INCIDENCE AND PREDICTORS OF POST-TRANSPLANT MALIGNANCY IN LIVER TRANSPLANT RECIPIENTS: A SINGLE-CENTRE COHORT STUDY
Bibliographic record
Abstract
Cancer is a major long-term complication after liver transplantation (LT). We aimed to characterize its incidence and risk factors in LT recipients at a Canadian center. Retrospective cohort of patients who underwent LT from 2007–2018, with at least 60 months of follow-up. Demographic, clinical, and oncologic data were analyzed using Cox regression. We included 575 LT recipients (30% women, median age 53.6 years). Main etiologies were ALD (22.9%), HCV (20.8%), MASLD (13.3%), and PSC (11.1%). HCC was present at LT in 25.7%. During follow-up, 55 patients (9.7%) developed cancer, most commonly non-melanoma skin cancer (3.5% of all recipients), followed by head and neck tumors (1.0%), PTLD (0.9%), gastrointestinal cancers (0.9%), and neuroendocrine tumors (0.7%). Mean time to cancer diagnosis was 27.3±19.1 months. Additionally, 20 patients (3.4%) developed post-LT HCC, with 90% being recurrences. Cancer types differed by underlying etiology (p=0.004): non-melanoma skin cancers predominated in SLD and viral hepatitis, while GI cancers were most common in autoimmune liver disease. Among those with prior HCC, 16.8% developed a non-HCC cancer, mostly skin cancer. In multivariate analysis, pre-LT HCC was independently associated with post-LT non-HCC cancer (HR 2.66; 95% CI 1.39–5.07; p=0.003), while age, gender, alcohol or tobacco use, and liver disease etiology were not. Cancer occurred in nearly 10% of LT recipients, mostly within three years. A history of HCC at LT tripled the risk of subsequent non-HCC cancer, highlighting the importance of targeted cancer surveillance in this high-risk population.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".