Radiosurgery for multiple brain metastases using volumetric modulated arc therapy: a single institutional series.
Bibliographic record
Abstract
Background: Patients with brain metastases (BM) live longer due to improved diagnosis and oncologic treatments. The association of volumetric modulated arc therapy (VMAT) and image-guided radiation therapy (IGRT) with brain radiosurgery (SRS) allows complex dose distributions and faster treatment delivery to multiple lesions. Materials and methods: This study is a retrospective analysis of SRS for brain metastasis using VMAT. The primary endpoints were local disease-free survival (LDFS) and overall survival (OS). The secondary outcomes were intracranial disease-free survival (IDFS) and meningeal disease-free survival (MDFS). Results: The average number of treated lesions was 5.79 (range: 2-20) per treatment in a total of 113 patients. The mean prescribed dose was 18 Gy (range: 12-24 Gy). The median LDFS was 46 months. The LDFS in 6, 12, and 24 months was for 86%, 79%, and 63%, respectively. Moreover, brain progression occurred in 50 patients. The median overall survival was 47 months. The OS in 75%, 69%, and 61% patients was 6, 12, and 24 months, respectively. IDFS was 6 and 24 months in 35% and 14% patients, respectively. The mean MDFS was 62 months; it was 6 and 24 months for 87% and 83% of patients. Acute severe toxicity was relatively rare. During follow-up, the rates of radionecrosis and neurocognitive impairment were low (10%). Conclusion: The use of VMAT-SRS for multiple BM was feasible, effective, and associated with low treatment-related toxicity rates. Thus, treatment with VMAT is a safe technique to plan to achieve local control without toxicity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".