Gender, Race and Disease Etiology Predict De Novo Malignancy Risk After Liver Transplantation: Insights for Future Individualized Cancer Screening Guidance
Bibliographic record
Abstract
BACKGROUND: Malignancy after liver transplant (LT) is a leading cause of mortality, but data is limited. The aim of this study was to identify patients at higher risk for de novo malignancies after LT in a large multicenter database. METHODS: The Scientific Registry of Transplant Recipients database comprising all 108 412 LT recipients across the United States between 1987 and March 2015 was analyzed with a median follow-up of 6.95 years. Potential risk factors for malignancies after LT were assessed using Cox regression analysis for the outcome of time to first malignancy. RESULTS: Mean age 51.9 ± 10.8 years, 64.6% male, 74.5% white, and 15.8% with previous malignancy. Malignancies during follow-up were 4,483 (41.3%) skin, 1519 (14.0%) hematologic, and 4842 (44.7%) solid organ. The 10-year probability of de novo malignancy was 11.5% (11.3-11.8%). On multivariable analysis, age by decade (hazard ratio [HR], 1.52; P < 0.001), male sex (HR, 1.28; P < 0.001), white race (compared with other races: HR, 1.45-2.04; P < 0.001), multiorgan transplant (HR, 1.35; P < 0.001), previous malignancy (HR, 1.34; P < 0.001), and alcoholic liver disease, autoimmune, nonalcoholic steatohepatitis (HR, 1.35; P < 0.001), and primary sclerosing cholangitis pre-LT (compared with hepatitis C virus, P < 0.001) were associated with higher risk of post-LT malignancy, but type of immunosuppression was not (P = NS). CONCLUSIONS: This large data set demonstrates the effects of ethnicity/race and etiologies of liver disease, particularly nonalcoholic steatohepatitis as additional risk factors for cancer after LT. Patients with these high-risk characteristics should be more regularly and diligently screened.
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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) | 0.000 | 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) | 0.000 | 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".