Living Donor Liver Transplantation Versus Deceased Donor Liver Transplantation for Hepatocellular Carcinoma: Comparable Survival and Recurrence
Bibliographic record
Abstract
Several studies have reported higher rates of recurrent hepatocellular carcinoma (HCC) after living donor liver transplantation (LDLT) versus deceased donor liver transplantation (DDLT). It is unclear whether this difference is due to a specific biological effect unique to the LDLT procedure or to other factors such as patient selection. We compared the overall survival (OS) rates and the rates of HCC recurrence after LDLT and DDLT at our center. Between January 1996 and September 2009, 345 patients with HCC were identified: 287 (83%) had DDLT and 58 (17%) had LDLT. The OS rates were calculated with the Kaplan-Meier method, whereas competing risks methods were used to determine the HCC recurrence rates. The LDLT and DDLT groups were similar with respect to most clinical parameters, but they had different median waiting times (3.1 versus 5.3 months, P = 0.003) and median follow-up times (30 versus 38.1 months, P = 0.02). The type of transplant did not affect any of the measured cancer outcomes. The OS rates at 1, 3, and 5 years were equivalent: 91.3%, 75.2%, and 75.2%, respectively, for the LDLT group and 90.5%, 79.7%, and 74.6%, respectively, for DDLT (P = 0.62). The 1-, 3-, and 5-year HCC recurrence rates were also similar: 8.8%, 10.7%, and 15.4%, respectively, for the LDLT group and 7.5%, 14.8%, and 17.0%, respectively, for the DDLT group (P = 0.54). A regression analysis identified microvascular invasion (but not the graft type) as a predictor of HCC recurrence. In conclusion, in well-matched cohorts of LDLT and DDLT recipients, LDLT and DDLT provide similarly low recurrence rates and high survival rates for the treatment of HCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".