Role of clinicopathological variables in predicting recurrence and survival outcomes after surgery for non‐metastatic renal cell carcinoma: Systematic review and meta‐analysis

Renal cell carcinoma (RCC) represents 2% of all diagnosed malignancies worldwide, with disease recurrence affecting 20% to 40% of patients. Existing prognostic recurrence models based on clinicopathological features continue to be a subject of controversy. In this meta-analysis, we summarized research findings that explored the correlation between clinicopathological characteristics and post-surgery survival outcomes in non-metastatic RCC patients. Our analysis incorporates 99 publications spanning 140 568 patients. The study's main findings indicate that the following clinicopathological characteristics were associated with unfavorable survival outcomes: T stage, tumor grade, tumor size, lymph node involvement, tumor necrosis, sarcomatoid features, positive surgical margins (PSM), lymphovascular invasion (LVI), early recurrence, constitutional symptoms, poor performance status (PS), low hemoglobin level, high body-mass index (BMI), diabetes mellitus (DM) and hypertension. All of which emerged as predictors for poor recurrence-free survival (RFS) and cancer-specific survival. Clear cell (CC) subtype, urinary collecting system invasion (UCSI), capsular penetration, perinephric fat invasion, renal vein invasion (RVI) and increased C-reactive protein (CRP) were all associated with poor RFS. In contrast, age, sex, tumor laterality, nephrectomy type and approach had no impact on survival outcomes. As part of an additional analysis, we attempted to assess the association between these characteristics and late recurrences (relapses occurring more than 5 years after surgery). Nevertheless, we did not find any prediction capabilities for late disease recurrences among any of the features examined. Our findings highlight the prognostic significance of various clinicopathological characteristics potentially aiding in the identification of high-risk RCC patients and enhancing the development of more precise prediction models.