Clinical predictors of negative/equivocal SPECT imaging outcomes in primary hyperparathyroidism: Factors calling for 18F-choline-PET
Bibliographic record
Abstract
For minimally invasive surgery of parathyroid adenomas, exact localization diagnostics are essential. Main imaging modalities used for diagnostics are sonography, SPECT with/without CT (traditional imaging) and 18F-choline-PET. The aim of our study was to identify predictors for inconclusive SPECT imaging and subsequently determine in which cases 18F-choline-PET is needed. Retrospective analysis of 138 patients with histologically confirmed primary hyperparathyroidism (pHPT). After sonography, patients underwent SPECT or SPECT/CT imaging, with subsequent 18F-choline-PET in cases of disconcordant results. Logistic regression analysis was used to identify clinical and laboratory factors predictive for negative SPECT results. Sensitivity rates for sonography, SPECT, SPECT/CT, and choline-PET were 47 %, 49 %, 71.7 %, and 97 %, respectively. Logistic regression revealed lower PTH levels (p < 0.001), presence of structural thyroid disease (p = 0.018), and negative sonography (p < 0.001) as predictive of negative/equivocal SPECT outcome. An additional traditional imaging CT scan to a SPECT enhanced detection odds, as did greater adenoma weight. Urolithiasis, osteoporosis, and calcium values as measurement of activity and duration of disease showed no significant association with the detection rate. Furthermore, our study demonstrated that 18F-choline-PET exhibited remarkable sensitivity in detecting adenomas among patients with negative/equivocal SPECT results. Our study reveals potential predictive factors for a negative/equivocal SPECT outcome in pHPT. Identifying these factors might allow minimizing futile SPECT examinations and perhaps encourage timely utilization of 18F-choline-PET imaging. Our study reinforces the clinical significance of 18F-choline-PET, especially in complex cases with disconcordant results by conventional parathyroid imaging methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".