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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".