P11.21 Treating numerous secondary tumors in the brain with tumor treating fields: Simulation study to identify optimal transducer array layout
Bibliographic record
Abstract
Abstract BACKGROUND Tumor Treating Fields (TTFields) use alternating electric fields for the treatment of solid tumors. The therapy is approved for glioblastoma multiforme (GBM), and a phase III trial in 1–10 brain metastases from non-small cell lung cancer (METIS) is currently enrolling patients. In GBM, the layout of the transducer arrays delivering the TTFields to the tumor is optimized for high field intensity in the tumor, while the dose in other regions is decreased. In the setting of secondary brain tumors, as they manifest as brain metastases in 10–30 % of adult cancer patients - especially in melanoma, lung, breast, colon, and kidney cancer - a high TTFields dose in the entire brain would be beneficial. Thus, numerous tumors instead of only one lesion should receive therapeutic TTFields doses. In this study, transducer array layouts aiming for a homogeneous TTFields distribution in the whole brain were investigated. MATERIAL AND METHODS We used computer simulations in a realistic computational head model of a 40+ years old man, constructed in-house from a T1 MRI series, to compute the field distributions obtained with various transducer array layouts. The distribution of TTFields delivered by pairs of transducer arrays at different positions on the head and neck was simulated using Sim4Life v3.0 (ZMT Zürich). For each layout, we determined and compared the mean and median field intensities in five pre-determined sections of the brain: (1) the cerebellum and brain stem together with other infra-tentorial anatomical regions; and (2–5) the four cerebral quadrants. RESULTS One array layout could be identified yielding median intensities between 1.5 V/cm to 1.7 V/cm in all areas and a homogeneous distribution within the brain. This layout is composed of one pair of arrays positioned on the right temple and left scapula, and the other pair positioned on the left temple and right scapula. CONCLUSION This study was able to determine a novel TTFields transducer array layout that might be used for treatment of the entire brain with therapeutic intensities, as would be beneficial in patients with brain metastases.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".