Image-guided Tumor Ablation: Standardization of Terminology and Reporting Criteria—A 10-Year Update
Bibliographic record
Abstract
Image-guided tumor ablation has become a well-established hallmark of local cancer therapy. The breadth of options available in this growing field increases the need for standardization of terminology and reporting criteria to facilitate effective communication of ideas and appropriate comparison among treatments that use different technologies, such as chemical (eg, ethanol or acetic acid) ablation, thermal therapies (eg, radiofrequency, laser, microwave, focused ultrasound, and cryoablation) and newer ablative modalities such as irreversible electroporation. This updated consensus document provides a framework that will facilitate the clearest communication among investigators regarding ablative technologies. An appropriate vehicle is proposed for reporting the various aspects of image-guided ablation therapy including classification of therapies, procedure terms, descriptors of imaging guidance, and terminology for imaging and pathologic findings. Methods are addressed for standardizing reporting of technique, follow-up, complications, and clinical results. As noted in the original document from 2003, adherence to the recommendations will improve the precision of communications in this field, leading to more accurate comparison of technologies and results, and ultimately to improved patient outcomes. Online supplemental material is available for this article .
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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 | 0.085 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".