Radiofrequency Ablation of Renal Tumors: Intermediate-Term Results
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Needle ablative therapies are being offered to patients presenting with small renal masses, but long-term outcomes are currently unavailable. We report our intermediate-term results (1-4 years) after radiofrequency ablation (RFA) of small (<4-cm) renal masses. PATIENTS AND METHODS: At our institution, all renal tumors treated using RFA since May 2001 have been recorded in a prospective database. During this time, 94 tumors (mean size 2.4 cm; range 1-4.2 cm) in 78 patients were treated using a temperature-based RFA generator by either a percutaneous (59%) or a laparoscopic approach. The patients followed with imaging at 6 weeks, 3 and 6 months, and every 6 months thereafter. Only patients with at least 12 months of follow-up were eligible for this analysis; the mean follow-up was 25 months. RESULTS: Of the 89% of masses that were biopsied, 77% were renal-cell carcinomas (RCC), of which 66% were Fuhrman grade 1, 31% were grade 2, and 3% were grade 3. Three recurrences were noted, for an overall recurrence-free rate of 96.8%. In this patient population with numerous comorbid conditions, there were six deaths but only one related to renal cancer, for a cancer-specific survival rate of 98.5% and an overall survival rate of 92.3%. CONCLUSION: In the intermediate term (1-4 years), the oncologic effectiveness of RFA appears comparable to that of traditional treatments offered for small renal masses. Further studies of larger numbers of patients with longer follow-up are needed.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".