PET detectives: Molecular imaging for phaeochromocytomas and paragangliomas in the genomics era
Bibliographic record
Abstract
Abstract Phaeochromocytomas and paragangliomas (PPGLs) are rare tumours that arise from the adrenal medulla or extra‐adrenal sympathetic or parasympathetic paraganglia. Recent advances in genetics have greatly enhanced understanding of the pathogenesis and molecular physiology of PPGL. Concomitantly, advances in molecular imaging mean four techniques are now available for use in PPGLs: [ 123 I]‐MIBG coupled with SPECT/CT; [ 18 F]‐ FDG, [ 68 Ga]‐DOTATATE and [ 18 F]‐FDOPA coupled with PET/CT. Each modality relies on unique cellular uptake mechanisms that are contingent upon the tumour's molecular behaviour—which, in turn, is determined by the tumour's genetic profile. This genotype‐phenotype correlation means the appropriate choice of radiotracer may depend on the known (or suspected) underlying genetic mutation, in addition to the clinical indication for the scan—whether confirming diagnosis, staging disease, surveillance or determining eligibility for radionuclide therapy. Given these rapid recent changes in genetic understanding and molecular imaging options, many clinicians find it challenging to choose the most appropriate scan for an individual with PPGL. To this end, recent guidelines published by the European Association of Nuclear Medicine and the Society of Nuclear Medicine and Molecular Imaging (EANM/SNMMI) have detailed the preferred radiotracer choices for individuals with PPGL based on their genotype and/or clinical presentation, providing timely clarity in this rapidly moving field. The current review summarizes the implications of the genotype‐phenotype relationship of PPGL, specifically relating this to the performance of molecular imaging modalities, to inform and enable practising endocrinologists to provide tailored, personalized care for individuals with PPGL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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".