Surveillance post-radiofrequency ablation for small renal masses: Recurrence and followup
Bibliographic record
Abstract
INTRODUCTION: Small renal masses (SRMs), enhancing tumors <4 cm in diameter, are suspicious for renal cell carcinoma (RCC). The incidence of SRMs have risen with the increased quality and frequency of imaging. Partial nephrectomy is widely accepted as a nephron-sparing approach for the management of clinically localized RCC, with a greater than 90% disease-specific survival for stage T1a. Radiofrequency ablation (RFA) has been emerging as an alternative management strategy, with evidence suggesting RFA as a safe alternative for SRMs. We aimed to evaluate the time to recurrence and recurrence rates of SRMs treated with RFA at our institution. METHODS: A retrospective review between October 2011 and May 2019 identified 141 patients with a single SRM treated with RFA at Hamilton Health Sciences and St. Joseph's Healthcare Hamilton. Patients with familial syndromes and distant metastases were excluded. Repeat RFAs of the ipsilateral kidney for incomplete ablation were not considered a new procedure. The primary variable measured was time from initial ablation to recurrence. A Cox proportional hazard regression model was used to identify possible prognostic variables for tumor recurrence defined a priori, including age, gender, mass size, RENAL nephrometry, and PADUA scores. RESULTS: The overall average age of our patients was 69.0±11.1 years, with 71.6% being male. Average tumor size was 2.6±0.8 cm. There were 22/154 total recurrences (15.6%) post-RFA. Median followup time was 67 (18-161) months. Those with new recurrences had median time to recurrence of 15 months and no recurrence beyond 53 months. Thirteen of 141 patients had residual disease (9.2%) and were identified within the first eight months post-RFA. The only prognostic variable identified as a predictor of residual disease was tumor size (hazard ratio 2.265; p<0.001). CONCLUSIONS: This study shows the risk of a new recurrence following RFA for SRMs is 6.4%. Most recurrences (9.2%) were a result of residual tumor at the ablation site identified within the first eight months post-RFA. No recurrences were identified beyond 53 months, with a total median followup time of 67 months. Tumor size alone, without need for complex scoring systems, may serve as a predictor of incomplete ablation following RFA and could be used to assist in shared decision-making on management strategies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".