Clinical Features of de Novo Lung Neuroendocrine Tumor after Liver Transplantation for Hepatocellular Carcinoma
Bibliographic record
Abstract
Objective: To analyze the clinical features of de novo lung neuroendocrine tumor (NET) after liver transplantation (LT) for hepatocellular carcinoma (HCC).
 Method: Retrospectively reviewed the clinical data of the 1253 patients who underwent LT from 2013 to 2022 in our institute.
 Result: Out of 1253 recipients of LT 7 patients suffered de novo lung carcinoma, of these 2 patients suffered lung NET accounting for 28.6% (2/7) of de novo lung carcinoma both at extensive stage. New on-set lung lesions and hilar and mediastinal lymphadenopathy were found by imaging tests; and were diagnosed as lung NETs in both patients through pathological examination. The interval between LT and diagnosis of lung NET ranged from 5.9 to 44.7 months. Both patients received cisplatin and etoposide as first-line chemotherapy and achieved partial remission. The progression-free survival period ranged from 1.9 to 2.2 months. Survival after diagnosis of lung NET ranged from 7.0 to 10.9 months. One of the patients tried to cease immunosuppressants during chemotherapy and incurred graft rejection.
 Conclusion: Lung NET may have a higher proportional incidence of de novo lung carcinoma in LT recipients. Early diagnosis is vital for the treatment of lung NET, while predictive and timely biopsy based on imaging findings is crucial for making an early diagnosis.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".