Image-Guided, Linac-Based, Surgical Cavity-Hypofractionated Stereotactic Radiotherapy in 5 Daily Fractions for Brain Metastases
Bibliographic record
Abstract
BACKGROUND: Cavity stereotactic radiotherapy has emerged as a standard option following resection of brain metastases. However, the optimal approach with either single-fraction or hypofractionated stereotactic radiotherapy (HSRT) remains a significant question. OBJECTIVE: To report outcomes for 5-fraction HSRT to the surgical cavity, based on contouring according to a recently reported international consensus guideline. METHODS: Patients treated with cavity HSRT were identified from a prospective institutional database. Local brain control (LC), distant brain failure (DBF), leptomeningeal disease (LMD), and overall survival rates were determined. Univariate and multivariable analyses were performed on potential predictive factors. RESULTS: One hundred thirty-seven cavities in 122 patients were treated at a median total dose of 30 Gy (range, 25-35 Gy). The median follow-up was 16 mo (range, 1-60 mo). Nonsmall cell lung cancer was the most common histology (44%), followed by breast cancer (21%). In 57% of surgical cavities, the preoperative tumor diameter was >3 cm. One-year LC, DBF, LMD, and overall survival rates were 84%, 45%, 22%, and 62%, respectively. Multivariable analyses identified colorectal (hazard ratio [HR] 4.1, P = .0066) and melanoma (HR 2.4, P = .012) metastases as predictors of local recurrence; preoperative tumor diameter >2 cm (HR 8.9, P = .012) and absence of targeted therapy (HR 4.4, P = .03) as predictors of DBF; and breast cancer histology (HR 2.1, P = .05) and subtotal resection (HR 2.6, P = .009) as predictors of LMD. Symptomatic radiation necrosis was observed in 7 patients (6%). CONCLUSION: High rates of LC were observed following this 5-fraction HSRT regimen. Superiority as compared to single-fraction SRS requires a randomized trial.
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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 | 0.000 | 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) | 0.001 | 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".