The Role of Active Brown Adipose Tissue in Patients With Pheochromocytoma or Paraganglioma
Bibliographic record
Abstract
OBJECTIVES: fluorodeoxyglucose positron emission tomography (FDG-PET) imaging in patients with pheochromocytoma or paraganglioma (PPGL). In addition to its clinical significance, we aimed to explore the prevalence of this finding on FDG-PET imaging in patients with PPGL. METHODS: We conducted a systematic review and meta-analysis of prospective and retrospective studies. Publications were identified through searches in MEDLINE/PubMed, Embase, and SCOPUS from inception until 2022-11-26, with an update check performed on 2024-05-02. Eligible studies included patients with PPGL who had completed FDG-PET imaging. Data on catecholamine levels stratified by the presence of aBAT were extracted and pooled using the random-effects model with the inverse variance method. For the quantitative synthesis, we used standardized mean differences and meta-analysis of proportions. A risk of bias assessment was performed using the Quality in Prognostic Studies tool. RESULTS: Our search yielded 6 studies suitable for inclusion. Pooled data showed a statistically significant positive difference in isolated demethylated catecholamine levels in aBAT positive groups compared to aBAT negative. No significant differences were found in multiple domains, including tumor size, tumor burden, germline mutations, or location. The proportion of patients with PPGL who present with aBAT stands at approximately 25%. CONCLUSIONS: The demethylated metabolite levels could have potential use in predicting the presence of active brown adipose tissue in patients with PPGL. There is no convincing evidence of increased aBAT prevalence in patients with PPGL and germline mutations. There was, however, evidence suggesting that the presence of aBAT may confer poorer outcomes and decreased life expectancy.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".