Concurrent Administration of Immune Checkpoint Inhibitors and Stereotactic Radiosurgery Is Well-Tolerated in Patients With Melanoma Brain Metastases: An International Multicenter Study of 203 Patients
Bibliographic record
Abstract
BACKGROUND: Melanoma brain metastases are commonly treated with stereotactic radiosurgery (SRS) and immune checkpoint inhibitors (ICIs). However, the toxicity of these 2 treatments is largely unknown when administered concurrently. OBJECTIVE: To evaluate the risk of radiation necrosis (RN) with concurrent and nonconcurrent SRS and ICIs. METHODS: The guidelines from the Strengthening the Reporting of Observational Studies in Epidemiology checklist were used. Inverse probability of treatment weighting, univariable and multivariable logistic regression, and the Kaplan-Meier method was utilized. RESULTS: There were 203 patients with 1388 brain metastases across 11 international institutions in 4 countries with a median follow-up of 15.6 months. The rates of symptomatic RN were 9.4% and 8.2% in the concurrent and nonconcurrent groups, respectively ( P =.766). On multivariable logistic regression, V12 ≥ 10 cm 3 (odds ratio [OR]: 2.76; P =.006) and presence of BRAF mutation (OR: 2.20; P =.040) were associated with an increased risk of developing symptomatic RN; the use of concurrent over nonconcurrent therapy was not associated with an increased risk (OR: 1.06; P =.877). There were 20 grade 3 toxic events reported, and no grade 4 events reported. One patient experienced a grade 5 intracranial hemorrhage. The median overall survival was 36.1 and 19.8 months for the concurrent and nonconcurrent groups (log-rank P =.051), respectively. CONCLUSION: Concurrent administration of ICIs and SRS are not associated with an increased risk of RN. Tumors harboring BRAF mutation, or perhaps prior exposure to targeted agents, may increase this risk. Radiosurgical optimization to maintain V12 < 10 cm 3 is a potential strategy to reduce the risk of RN.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".