Radiation necrosis in renal cell carcinoma brain metastases treated with checkpoint inhibitors and radiosurgery: An international multicenter study
Bibliographic record
Abstract
BACKGROUND Patients with renal cell carcinoma (RCC) brain metastases are frequently treated with immune checkpoint inhibitors (ICIs) and stereotactic radiosurgery (SRS). However, data reporting on the risk of developing radiation necrosis (RN) are limited. METHODS RN rates were compared for concurrent therapy (ICI/SRS administration within 4 weeks of one another) and nonconcurrent therapy with the χ 2 test. Univariable logistic regression was used to identify factors associated with developing RN. RESULTS Fifty patients (23 concurrent and 27 nonconcurrent) with 395 brain metastases were analyzed. The median follow‐up was 12.1 months; the median age was 65 years. The median margin dose was 20 Gy, and 4% underwent prior whole‐brain radiation therapy (WBRT). The median treated tumor volume was 3.32 cm 3 (range, 0.06‐42.38 cm 3 ); the median volume of normal brain tissue receiving a dose of 12 Gy or higher (V12 Gy) was 8.42 cm 3 (range, 0.27‐111.22 cm 3 ). Any‐grade RN occurred in 17.4% and 22.2% in the concurrent and nonconcurrent groups, respectively ( P = .67). Symptomatic RN occurred in 4.3% and 14.8% in the concurrent and nonconcurrent groups, respectively ( P = .23). Increased tumor volume during SRS (odds ratio [OR], 1.08; 95% confidence interval [CI], 1.01‐1.19; P = .04) was associated with developing RN, although V12 Gy (OR, 1.03; 95% CI, 0.99‐1.06; P = .06), concurrent therapy (OR, 0.74; 95% CI, 0.17‐2.30; P = .76), prior WBRT, and ICI agents were not statistically significant. CONCLUSIONS Symptomatic RN occurs in a minority of patients with RCC brain metastases treated with ICI/SRS. The majority of events were grade 1 to 3 and were managed medically. Concurrent ICI/SRS does not appear to increase this risk. Attempts to improve dose conformality (reduce V12) may be the most successful mitigation strategy in single‐fraction SRS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".