Role of clinicopathological variables in predicting recurrence and survival outcomes after surgery for non‐metastatic renal cell carcinoma: Systematic review and meta‐analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.021 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".