A Rare Early-Onset Fatal Complication after Transarterial Chemoembolization: A Case Report and Review of the Literature
Bibliographic record
Abstract
Transarterial chemoembolization (TACE) is a minimally invasive treatment for liver cancer, often employed as a bridging therapy or destination treatment for non-operable cases. This case report discusses an 82-year-old woman with a large hepatocellular carcinoma (HCC) who underwent elective TACE due to the high surgical risk associated with her tumor size. Unexpectedly, the patient experienced liver rupture 20 h post-procedure, leading to acute surgical intervention. Despite successful hemostasis during surgery, the patient succumbed to progressive multi-organ failure. We aimed to search the PubMed database for documented cases of ruptured HCC after TACE. This study highlights risk factors for spontaneous HCC rupture and specific factors associated with TACE-induced rupture. Transarterial embolization (TAE) is currently favored as the treatment method for spontaneous ruptures, while the optimal therapy for TACE-induced ruptures remains unclear. In conclusion, this case underscores the importance of recognizing the rare complication of HCC rupture post-TACE and the need for personalized risk assessment. While TAE emerges as a primary treatment choice, the lack of consensus necessitates further studies to establish evidence-based approaches for managing this uncommon yet life-threatening complication.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".