Radiofrequency ablation for liver tumors abutting complex blood vessel structures: treatment protocol optimization using response surface method and computer modeling
Bibliographic record
Abstract
Objective To achieve a result of a large tumor ablation volume with minimal thermal damage to the surrounding blood vessels by designing a few clinically-adjustable operating parameters in radiofrequency ablation (RFA) for liver tumors abutting complex vascular structures.Methods Response surface method (RSM) was employed to correlate the ablated tumor volume (Ra) and thermal damage to blood vessels (Dt) based on RFA operating parameters: ablation time, electrode position, and insertion angle. A coupled electric-thermal-fluid RFA computer model was created as the testbed for RSM to simulate RFA process. Then, an optimal RFA protocol for the two conflicting goals, namely (1) large tumor ablation and (2) small thermal damage to the surrounding blood vessels, has been achieved under a specific ablation environment.Results Linear regression analysis confirmed that the RFA protocol significantly affected Ra and Dt (the adjusted coefficient of determination Radj2 = 93.61% and 95.03%, respectively). For a proposed liver tumor scenario (liver tumor with a dimension of 4×3×2.9 cm3 abutting a complex vascular structure), an optimized RFA protocol was found based on the regression results in RSM. Compared with a reference RFA protocol, in which the electrode was centered in the tumor with a 12-min ablation time, the optimized RFA protocol has increased Ra from 98.1% to 99.6% and decreased Dt from 4.1% to 0.4%, achieving nearly the complete ablation of proposed liver tumor and ignorable thermal damages to vessels.Conclusion This work showed that it is possible to design a few clinically-adjustable operating parameters of RFA for achieving a large tumor ablation volume while minimizing thermal damage to the surrounding blood vessels.
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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 | 0.000 | 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".