Toxicity of concurrent stereotactic radiotherapy and targeted therapy or immunotherapy: A systematic review
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Both stereotactic radiotherapy (SRT) and immune- or targeted therapy play an increasingly important role in personalized treatment of metastatic disease. Concurrent application of both therapies is rapidly expanding in daily clinical practice. In this systematic review we summarize severe toxicity observed after concurrent treatment. MATERIAL AND METHODS: PubMed and EMBASE databases were searched for English literature published up to April 2016 using keywords "radiosurgery", "local ablative therapy", "gamma knife" and "stereotactic", combined with "bevacizumab", "cetuximab", "crizotinib", "erlotinib", "gefitinib", "ipilimumab", "lapatinib", "sorafenib", "sunitinib", "trastuzumab", "vemurafenib", "PLX4032", "panitumumab", "nivolumab", "pembrolizumab", "alectinib", "ceritinib", "dabrafenib", "trametinib", "BRAF", "TKI", "MEK", "PD1", "EGFR", "CTLA-4" or "ALK". Studies performing SRT during or within 30days of targeted/immunotherapy, reporting severe (⩾Grade 3) toxicity were included. RESULTS: Concurrent treatment is mostly well tolerated in cranial SRT, but high rates of severe toxicity were observed for the combination with BRAF-inhibitors. The relatively scarce literature on extra-cranial SRT shows a potential risk of increased toxicity when SRT is combined with EGFR-targeting tyrosine kinase inhibitors and bevacizumab, which was not observed for cranial SRT. CONCLUSIONS: This review gives a best-possible overview of current knowledge and its limitations and underlines the need for a timely generation of stronger evidence in this rapidly expanding field.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.004 | 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".