High‐frequency irreversible electroporation for gliomas: A feasibility study using patient‐specific finite element models
Bibliographic record
Abstract
BACKGROUND: High-frequency irreversible electroporation (H-FIRE) has gradually become an attractive alternative treatment of intracranial tumors due to its clinically favorable characteristics, such as mild muscle contractions, precise ablation margins, and preservation of vessel structures. Encouraging results have been obtained in pre-clinical trials with animal models. However, a more comprehensive understanding of spatiotemporal distributions of electric field and temperature in clinically relevant intracranial tissue during the treatment of H-FIRE is still required prior to its clinical implementation. PURPOSE: In this study, we performed the first attempt to numerically investigate the electric field and temperature distributions for the conformal ablation of intracranial tumors in patient-specific glioma tumor models. METHODS: Four representative 3D patient-specific glioma models were constructed based on T1-weighted MR images of four clinical patients. The treatment protocols of H-FIRE were optimized for the conformal ablation of these glioma patients by using a multi-objective optimization genetic algorithm. To alleviate the temperature increase during the H-FIRE administration, a new ablation procedure was designed and tested numerically. RESULTS: The results achieved in this study demonstrated that the conformal ablation of gliomas with differing sizes and shapes can be achieved by optimizing the number of electrodes, applied pulse voltage, active tip length, electrode gap, and electrode insertion depth. The temperature increases due to the administration of H-FIRE pulses can be effectively alleviated by introducing a pulse-off time between two ablation procedures. CONCLUSION: This study contributes to the field of H-FIRE in the treatment of intracranial tumors and promotes its clinical implementation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".