BMET-03. METIS: A PHASE III STUDY OF RADIOSURGERY WITH TTFIELDS FOR 1-10 BRAIN METASTASES FROM NSCLC
Bibliographic record
Abstract
Tumor Treating Fields (TTFields) are a novel, non-invasive regional anti-mitotic treatment modality, based on low intensity alternating electric fields. Efficacy of TTFields in non-small cell lung cancer (NSCLC) has been demonstrated in multiple in vitro and in vivo models, and in a phase I/II clinical study. TTFields treatment to the brain was shown to be safe and effective in glioblastoma patients. TRIAL DESIGN: 270 patients with 1-10 brain metastases (BM) from NSCLC will be randomized in a ratio of 1:1 to receive stereotactic radio surgery (SRS) followed by either TTFields or supportive care alone. Patients are followed-up every two months until second cerebral progression. Patients in the control arm may cross over to receive TTFields at the time of second cerebral progression. To test the efficacy, safety and neurocognitive outcomes of TTFields in this patient population. ENDPOINTS: Time to first cerebral progression (primary); time to neurocognitive failure; overall survival; radiological response rate; quality of life; adverse events severity and frequency (secondary). TREATMENT: Continuous TTFields at 150 kHz will be applied to the brain within 7 days of SRS. The treatment system is a portable medical device allowing normal daily life activities. Patients will receive the best standard of care for their systemic disease. The trial is designed to detect an increase in the time to cerebral progression from 7.7 to 13.4 months (hazard ratio 0.57). The sample size assessment takes into consideration a competing risk (death prior to cerebral progression) of 0.08252 per month in both treatment arms. The competing risk is based on a predicted median overall survival of 8.4 months mainly due to systemic disease progression. The trial has 80% power at a two sided alpha of 0.05. The sample size was calculated using a log-rank test (based on Lakatos 1988 and 2002).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".