Role of four-dimensional computer tomography (4D-CT) in non-localising and discordant first-line imaging in primary hyperparathyroidism
Bibliographic record
Abstract
Background Accurate preoperative localisation of parathyroid adenoma is imperative for the success of minimally invasive parathyroidectomy (MIP). Objective Our study aimed to evaluate the role of four-dimensional computer tomography (4D-CT) scan as an imaging modality in patients with failed and discordant localisation reported in the first-line imaging modalities (ultrasonography and 99mTc-MIBI-SPECT/CT). Methods This is a prospective cohort study performed at a university teaching centre from March 2013 to July 2021. All patients with primary hyperparathyroidism who had failed localisation by ultrasonography and 99mTc-MIBI-SPECT/CT (SpCT), or discordance between them, had 4D-CT performed in this study. Results One hundred and two sporadic cases of pHPT with failed/discordant first-line imaging had 4D-CT imaging prior to parathyroidectomy. In 102 patients, 105 parathyroid adenomas were reported on histopathology. 4D-CT was able to localise 78% of them to the correct side and 64% to the correct quadrant in 102 patients, as compared with US (correct side 21%, correct quadrant 16%) and 99mTc-MIBI-SPECT/CT (correct side 36%, correct quadrant 31%). 4D-CT had a sensitivity, precision, accuracy and F1 score for correct quadrant localisation as 79%, 81%, 66% and 80%; and for correct side localisation as 82%, 98%, 80% and 89%, respectively. 4D-CT was able to identify three ectopic adenomas (two in superior mediastinum and one in the oesophageal wall) which were not detected on US or SpCT. Conclusion 4D-CT was found to be sensitive and accurate in preoperative localising of the diseased parathyroid glands after failed/discordant US and SpCT. This led to more patients being offered MIP as the primary surgery and improved operative outcomes.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".