CTNI-59. FIRST SAFETY ANALYSIS OF ANAPLASTIC MENINGIOMA PATIENTS TREATED WITH TUMOR TREATING FIELDS (TTFIELDS)
Bibliographic record
Abstract
Abstract BACKGROUND Anaplastic meningioma (AM) is a rare and aggressive intracranial tumor with very limited treatment options. TTFields is an established therapy and indicated for the treatment of newly diagnosed and recurrent glioblastoma (GBM) patients. The phase 3 study METIS tests the application in another intracranial cancer type, more specifically brain metastases of non-small cell lung cancer. First preclinical data show that TTFields can reduce proliferation of patient-derived meningioma cells and one first case report of a GBM patient with an in-field meningioma was published. First clinical trials are underway to investigate the safety and efficacy in meningioma patients (NCT02847559). Here, we describe first surveillance data on anaplastic meningioma patients treated with the TTFields device indicated for GBM treatment. METHODS Global post-market surveillance data of patients with anaplastic meningioma were assessed employing the MedDRA body system (System organ class (SOC) and preferred terms) with data cut-off in April 2020. RESULTS A total of 29 patients with AM were treated since 2015 to date. The median age is 57y (range 26-74y; based on YOB). Of those 9 (31%) were female and 20 (69%) were male. 15 (52 %) patients with AM reported at least one adverse event (AE). A total of 51 AEs were reported, of those 32 were assessed by the manufacturer as potentially related to TTFields treatment. Most commonly reported were skin reactions beneath the transducer arrays (n=15). No serious adverse events related to TTFields were reported. CONCLUSION Here we present the first post-market surveillance data of anaplastic meningioma patients treated with TTFields. Clinical studies are warranted to investigate the safety and efficacy for the use of TTFields in anaplastic meningioma patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".