Laser ablation after stereotactic radiosurgery: a multicenter prospective study in patients with metastatic brain tumors and radiation necrosis
Bibliographic record
Abstract
OBJECTIVE: Laser Ablation After Stereotactic Radiosurgery (LAASR) is a multicenter prospective study of laser interstitial thermal (LITT) ablation in patients with radiographic progression after stereotactic radiosurgery for brain metastases. METHODS: Patients with a Karnofsky Performance Scale (KPS) score ≥ 60, an age > 18 years, and surgical eligibility were included in this study. The primary outcome was local progression-free survival (PFS) assessed using the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. Secondary outcomes were overall survival (OS), procedure safety, neurocognitive function, and quality of life. RESULTS: Forty-two patients—19 with biopsy-proven radiation necrosis, 20 with recurrent tumor, and 3 with no diagnosis—were enrolled. The median age was 60 years, 64% of the subjects were female, and the median baseline KPS score was 85. Mean lesion volume was 6.4 cm3 (range 0.4–38.6 cm3). There was no significant difference in length of stay between the recurrent tumor and radiation necrosis patients (median 2.3 vs 1.7 days, respectively). Progression-free survival and OS rates were 74% (20/27) and 72%, respectively, at 26 weeks. Thirty percent of subjects were able to stop or reduce steroid usage by 12 weeks after surgery. Median KPS score, quality of life, and neurocognitive results did not change significantly for either group over the duration of survival. Adverse events were also similar for the two groups, with no significant difference in the overall event rate. There was a 12-week PFS and OS advantage for the radiation necrosis patients compared with the recurrent tumor or tumor progression patients. CONCLUSIONS: In this study, in which enrolled patients had few alternative options for salvage treatment, LITT ablation stabilized the KPS score, preserved quality of life and cognition, had a steroid-sparing effect, and was performed safely in the majority of cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".