduced 15 features to seven potential predictors of survival, including tumor grade, the extent of surgery, N and T status, histological profile, and brain and bone metastasis status. Such features had good discrimination in the KM survival curves. The nomogram showed excellent discriminatory power ( C -index, 0.71; 95% confidence interval: 0.70 to 0.72) and good calibration in terms of both 1- and 2-year cancer-specific survival. The nomogram showed great discriminatory power ( C -index 0.68) and adequate calibration when applied to the validation cohort. The areas under the curve (AUCs) of nomogram were 0.767 and 0.780, respectively, and the AUCs of TNM stage were 0.617 and 0.618 at 1 and 2 years, respectively. Conclusions. Our nomogram might play a major role in predicting the cancer-specific survival of RCC patients with lung metastases.

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“…Moreover, the results of the multivariate Cox regression analysis showed that the TNM stage (HR = 1.680, p < 0.001) and our prognostic model (HR = 3.607, p < 0.001) were both independent factors of RFS among the 334 TCGA-LIHC patients. However, among the 108 patients in the GSE76427 cohort, the TNM stage was not an independent prognostic factor for RFS [ 24 ]. The prognostic model (HR = 2.407, p = 0.014) was also an independent factor for RFS (Fig.…”

Section: Resultsmentioning

confidence: 99%

A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

Wang

Xie

et al. 2021

BMC Cancer

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Background Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice. Methods Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness. Results We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases. Conclusion Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

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Validation of the GRade, Age, Nodes and Tumor (GRANT) score within the Surveillance Epidemiology and End Results (SEER) database: A new tool to predict survival in surgically treated renal cell carcinoma patients

Cited by 17 publications

References 25 publications

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

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

A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases

A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

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