Risk models for patients with localised renal cell carcinoma

The Four-Feature Prognostic Models for Cancer-Specific and Overall Survival after Surgery for Localized Clear Cell Renal Cancer: Is There a Place for Inflammatory Markers?

We aimed at a determination of the relevance of comorbidities and selected inflammatory markers to the survival of patients with primary non-metastatic localized clear cell renal cancer (RCC). We retrospectively analyzed data from a single tertiary center on 294 patients who underwent a partial or radical nephrectomy in the years 2012–2018. The following parameters were incorporated in the risk score: tumor stage, grade, size, selected hematological markers (SIRI—systemic inflammatory response index; SII—systemic immune-inflammation index) and a comorbidities assessment tool (CCI—Charlson Comorbidity Index). For further analysis we compared our model with existing prognostic tools. In a multivariate analysis, tumor stage (p = 0.01), tumor grade (p = 0.03), tumor size (p = 0.006) and SII (p = 0.02) were significant predictors of CSS, while tumor grade (p = 0.02), CCI (p = 0.02), tumor size (p = 0.01) and SIRI (p = 0.03) were significant predictors of OS. We demonstrated that our model was characterized by higher accuracy in terms of OS prediction compared to the Leibovich and GRANT models and outperformed the GRANT model in terms of CSS prediction, while non-inferiority to the VENUSS model was revealed. Four different features were included in the predictive models for CSS (grade, size, stage and SII) and OS (grade, size, CCI and SIRI) and were characterized by adequate or even superior accuracy when compared with existing prognostic tools.

Recurrence risk groups after nephrectomy for renal cell carcinoma

INTRODUCTION

There is no consensus on the follow-up protocol after nephrectomy for renal cell carcinoma (RCC), and the identification of recurrence risk groups (RRG) is required.

OBJECTIVE

Establish recurrence risk groups (RRG).

MATERIAL AND METHOD

A retrospective analysis of 696 patients with renal cancer submitted to surgery between 1990-2010; 568 (81.6%) patients treated with radical nephrectomy and 128 (18.4%) treated with partial nephrectomy. Pathological variables were classified as: 1st-level variables (1LPV): pTpN stage and Fuhrman grade (FG); and 2nd level pathological variables (2LPV): sarcomatoid differentiation (SD), tumor necrosis (TN), microvascular invasion (MVI) and positive surgical margins (PSM). Univariate and multivariate analysis have been performed using Cox regression to determine 1LPV related to recurrence. Based on 1LPV, we classified patients into three RRG: Low (LRG)<25%; Intermediate (IRG) 26-50% and High (HRG)>50%. We performed univariate and multivariate analysis with the 2LPVs for each RRG. With these data, patients were reclassified as RRG+. ROC curves were used for comparison of RRG and RRG+.

RESULTS

The median follow-up was 105 months (range 63 to 148). There were 177 (25.4%) patients with recurrence: 111 (15.9%) distant, 34 (4.9%) local and 32 (4.6%) distant and local. In the multivariable analysis, Fuhrman grade (HR=2,75; P=.0001) and pTpN stage (HR=2,19; P=.0001) behaved as independent predictive variables of recurrence. Patients were grouped as RRG (AUC=0,76; p=0,0001): - LRG (pT1pNx-0 G1-4; pT2pNx-0 G1-2): 456 (65,5%) patients. - IRG (pT2pNx-0 G3-4; pT3-4pNx-0 G1-2): 110 (15,8%) patients. - HRG (pT3-4pNx-0 G3-4; pT1-4pN+): 130 (18,6%) patients. After multivariate analysis with 2LPV, RRG were reclassified (RRG+) (AUC=.84, P=.0001): -LRG+(LRG without TN, SD and/or PSM(+)). -IRG+(IRG; LRG with TN) -HRG+(HRG; LRG with SD and/or PSM(+); IRG with TN and/or SD) CONCLUSIONS: The inclusion of 2LPV to the classification according to VP1N improves the discriminating capacity of RRG classification.