Evolving Role of [18F]Flurocholine PET/CT in Assessing Primary Hyperparathyroidism: Can It Be Considered the First-Line Functional Imaging Approach?
Bibliographic record
Abstract
The accurate detection of hyperfunctioning parathyroid tissue (HFPT) is pivotal in the preoperative assessment of primary hyperparathyroidism (PHPT). PET/CT using [18F]fluorocholine ([18F]FCH) showed superior diagnostic performance compared to conventional functional imaging modalities. We aimed to evaluate the diagnostic performance of [18F]FCH PET/CT as a first-line functional imaging approach in patients with clinically diagnosed PHPT. The imaging and clinical data of 321 PHPT patients, including 271 overt PHPT and 50 mild PHPT, who underwent [18F]FCH PET/CT as first-line imaging were analysed in this retrospective study. Histopathology was the reference standard. In case of no available histopathology evaluation (conservative management), imaging and clinical follow-ups were considered reference standards. In the overt group (n = 271), [18F]FCH PET/CT showed sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of 0.99, 0.91, 1.00, 0.80, and 0.99, respectively. Regarding the correlation of the index lesions and initial laboratory data, all [18F]FCH PET/CT parameters (SUVs, SULs, and mSAD) were significantly correlated with the serum iPTH level. Additionally, SUVmax, SULpeak, and mSAD were significantly associated with the serum calcium level. In the mild group (n = 50), [18F]FCH PET/CT showed a sensitivity, specificity, PPV, NPV, and accuracy of 0.93, 0.75, 0.95, 0.67, and 0.90. In conclusion, [18F]FCH PET/CT revealed high diagnostic performance in the detection of HFPTs and the potential to be considered as a first-line imaging modality in the assessment of PHPT, including both overt and mild types. However, its cost–benefit concerning the clinical impact of early PHPT detection should be investigated in future studies.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".