Malignancy after Solid Organ Transplantation: Comprehensive Imaging Review
Bibliographic record
Abstract
Life expectancies for solid organ recipients as well as graft survival rates for these patients have improved over the years because of advanced immunosuppressive therapies; however, with chronic use of these drugs, posttransplant malignancy has become one of the leading causes of morbidity for them. The risk of carcinogenesis in transplant recipients is significantly higher than for the general population and cancers tend to manifest at an advanced stage. Posttransplant malignancies are thought to develop by three mechanisms: de novo development, donor-related transmission, and recurrence of a recipient's pretransplant malignancy. Although nonmelanoma skin cancer, Kaposi sarcoma, posttransplant lymphoproliferative disorder, anogenital cancer, and lung cancer are malignancies that are thought to arise de novo, malignant melanoma and cancers that arise in the renal allograft are frequently donor related. Hepatocellular carcinomas and cholangiocarcinomas have a greater tendency to recur in liver transplant recipients. An altered or deranged immune system caused by chronic immunosuppression is considered to be one of the major contributing factors to carcinogenesis. The proposed pathogenic mechanisms for oncogenesis include impaired immunosurveillance of neoplastic cells, weakened immune activity against oncogenic viruses, and direct carcinogenic effects of immunosuppressive agents. Imaging plays an important role in screening, follow-up, and long-term surveillance in patients with malignancies because key imaging features can guide in their timely diagnosis. However, some benign entities such as transplant-related renal fibrosis, biliary necrosis, and infectious nodules in the lungs mimic malignancies and require pathologic confirmation. Management strategies that can improve malignancy-related morbidity and mortality in transplant recipients include prevention of risk factors, appropriate modulation of immunosuppressive agents, prophylaxis against infection-related malignancies, and use of intensive targeted screening programs. (©)RSNA, 2016.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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".