Treatment Planning Optimization in Irreversible Electroporation for Complete Ablation of Variously Sized Cervical Tumors: A Numerical Study
Bibliographic record
Abstract
Irreversible electroporation (IRE), a relatively new energy-based tumor ablation technology, has shown itself in the last decade to be able to safely ablate tumors with favorable clinical outcomes, yet little work has been done on optimizing the IRE protocol to variously sized tumors. Incomplete tumor ablation has been shown to be the main reason leading to the local recurrence and thus treatment failure. The goal of this study was to develop a general optimization approach to optimize the IRE protocol for cervical tumors in different sizes, while minimizing the damage to normal tissues. This kind of approach can lay a foundation for future personalized treatment of IRE. First, a statistical IRE cervical tumor death model was built using previous data in our group. Then, a multi-objective optimization problem model was built, in which the decision variables are five IRE-setting parameters, namely, the pulse strength (U), the length of active tip (H), the number of pulses delivered in one round between a pair of electrodes (A), the distance between electrodes (D), and the number of electrodes (N). The domains of the decision variables were determined based on the clinical experience. Finally, the problem model was solved by using nondominated sorting genetic algorithms II (NSGA-II) algorithm to give respective optimal protocol for three sizes of cervical tumors. Every protocol was assessed by the evaluation criterion established in the study to show the efficacy in a more straightforward way. The results of the study demonstrate this approach can theoretically provide the optimal IRE protocol for different sizes of tumors and may be generalizable to other types, sizes, and locations of tumors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".