Tumor Treating Fields: At the Crossroads Between Physics and Biology for Cancer Treatment
Bibliographic record
Abstract
Despite extraordinary advances that have been achieved in the last few decades, cancer continues to represent a leading cause of mortality worldwide. Lethal cancer types ultimately become refractory to standard of care approaches; thus, novel effective treatment options are desperately needed. Tumor Treating Fields (TTFields) are an innovative non-invasive regional anti-mitotic treatment modality with minimal systemic toxicity. TTFields are low intensity (1–3 V/cm), intermediate frequency (100–300 kHz) alternating electric fields delivered to cancer cells. In patients, TTFields are applied using FDA-approved transducer arrays, orthogonally positioned on the area surrounding the tumor region, with side effects mostly limited to the skin. The precise molecular mechanism of the anti-tumor effects of TTFields is not well understood, but preclinical research on TTFields suggests it may act during two phases of mitosis: at metaphase, by disrupting the formation of the mitotic spindle, and at cytokinesis, by dielectrophoretic dislocation of intracellular organelles leading to cell death. This review describes the mechanism of action of TTFields and provides an overview of the most important in vitro studies that investigate the disruptive effects of TTFields in different cancer cells, focusing mainly on anti-mitotic roles. Lastly, we summarize completed and ongoing TTFields clinical trials on a variety of solid tumors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".