Optimization of Electrode Configuration and Pulse Strength in Irreversible Electroporation for Large Ablation Volumes Without Thermal Damage
Bibliographic record
Abstract
The aim of this study was to analyze five factors that are responsible for the ablation volume and maximum temperature during the procedure of irreversible electroporation (IRE). The five factors used in this study were the pulse strength (U), the electrode diameter (B), the distance between the electrode and the center (D), the electrode length (L), and the number of electrodes (N). A validated finite element model (FEM) of IRE was built to collect the data of the ablation volume and maximum temperature generated in a liver tissue. Twenty-five experiments were performed, in which the ablation volume and maximum temperature were taken as response variables. The five factors with ranges were analyzed to investigate their impacts on the ablation volume and maximum temperature, respectively, using analysis of variance. Response surface method (RSM) was used to optimize the five factors for the maximum ablation volume without thermal damage (the maximum temperature ≤ 50 °C for 90 s). U and L were found with significant impacts on the ablation volume (P < 0.001, and P = 0.009, respectively) while the same conclusion was not found for B, D and N (P = 0.886, P = 0.075 and P = 0.279, respectively). Furthermore, U, D, and N had the significant impacts on the maximum temperature with P < 0.001, P < 0.001, and P = 0.003, respectively, while same conclusion was not found for B and L (P = 0.720 and P = 0.051, respectively). The maximum ablation volume of 2952.9960 mm3 without thermal damage can be obtained by using the following set of factors: U = 2362.2384 V, B = 1.4889 mm, D = 7 mm, L = 4.5659 mm, and N = 3. The study concludes that both B and N have insignificant impacts (P = 0.886, and P = 0.279, respectively) on the ablation volume; U has the most significant impact (P < 0.001) on the ablation volume; electrode configuration and pulse strength in IRE can be optimized for the maximum ablation volume without thermal damage using RSM.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".