Efficacy of Contrast-Enhanced Ultrasound in Guiding Radiofrequency Ablation of Hepatocellular Carcinoma
Bibliographic record
Abstract
Purpose The objective of this retrospective study is to evaluate the efficacy of contrast-enhanced ultrasound (CEUS) in guiding treatment of hepatocellular carcinomas (HCCs) and hepatic metastases with percutaneous radiofrequency ablation (RFA). Material and methods From January 2010 to December 2015, a single-center retrospective review was conducted of all the RFAs performed at our institution. 295 RFA procedures were performed, 59 were CEUS-guided with a total of 49 patients (39 Males, average age 62.5 years (range: 32-87)) presenting with an average of 1.3 lesions. Pre-operative baseline imaging was performed with computed tomography (CT) or magnetic resonance imaging (MRI). Intra-operatively, CEUS images were compared to US images performed at the same RFA session to determine the ability of CEUS to increase the visualization of lesions. The efficacy of the treatment was evaluated with follow-up imaging for presence of residual or recurrent disease. Results Following administration of contrast agent, CEUS allowed for increased visualization in 54 (91.5%) of cases as indicated by increased flow signals or newly appeared signals. This enabled targeted treatment of the lesion, previously poorly appreciated on pre-operative imaging. With an average of 207 days of follow-up, 12 (20.3%) of the targeted lesions were incompletely treated or missed despite improved visualization as evidenced by reoccurrence on follow-up imaging. These lesions required re-intervention, which was subsequently successful in 100% of cases. Conclusion CEUS increases visualization and provides better characterization of hypervascular hepatic lesions not well appreciated on conventional US. Hence, this retrospective study suggests that contrast-enhanced ultrasound can be a promising adjunct to conventional image guided hepatic RFAs * Indicates faculty mentor.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".