EXTH-05. TOWARD 100% CLINICAL EFFICACY FOR TUMOR-TREATING FIELDS
Bibliographic record
Abstract
Abstract In vitro and in animal models of various tumor cell types, empirical data suggest tumor-treating fields (TTFields) can attain 100% efficacy, with two pre-conditions: 1) Delivering sufficient electric field strength to the target tumor cells, and 2) Changing the direction of the imposed electric field such that intra-cellular structures at any angle with respect to the field see the required efficacious field strength. Requirement #2 reflects empirical findings as well as prevailing theories of TTFields’ mechanism of action, which center on mitosis disruption via energy imparted to polarizable intracellular structures aligned with the field. We tried various trial curve-fits to TTFields empirical dose-response curves. Quadratic formulae yield a close fit, resembling well-known radiotherapy linear-quadratic (LQ) curves and suggesting a connection between the mechanisms of the two treatment modalities: F98 (rat glioma): -16.3271x2 - 0.664019x+100 (R2=0.9995); MDA-MB-231 (human breast cancer): -9.64922x2 + 2.96348x + 100 (R2=0.9999); H1299 (human non-small cell lung carcinoma): -12.0515x2 - 1.58272x + 100 (R2=0.9993); B16 (mouse melanoma): -83.8066x2 + 47.4537x + 100 (R2=0.9954). We extrapolate these fits to predict the approximate required field strength seen at the target cells for 100% efficacy: F98: 2.45 V/cm; MDA-MB-231: 3.4 V/cm; H1299: 2.8 V/cm; B16: 1.4 V/cm. Alternatively, the LQ fits may not reflect the extrapolated high amplitude range for which data is lacking. Under two assumptions, 1) intracellular structures are randomly oriented in vivo, and 2) only two changes of TTFields direction are used, as is the current clinical practice, then extrapolating the dose-response curves predicts that infinite field strength is required for 100% efficacy, since some intracellular structures must be oriented at 90 degrees to the imposed field and the cosine of 90 degrees is zero. Adding a third TTFields direction, not necessarily orthogonal to the plane of the other two directions, can solve this problem.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".