Study of the relationship between the target tissue necrosis volume and the target tissue size in liver tumours using two-compartment finite element RFA modelling
Bibliographic record
Abstract
PURPOSE: The aim of this study was to investigate the relationship between the target tissue necrosis volume and the target tissue size during the radiofrequency ablation (RFA) procedure. MATERIALS AND METHODS: The target tissues with four different sizes (dxy = 20, 25, 30 and 35 mm) were modelled using a two-compartment radiofrequency ablation model. Different voltages were applied to seek the maximum target tissue necrosis volume for each target tissue size. The first roll-off occurrence or the standard ablation time (12 min) was taken as the sign for the termination of the RFA procedure. RESULTS: Four different maximum voltages without the roll-off occurrence were found for the four different sizes of target tissues (dxy = 20, 25, 30 and 35 mm), and they were 36.6, 35.4, 33.9 and 32.5 V, respectively. The target tissues with diameters of 20, 25 mm can be cleanly ablated at their own maximum voltages applied (MVA) but the same finding was not found for the 35-mm target tissue. For the target tissue with diameter of 30 mm, the 50 °C isothermal contour (IT50) result showed that the target tissue can be cleanly ablated, but the same result did not show in the Arrhenius damage model result. Furthermore, two optimal RFA protocols with a minimal thermal damage to the healthy tissues were found for the target tissues with diameters of 20 and 25 mm, respectively. CONCLUSIONS: The study suggests that target tissues of different sizes should be treated with different RFA protocols. The maximum target tissue volume was achieved with the MVA without the roll-off occurrence for each target tissue size when a constant RF power supply was used.
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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 | 0.000 |
| 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 | 0.001 | 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".