A row-generation approach for simultaneous multiple needle trajectory planning in radiofrequency ablation
Bibliographic record
Abstract
Abstract Radiofrequency ablation is a thermal therapy for moderately-sized cancerous tumors. A target is killed with high temperatures obtained due to the current passed through one or more electrodes (needles) inserted into it. The needles’ trajectory must be meticulously planned to prevent interference with dense organs like bone or puncturing of critical structures like veins. By approximating the thermal lesion to an ellipse, we predefine several valid needle trajectories and then solve an integer programming model to identify pairwise valid needle positions, that meet clinical criteria, using a variation of the classic set cover model. To improve the models’ tractability and scalability, we use row generation-based decomposition techniques that determines pairwise validity using two different types of cuts. Finally, we analyze target and organ-at-risk (OAR) damage using several thermal damage models. Our method is tested on 12 liver targets: three targets each with four different surgical margins. We show promising results that meet clinical guidelines while obtaining full target coverage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".