Percutaneous Liver Tumour Ablation: Image Guidance, Endpoint Assessment, and Quality Control
Bibliographic record
Abstract
Liver tumour ablation nowadays represents a routine treatment option for patients with primary and secondary liver tumours. Radiofrequency ablation and microwave ablation are the most widely adopted methods, although novel techniques, such as irreversible electroporation, are quickly working their way up. The percutaneous approach is rapidly gaining popularity because of its minimally invasive character, low complication rate, good efficacy rate, and repeatability. However, matched to partial hepatectomy and open ablations, the issue of ablation site recurrences remains unresolved and necessitates further improvement. For percutaneous liver tumour ablation, several real-time imaging modalities are available to improve tumour visibility, detect surrounding critical structures, guide applicators, monitor treatment effect, and, if necessary, adapt or repeat energy delivery. Known predictors for success are tumour size, location, lesion conspicuity, tumour-free margin, and operator experience. The implementation of reliable endpoints to assess treatment efficacy allows for completion-procedures, either within the same session or within a couple of weeks after the procedure. Although the effect on overall survival may be trivial, (local) progression-free survival will indisputably improve with the implementation of reliable endpoints. This article reviews the available needle navigation techniques, evaluates potential treatment endpoints, and proposes an algorithm for quality control after the procedure.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".