3D US-Based Evaluation and Optimization of Tumor Coverage for US-Guided Percutaneous Liver Thermal Ablation
Bibliographic record
Abstract
Complete tumor coverage by the thermal ablation zone and with a safety margin (5 or 10 mm) is required to achieve the entire tumor eradication in liver tumor ablation procedures. However, 2D ultrasound (US) imaging has limitations in evaluating the tumor coverage by imaging only one or multiple planes, particularly for cases with multiple inserted applicators or irregular tumor shapes. In this paper, we evaluate the intra-procedural tumor coverage using 3D US imaging and investigate whether it can provide clinically needed information. Using data from 14 cases, we employed surface- and volume-based evaluation metrics to provide information on any uncovered tumor region. For cases with incomplete tumor coverage or uneven ablation margin distribution, we also proposed a novel margin uniformity -based approach to provide quantitative applicator adjustment information for optimization of tumor coverage. Both the surface- and volume-based metrics showed that 5 of 14 cases had incomplete tumor coverage according to the estimated ablation zone. After applying our proposed applicator adjustment approach, the simulated results showed that 92.9% (13 of 14) cases achieved 100% tumor coverage and the remaining case can benefit by increasing the ablation time or power. Our proposed method can evaluate the intra-procedural tumor coverage and intuitively provide applicator adjustment information for the physician. Our 3D US-based method is compatible with the constraints of conventional US-guided ablation procedures and can be easily integrated into the clinical workflow.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".