GRade, Age, Nodes, and Tumor (GRANT) compared with Leibovich score to predict survival in localized renal cell carcinoma: A nationwide study

Juul, Simon; Donskov, Frede; Clark, Peter E.; Lund, Lars; Azawi, Nessn

doi:10.1111/iju.14859

Cited by 5 publications

(3 citation statements)

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“…Detecting patients at high risk for RCC recurrence after curative treatment remains an unmet medical need. Many nomograms fail to this end, either due to the complexity of the nomogram or the low accuracy rate [ 35 ], [ 36 ]. Numerous biomarkers for RCC have demonstrated promising results in predicting recurrence-free survival, yet none have been validated and standardized for clinical use.…”

Section: Discussionmentioning

confidence: 99%

suPARnostic: an advanced predictive tool for detecting recurrence in renal cell carcinoma

Azawi,

Mosholt,

Fryd

et al. 2023

BMC Urol

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Background Plasma soluble urokinase-type Plasminogen Activator Receptor (suPAR) predicts disease aggressiveness in renal cell carcinoma (ccRCC), but its prognostic accuracy has not been investigated. To investigate the prognostic accuracy of preoperative plasma suPAR in patients who received curative treatment for initially localized ccRCC. Methods We retrospectively analyzed plasma samples stored in the Danish National Biobank between 2010 and 2015 from 235 patients with ccRCC at any stage. Relationships with outcome analyzed using univariate and multiple logistic Cox regression analysis. Results There were 235 patients with ccRCC. The median follow-up period was 7.7 years. In univariate analysis suPAR ≥ 6 ng/mL was significantly associated with overall survival (OS) and recurrence-free survival (RFS). Patients with elevated suPAR were more likely to recur, with a Hazard Ratio (HR) of 2.3 for RFS. In multiple logistic regression, suPAR ≥ 6 ng/mL remained a negative predictor of OS and RFS. Limitations include retrospective study design, wide confidence intervals, and tumor subtype heterogeneity bias. Conclusions ccRCC patients with high plasma suPAR concentrations are at an elevated risk of disease recurrence and see lower OS. suPAR is a promising surveillance tool to more precisely follow up with ccRCC patients and detect future recurrences. Patient Summary In this study, we showed that new type of liquid marker in blood plasma, called suPAR, is associated to a higher risk of kidney cancer recurrence when elevated above 6ng/mL. We also showed suPAR to independently be able to predict patients overall and recurrence free survival in patient with any stage of kidney cancer.

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Section: Discussionmentioning

confidence: 99%

suPARnostic: an advanced predictive tool for detecting recurrence in renal cell carcinoma

Azawi,

Mosholt,

Fryd

et al. 2023

BMC Urol

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“…Several prognostic models incorporating clinical and pathological RCC variables have been validated to predict recurrence and mortality after nephrectomy in chRCC [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Of these, two models are recommended by European guidelines to predict cancer control outcomes after nephrectomy in non-metastatic chRCC [ 15 ]: Leibovich 2018 [ 2 , 16 ] and GRade, Age, Nodes and Tumor (GRANT) [ 3 , 16 , 17 , 18 , 19 , 20 ]. However, their ability to predict cancer-specific survival (CSS) has only been validated in a mono-institutional European cohort of 127 chRCC patients [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Critical Appraisal of Leibovich 2018 and GRANT Models for Prediction of Cancer-Specific Survival in Non-Metastatic Chromophobe Renal Cell Carcinoma

Piccinelli

Morra

Tappero

et al. 2023

Cancers

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Within the Surveillance, Epidemiology, and End Results database (2000–2019), we identified 5522 unilateral surgically treated non-metastatic chromophobe kidney cancer (chRCC) patients. This population was randomly divided into development vs. external validation cohorts. In the development cohort, the original Leibovich 2018 and GRANT categories were applied to predict 5- and 10-year cancer-specific survival (CSS). Subsequently, a novel multivariable nomogram was developed. Accuracy, calibration and decision curve analyses (DCA) tested the Cox regression-based nomogram as well as the Leibovich 2018 and GRANT risk categories in the external validation cohort. The accuracy of the Leibovich 2018 and GRANT models was 0.65 and 0.64 at ten years, respectively. The novel prognostic nomogram had an accuracy of 0.78 at ten years. All models exhibited good calibration. In DCA, Leibovich 2018 outperformed the novel nomogram within selected ranges of threshold probabilities at ten years. Conversely, the novel nomogram outperformed Leibovich 2018 for other values of threshold probabilities. In summary, Leibovich 2018 and GRANT risk categories exhibited borderline low accuracy in predicting CSS in North American non-metastatic chRCC patients. Conversely, the novel nomogram exhibited higher accuracy. However, in DCA, all examined models exhibited limitations within specific threshold probability intervals. In consequence, all three examined models provide individual predictions that might be suboptimal and be affected by limitations determined by the natural history of chRCC, where few deaths occur within ten years from surgery. Further investigations regarding established and novel predictors of CSS and relying on large sample sizes with longer follow-up are needed to better stratify CSS in chRCC.

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“…In this article, Juul et al . compared GRANT system with Leibovich algorithm to evaluate which is more feasible to predict patient prognosis using the nationwide Danish RCC database 1 . They concluded that both Leibovich and GRANT system were useful in assessing recurrence and survival risk groups in clear and non‐clear cell RCC.…”

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confidence: 99%

Editorial Comment to GRade, Age, Nodes, and Tumor (GRANT) compared with Leibovich score to predict survival in localized renal cell carcinoma: A nationwide study

Yoshimura

2022

Int J of Urology

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It would be of great use to objectively predict the postoperative outcome of progression for patients with localized renal cell carcinoma (RCC) who underwent radical or partial nephrectomy. Leibovich et al. reported a prediction model after surgery for localized RCC in 2003 when radical nephrectomy was a standard therapy for T1 RCC. They proposed scoring algorithm using five parameters, such as pathological T stage, pathological N stage, tumor size, nuclear grade, and histological tumor necrosis. However, this scoring system has not been so popular because it is difficult to collect five parameters precisely, especially presence or absence of tumor necrosis. They also reported the updated model for predicting oncologic outcomes in RCC after surgery in 2018. In 2017, to more easily predict outcome after nephrectomy, GRade, Age, Nodes, and Tumor (GRANT) scoring system was reported. It was based on four easily obtained parameters: Fuhrman grade, age, pathological nodal status and pathological tumor size. In this article, Juul et al. compared GRANT system with Leibovich algorithm to evaluate which is more feasible to predict patient prognosis using the nationwide Danish RCC database. 1 They concluded that both Leibovich and GRANT system were useful in assessing recurrence and survival risk groups in clear and non-clear cell RCC. They also mentioned that Leibovich scoring model outperformed GRANT model when five parameters for Leibovich model are available.In 2022, Rosiello et al. reported the results of head-to-head comparison of all the prognostic models recommended by the European Association of Urology (EAU) guidelines to predict oncologic outcomes in patients with RCC. 2 There are five prognostic models for predicting oncologic outcomes in patients with localized RCC that EAU guidelines 2021 strongly recommend, namely UCLA Integrated Staging System (UISS), 3 Leibovich score 2003, Leibovich score 2018, VENUSS score, 4 and GRANT score. UISS utilizes three parameters (Tumor, Nodes, and Metastasis stage, Fuhrman grade, and Eastern Cooperative Oncology Group performance status) and VENUSS score is based on tumor size, T stage, N stage, presence of venous tumor thrombus, and nuclear grade. They concluded that there is a non-negligible difference in terms of performance accuracy among five models. Leibovich score 2018 is the most accurate prognostic model for patients with clear cell RCC. On the other hand, VENUSS or UISS is the most accurate predicting tool for patients with papillary or chromophobe RCC. These scoring models could make patient's risk stratification easier for planning the postoperative follow-up program after radical or partial nephrectomy for localized RCC.

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GRade, Age, Nodes, and Tumor (GRANT) compared with Leibovich score to predict survival in localized renal cell carcinoma: A nationwide study

Cited by 5 publications

References 14 publications

suPARnostic: an advanced predictive tool for detecting recurrence in renal cell carcinoma

suPARnostic: an advanced predictive tool for detecting recurrence in renal cell carcinoma

Critical Appraisal of Leibovich 2018 and GRANT Models for Prediction of Cancer-Specific Survival in Non-Metastatic Chromophobe Renal Cell Carcinoma

Editorial Comment to GRade, Age, Nodes, and Tumor (GRANT) compared with Leibovich score to predict survival in localized renal cell carcinoma: A nationwide study

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