The Impact of Immune Checkpoint Inhibition on the Risk of Radiation Necrosis Following Stereotactic Radiotherapy for Metastatic Brain Cancer
Bibliographic record
Abstract
Purpose/objective Forty percent of cancer patients develop brain metastases (BM) and are often treated with stereotactic radiation (SRS/SRT). Checkpoint inhibitor (CI) use is suspected of increasing the risk of radiation necrosis (RN). Our aim is to determine whether treatment with CI is associated with an increased risk of RN in BM patients treated with SRS/SRT. Methods We retrospectively identified the medical records of BM patients treated with SRS/SRT between 1/2017 and 12/2021 using an institutional database. RN was defined by MRI imaging read by neuroradiologists and/or surgical pathology. V12GY of patients with and without RN was compared using the Mann-Whitney test. The chi-square test was used to see if RN was associated with CI use, histology, particular CI agent used, > 1 course SRS/SRT, SRS/SRT dose, chemotherapy, whole brain radiotherapy (WBRT), age, or sex. Results Two hundred and fifty-nine patients treated with 455 courses of SRS/SRT were analyzed. The most common primary histologies were lung 56% (N=146), breast 14% (N= 37), melanoma 9% (N=24), and renal cancer 7% (N=18). A total of 53.8% (N = no. of patients) were treated with CI. The overall rate of any RN was 21.8% (N=27) in the CI group compared to 14.8% (N=141) in the non-CI group (p=0.174). Mean V12Gy was 15.525 cc and 9.419 cc in patients with and without RN (p=0.02768). Mean number of SRS/SRT courses was 2 and 1.53 for patients with and without RN, and >1 course of SRS/SRT was a predictor of RN (p <0.01). Other features analyzed were not significant. Conclusion RN was higher in the BM patients treated with SRS/SRT receiving CI compared to non-CI patients (21.8%, N=27, versus 14.6%, N= 16), but failed to reach statistical significance. V12Gy and > 1 course of SRS/SRT was associated with RN. Caution should be taken in treating patients with SRS/SRT and CI there might be an increased 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".