Stereotactic Body Radiotherapy for Renal Cell Carcinoma—A Review of Use in the Primary, Cytoreductive and Oligometastatic Settings
Bibliographic record
Abstract
Renal cell carcinoma (RCC) has been increasing in incidence by around 1.5% per year for several years. However, the mortality rate has been decreasing by 1.6% per year, and this can be attributed to stage migration and improvements in treatment. One treatment modality that has emerged in recent years is stereotactic body radiotherapy (SBRT), which is an advanced radiotherapy technique that allows the delivery of high-dose radiation to the tumor while minimizing doses to the organs at risk. SBRT has developed a role in the treatment of early-stage, oligometastatic and oligoprogressive RCC. In localized disease, phase II trials and meta-analyses have shown that SBRT provides a very high probability of long-term local control with a low risk of severe late toxicity. In oligometastatic (OMD) RCC, the same level of evidence has similarly shown good local control and minimal toxicity. SBRT could also delay the necessity to start or switch systemic treatments. Medical societies have started to incorporate SBRT in their guidelines in the treatment of localized disease and OMD. A possible future role of SBRT involves cytoreduction. It is theorized that SBRT can lower tumor burden and enhance immune-related response, but it cannot be recommended until the results of the phase II trials are published.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".