Design of a Novel Electrode of Radiofrequency Ablation for Large Tumors: A Finite Element Study
Bibliographic record
Abstract
The aim of the study was to design a novel radiofrequency (RF) electrode for larger and rounder ablation volumes and its ability to achieve the complete ablation of liver tumors larger than 3 cm in diameter using finite element method. A new RF expandable electrode comprising three parts (i.e., insulated shaft, changing shaft, and hooks) was designed. Two modes of this new electrode, such as monopolar expandable electrode (MEE) and hybrid expandable electrode (HEE), and a commercial expandable electrode (CEE) were investigated using liver tissue with (scenario I) and without (scenario II) a liver tumor. A temperature-controlled radiofrequency ablation (RFA) protocol with a target temperature of 95 °C and an ablation time of 15 min was used in the study. Both the volume and shape of the ablation zone were examined for all RF electrodes in scenario I. Then, the RF electrode with the best performance in scenario I and CEE were used to ablate a large liver tumor with the diameter of 3.5 cm (scenario II) to evaluate the effectiveness of complete tumor ablation of the designed RF electrode. In scenario I, the ablation volumes of CEE, HEE, and MEE were 12.11 cm3, 33.29 cm3, and 48.75 cm3, respectively. The values of sphericity index (SI) of CEE, HEE, and MEE were 0.457, 0.957, and 0.976, respectively. The best performance was achieved by using MEE. In scenario II, the ablation volumes of MEE and CEE were 71.59 cm3 and 19.53 cm3, respectively. Also, a rounder ablation volume was achieved by using MEE compared to CEE (SI: 0.978 versus 0.596). The study concluded that: (1) compared with CEE, both MEE and HEE get larger and rounder ablation volumes due to the larger electrode–tissue interface and rounder shape of hook deployment; (2) MEE has the best performance in getting a larger and rounder ablation volume; and (3) computer simulation result shows that MEE is also able to ablate a large liver tumor (i.e., 3.5 cm in diameter) completely, which has at least 0.785 cm safety margin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".