Inverse planning for radiofrequency ablation in cancer therapy using multiple damage models
Bibliographic record
Abstract
Abstract Radiofrequency ablation (RFA) offers localized and minimally invasive treatment of small-to-medium sized inoperable tumors. In RFA, tissue is ablated with high temperatures obtained from electrodes (needles) inserted percutaneously or via an open surgery into the target. RFA treatments are generally not planned in a systematic way, and do not account for nearby organs-at-risk (OARs), potentially leading to sub-optimal treatments and inconsistent treatment quality. We therefore develop a mathematical framework to design RFA treatment plans that provide complete ablation while minimizing healthy tissue damage. Borrowing techniques from radiosurgery inverse planning, we design a two-stage approach where we first identify needle positions and orientations, called needle orientation optimization, and then compute the treatment time for optimal thermal dose delivery, called thermal dose optimization. Several different damage models are used to determine both target and OAR damage. We present numerical results on three clinical case studies. Our findings indicate a need for high source voltage for short tip length (conducting portion of the needle) or fewer needles, and low source voltage for long tip length or more needles to achieve full coverage. Further, more needles yields a larger ablation volume and consequently more OAR damage. Finally, the choice of damage model impacts the source voltage, tip length, and needle quantity.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".