Oncological Outcomes of Active Surveillance versus Surgery or Ablation for Patients with Small Renal Masses: A Systematic Review and Quantitative Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: While active surveillance (AS) is an alternative to surgical interventions in patients with small renal masses (SRMs), evidence regarding its oncological efficacy is still debated. We aimed to evaluate oncological outcomes for patients with SRMs who underwent AS in comparison to surgical interventions. METHODS: In April 2024, PubMed, Scopus, and Web of Science were queried for comparative studies evaluating AS in patients with SRMs (PROSPERO: CRD42024530299). The primary outcomes were overall (OS) and cancer-specific survival (CSS). A random-effects model was used for quantitative analysis. KEY FINDINGS AND LIMITATIONS: We identified eight eligible studies (three prospective, four retrospective, and one study based on Surveillance, Epidemiology and End Results [SEER] data) involving 4947 patients. Pooling of data with the SEER data set revealed significantly higher OS rates for patients receiving surgical interventions (hazard ratio [HR] 0.73; p = 0.007), especially partial nephrectomy (PN; HR 0.62; p < 0.001). However, in a sensitivity analysis excluding the SEER data set there was no significant difference in OS between AS and surgical interventions overall (HR 0.84; p = 0.3), but the PN subgroup had longer OS than the AS group (HR 0.6; p = 0.002). Only the study based on the SEER data set showed a significant difference in CSS. The main limitations include selection bias in retrospective studies, and classification of interventions in the SEER database study. CONCLUSIONS AND CLINICAL IMPLICATIONS: Patients treated with AS had similar OS to those who underwent surgery or ablation, although caution is needed in interpreting the data owing to the potential for selection bias and variability in AS protocols. Our review reinforces the need for personalized shared decision-making to identify patients with SRMs who are most likely to benefit from AS. PATIENT SUMMARY: For well-selected patients with a small kidney mass suspicious for cancer, active surveillance seems to be a safe alternative to surgery, with similar overall survival. However, the evidence is still limited and more studies are needed to help in identifying the best candidates for active surveillance.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".