Frameless Stereotactic Radiosurgery With Linear Accelerator (LINAC)-Based Technology for Brain Metastases: Outcomes Analysis in 141 Patients
Bibliographic record
Abstract
Objectives Brain metastases (BM) are the most common intracranial tumors in adults. Surgery and frame-based stereotactic radiosurgery (SRS) are well-described treatment options. Frameless SRS is an emerging BM treatment option offering fewer side effects. The aim of this study was to describe the therapeutic outcomes and toxicity of frameless SRS with linear accelerator (LINAC)-based technology for BM treatment in our institution. Materials and methods We performed a retrospective study including all adult patients treated with frameless SRS with LINAC-based technology for BM between October 2010 and July 2016. Patients were followed routinely with MRI scans at three-month intervals. Primary endpoints were progression-free survival, local control, overall survival, and toxicity related to the treatment. All survival times were computed with the Kaplan-Meier method. All cumulative incidences were computed using competing risk analyses. Results A total of 194 metastatic lesions in 141 patients were treated in a 69-month interval. At the time of analysis, 33 patients were still alive, with a median follow-up time of 25.1 months. The overall median survival was 8.7 months. The median progression-free survival was 5.3 months. Local recurrence as a first event was 25% and 38% at one and two years, respectively, while distant brain recurrence as a first event was 18% and 21%. Death before any brain event occurred in 31% of patients. The cumulative incidence of radiation necrosis as a first brain event was 2% at one and two years. Conclusions The treatment of BM with LINAC-based frameless SRS in our institution had an overall and progression-free survival comparable with the literature for frameless SRS and for conventional frame-based SRS while being less invasive and more comfortable for the patient. In our study, frameless SRS with LINAC technology seems to be safe for BM treatment with minimal rates of radiation necrosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".