[18F]FDG PET/CT and PET/MR in Patients with Adrenal Lymphoma: A Systematic Review of Literature and a Collection of Cases
Bibliographic record
Abstract
Aim. The present study aimed to assess the existing data about Primary Adrenal Lymphoma (PAL) evaluated with FDG PET and to describe a small monocentric series of cases. A systematic analysis (from 2010 to 2022) was made by using PubMed and Web of Science databases reporting data about the role of FDG PET/CT in patients with suspicious or known adrenal lymphoma. The quality of the papers was assessed by using QUADAS-2 criteria. Moreover, from a single institutional collection between 2010 and 2021, data from patients affected by adrenal lymphoma and undergoing contrast-enhanced compute tomography (ceCT)/magnetic resonance (MR) and FDG PET/CT or PET/MR were retrieved and singularly described. Seventy-eight papers were available from PubMed and 25 from Web of Science. Forty-seven (Nr. 47) Patients were studied, most of them in the initial staging of disease (n = 42; 90%). Only in one paper, the scan was made before and after therapy. The selected clinical cases were relative to the initial staging of disease, the restaging, and the evaluation of response to therapy. PET/CT and PET/MR always showed a high FDG uptake in the primary adrenal lesions and in metastatic sites. Moreover, PET metrics, such as maximum standardized uptake value (SUVmax) and metabolic tumor volume (MTV), were elevated in all primary adrenal lesions. In conclusions, FDG PET either coupled with CT or MRI can be useful in staging, restaging, and for the evaluation of treatment response in patients affected by PAL
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".