Tissue Property Characterization and Radiofrequency Ablation Enhancement by Electroporation: An In Vivo and Computational Study
Bibliographic record
Abstract
Radiofrequency ablation (RFA) is an energy-based tumor ablation modality that heats tissues by delivering radiofrequency current. The tissue properties of both electrical and thermal conductivities dominate the efficiency of RFA. This study aims to investigate the potential of electroporation (EP) pretreatment to enhance RFA by increasing the electrical and thermal conductivities of tissues. A rabbit liver model was used in an in vivo experiment to do the following: 1) characterize the change of electrical conductivity and thermal conductivity after various EP protocols and 2) explore the RFA enhancement with previous EP by comparing the ablation zone among the groups of different ablation protocols. Furthermore, a computer model, validated by the in vivo experiment, was built to study the enhancing effect of previous EP on RFA treatments. Compared with untreated tissues, the maximum electrical and thermal conductivities of liver tissues treated by EP treatments could be obtained at the electric field strengths of 2500 and 1500 V/cm, respectively, which increased by 16.54% (from$0.29~\pm ~0.11$to$0.34~\pm ~0.01$S/m) and 65.17% [from$0.71~\pm ~0.03$to$1.18~\pm ~0.12$W/(m$\cdot $K)], respectively. The areas of ablation zone in the in vivo experiment were$56.35~\pm ~5.93$,$63.52~\pm ~13.73$, and$80.82~\pm ~11.91$mm2 following the EP of 0, 375, and 750 V/cm, respectively. Correspondingly, the ablation zone in the computer simulation was increased to 73.26 mm2 with an EP of 750 V/cm compared to 50.97 mm2 with no EP. The study concludes that the ablation effectiveness of RFA can be enhanced by EP treatment just prior to RFA, and the enhancement positively correlates with the applied electric field strengths.
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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 | 0.000 |
| 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 | 0.001 | 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".