Superiority of Digital Subtraction for Analysis of Simultaneously-Acquired Dual-Radiopharmaceutical Parathyroid Scintigraphy
Bibliographic record
Abstract
With dual-radiopharmaceutical (DR) parathyroid scintigraphy, imaging with 99mTcO4-or 123I-NaI is combined with 99mTc-sestamibi scintigraphy for localization of parathyroid adenomas. The set images are then either visually compared or digitally subtracted to aid in interpretation. While both EANM and SNMMI guidelines recommend use of digital subtraction over visual interpretation alone, to date, the few formal comparisons performed have not demonstrated superiority. The purpose of this investigation is to rigorously assess the added value of digital subtraction over visual interpretation alone using simultaneously-acquired 123I-NaI and 99mTc-sestamibi images. Materials: 90 consecutive patients with DR parathyroid scintigraphy for primary hyperparathyroidism who underwent successful parathyroidectomy were included. DR planar acquisition was performed 15 minutes post injection using 10% dual energy windows. Digital subtraction was subsequently performed using commercially available software. Images were independently reviewed by 3 nuclear medicine trainees and 2 experienced nuclear medicine physicians with and without digital subtraction. Results were compared with surgical and histopathologic findings, which served as ground truth. Results: 90 patients had a total of 91 confirmed parathyroid lesions. All 5 readers had significantly greater sensitivity with digital subtraction compared with visual interpretation alone while specificity was not significantly diminished. Area under the ROC curve was significantly greater with digital subtraction in 3 of 5 readers. Agreement was greater among trainees and experienced physicians when using digital subtraction. Conclusion: Using an optimized DR planar co-imaging technique, digital subtraction significantly improved inter-observer agreement and confidence of interpretation and increased sensitivity, without diminishing specificity.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".