Magnitude of Change in Alpha-Fetoprotein in Response to Transarterial Chemoembolization Predicts Survival in Patients Undergoing Liver Transplantation for Hepatocellular Carcinoma
Bibliographic record
Abstract
BACKGROUND: Downsizing strategies are often attempted for patients with hepatocellular carcinoma (hcc) before liver transplantation (lt). The objective of the present study was to determine clinical predictors of favourable survival outcomes after transarterial chemoembolization (tace) before lt for hcc outside the Milan criteria, so as to better select candidates for this strategy. METHODS: In this retrospective study, patients with hcc tumours either beyond Milan criteria (single lesion > 5 cm, 3 lesions with 1 or more > 3 cm) or at the upper limit of Milan criteria (single lesions between 4.1 cm and 5.0 cm), with a predicted waiting time of more than 3 months, received carboplatin-based tace treatments. Exclusion criteria for tace included Child-Pugh C cirrhosis or the presence of portal vein invasion or extrahepatic disease on imaging. Only patients without tumour progression after tace underwent lt. RESULTS: Of 160 hcc patients who received liver grafts between 1997 and 2010, 35 were treated with tace preoperatively. The median of the sum of tumour diameters was 6.7 cm (range: 4.8-8.5 cm), which decreased with tace to 5.0 cm (range: 3.3-7.0 cm) at transplantation (p < 0.0004). The percentage drop in alpha-fetoprotein (αfp) was a positive predictor (p = 0.0051) and the time from last tace treatment to transplantation was a negative predictor (p < 0.0001) for overall survival. CONCLUSIONS: The percentage drop in αfp and a shorter time from the final tace treatment to transplantation significantly predicted improved overall survival after lt for hcc downsized with tace. As a serum marker, αfp should be followed when tace is used as a strategy to stabilize or downsize hcc lesions before lt.
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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) | 0.000 | 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".