The Role of Interventional Radiology in the Management of Hepatocellular Carcinoma
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (hcc) is one of the most common causes of cancer-related death worldwide. Overall, liver transplantation and resection are the only available treatments with potential for cure. Various locoregional therapies are widely used to manage patients with advanced hcc or as a bridging therapy for patients with early and intermediate disease. This article reviews and evaluates the role of interventional radiology in the management of such cases by assessing various aspects of each method, such as effect on rates of survival, recurrence, tumour response, and complications. METHODS: A systemic search of PubMed, medline, Ovid Medline In-Process, and the Cochrane Database of Systematic Reviews retrieved all related scientific papers for review. RESULTS: Needle core biopsy is a highly sensitive, specific, and accurate method for hcc grading. Portal-vein embolization provides adequate expansion of the future liver remnant, making more patients eligible for resection. In focal or multifocal unresectable early-stage disease, radiofrequency ablation tops all other thermoablative methods. However, microwave ablation is preferred in large tumours and in patients with Child-Pugh B disease. Cryoablation is preferred in recurrent disease and in patients who are poor candidates for anesthesia. Of the various transarterial modalities-transarterial chemoembolization (tace), drug-eluting beads, and transarterial radio-embolization (tare)-tace is the method of choice in Child-Pugh A disease, and tare is the method of choice in hcc cases with portal vein thrombosis. CONCLUSIONS: The existing data support the importance of a multidisciplinary approach in hcc management. Large randomized controlled studies are needed to provide clear indication guidelines for each method.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".