Percutaneous Ablation of Hepatic Tumors at the Hepatocaval Confluence Using Irreversible Electroporation: A Preliminary Study
Bibliographic record
Abstract
BACKGROUND: Tumors at the hepatocaval confluence are difficult to treat, either surgically or ablatively. METHODS: A retrospective longitudinal study on patients ineligible for thermal ablation who underwent computed tomography-guided IRE for hepatic tumors at the hepatocaval confluence was conducted. Factors analyzed included patient and tumor characteristics, IRE procedure details, treatment-related complications, and prognosis. RESULTS: Between 2017 and 2021, 21 patients at our institute received percutaneous IRE. Of the 38 lesions, 21 were at the hepatocaval confluence. Complete ablation was achieved in all cases. Local and distant recurrence was observed in 4.8% (1/21) and 42.6% (9/21) of the ablated tumors, respectively. All postcava remained perfused at follow-up, except for 1 (4.8%) hepatic vein near the lesion found to be temporarily occluded and restored within 1 month. The ratio of the maximum diameter of ablation area at 1, 3, and 6 months post procedure compared to that immediately after IRE was 0.68 (0.50-0.84), 0.49 (0.27-0.61), and 0.38 (0.25-0.59), respectively. Progression-free survival of the patients with recurrence was 121 (range, 25-566) days. Four (19.0%) patients died at the end of follow-up with median overall survival of 451.5 (range, 25-716) days. CONCLUSIONS: IRE could be a safe and effective treatment for hepatic tumors at the hepatocaval confluence. This article provides valuable prognostic data; further clinical research is needed for better prognosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".