Dose-response of localized renal cell carcinoma after stereotactic body radiation therapy: A meta-analysis
Bibliographic record
Abstract
Background Stereotactic ablative radiation therapy (SBRT) is an emerging treatment option for primary renal cell carcinoma (RCC), particularly in patients who are unsuitable for surgery. The aim of this review is to assess the effect of increasing the biologically equivalent dose (BED) via various radiation fractionation regimens on clinical outcomes. Methods A literature search was conducted in PubMed (Medline), EMBASE, and the Cochrane Library for studies published up to October 2023. Studies reporting on patients with localized RCC receiving SBRT were included to determine its effectiveness on local control, progression-free survival, and overall survival. A random effects model was used to meta-regress clinical outcomes relative to the BED for each study and heterogeneity was assessed by I 2 . Results A total of 724 patients with RCC from 22 studies were included, with a mean age of 72.7 years (range44.0–81.0). Local control was excellent with an estimate of 99 % (95 %CI: 97–100 %, I 2 = 19 %), 98 % (95 %CI: 96–99 %, I 2 = 8 %), and 94 % (95 %CI: 90–97 %, I 2 = 11 %) at one year, two years, and five years respectively. No definitive association between increasing BED and local control, progression-free survival and overall survival was observed. No publication bias was observed. Conclusions A significant dose response relationship between oncological outcomes and biologically effective dose was not identified, and excellent local control outcomes were observed at the full range of doses. Until new evidence points otherwise, we support current recommendations against routine dose escalation beyond 25–26 Gy in one fraction or 42–48 Gy in three fractions, and to consider de-escalation or compromising target coverage if required to achieve safe organ at risk doses.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.072 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".