Design of a Novel Electrode of Radiofrequency Ablation for Large Tumors: A Finite Element Study
Bibliographic record
Abstract
Objective: The aim of this study was to design a novel radiofrequency (RF) electrode for larger and more round ablation volumes and its ability to achieve the complete ablation of liver tumors (> 3 cm in diameter) using finite element method. Methods: A new RF expandable electrode comprising three parts (i.e., insulated shaft, changing shaft, and hooks) was designed. Two modes of this new electrode (i.e., monopolar expandable electrode (MEE) and hybrid expandable electrode (HEE)) and a commercial expandable electrode (CEE) were investigated using liver tissue with and without liver tumor. A temperature-controlled radiofrequency ablation (RFA) protocol with a target temperature of 95 °C and an ablation time of 15 minutes was used in this study. Both the volume and shape of the ablation zone were studied for all RF electrodes. A large liver tumor with the diameter of 3.5 cm was used to evaluate the effectiveness on the complete ablation of the new designed electrode. Results: In the first scenario (without liver tumor), the ablation volumes of CEE, HEE, and MEE were 9.96 cm3, 41.0 cm3, and 46.14 cm3, respectively. The values of sphericity index (SI) of CEE, HEE, and MEE were 0.36, 0.94, and 0.98, respectively. The best performance was achieved by the MEE electrode. In the second scenario (with liver tumor), the ablation volumes of MEE and CEE were 67.56 cm3 and 20.62 cm3, respectively. Also, a rounder ablation volume was generated by MEE compared to CEE (SI: 0.98 vs 0.55). Conclusion: This study concludes that compared with CEE, both MEE and HEE are able to get larger and more round ablation volumes due to the larger electrode-tissue interface and more round shape of hooks; compared with HEE, MEE is better to get a larger and rounder ablation volume; MEE is able to ablate a large liver tumor (i.e., 3.5 cm in diameter) completely.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".