Concurrent Presentation of High-Grade Lymphoma and Metastatic Pancreatic Neuroendocrine Tumor 14 Years After Renal Transplant
Bibliographic record
Abstract
The development of malignancy, especially lymphoma, is common after solid organ transplant. However, concurrent malignancies are rare and result in a diagnostic and treatment dilemma, particularly in the post-transplant setting. We present a case of a 78-year-old male who was discovered to have a high-grade post-transplant lymphoproliferative disorder (PTLD) and metastatic pancreatic neuroendocrine tumor (PNET) 14 years after kidney transplant. He presented with abdominal pain and at surgical resection was found to have a large small intestine tumor that was consistent with high-grade diffuse large B cell lymphoma. Immune suppression was reduced, and staging workup was completed. Positron emission tomography-computed tomography (PET-CT) showed fluorodeoxyglucose (FDG)-avid metastatic lesions in the liver and a mass in the pancreatic head. Before treatment was initiated, biopsy of a liver lesion revealed metastatic PNET. Due to aggressiveness and potential high mortality of the lymphoma, he was started on rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP). After five cycles he developed worsening abdominal pain consistent with progression of the PNET and was placed on everolimus. Here we discuss the complexity of diagnosing concurrent primaries and the treatment of such in the post-transplant setting. J Hematol. 2014;3(4):112-115 doi: http://dx.doi.org/10.14740/jh180w
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".