Cytoreductive stereotactic body radiotherapy (SBRT) and combination SBRT with immune checkpoint inhibitors (ICIs) in metastatic renal cell carcinoma
Bibliographic record
Abstract
INTRODUCTION: Preclinical evidence demonstrates the immunogenic potential of stereotactic body radiotherapy (SBRT). There is growing interest in investigating this interplay with the immune system in metastatic renal cell carcinoma (mRCC). Cytoreduction with SBRT and combination therapy with SBRT and checkpoint inhibitor immuno-oncology agents (IO) are two potential therapeutic strategies in mRCC. In this review, we summarize the current clinical evidence for the use of cytoreductive SBRT to primary kidney and combination SBRT with IO. METHODS: A literature review for articles and abstracts published between January 2000 and March 2020 was conducted through the PubMed, the American Society of Clinical Oncology (ASCO), and the American Society of Radiation Oncology (ASTRO) databases. Evaluation of studies followed the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) criteria. RESULTS: A total of three articles for cytoreductive SBRT and one article and three abstracts for combination SBRT and IO in mRCC met inclusion criteria for this review. Evidence for SBRT to primary kidney is limited by small series and pilot studies. Outcomes vary widely due to small patient numbers and study heterogeneity. Local control ranges from 85-100% and one- and two-year overall survival ranges from 38-71% and 19-53%, respectively. Combination SBRT and IO are tolerable for patients with early data, suggesting grade 3-4 adverse event rates of 0-24%. Long-term survival data is not yet available. CONCLUSIONS: Cytoreductive SBRT and combination SBRT with IO therapy represent promising treatment strategies in mRCC. The evidence for clinical benefit is currently limited and requires further study with well-designed, randomized controlled trials.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".