Feasibility of <sup>18</sup>F-FDG Dose Reductions in Breast Cancer PET/MRI
Bibliographic record
Abstract
The goal of this study was to determine the level of clinically acceptable 18F-FDG dose reduction in time-of-flight PET/MRI in patients with breast cancer. Methods: Twenty-six consecutive women with histologically proven breast cancer were analyzed (median age, 51 y; range, 34–83 y). Simulated dose-reduced PET images were generated by unlisting the list-mode data on PET/MRI. The acquired 20-min PET frame was reconstructed in 5 ways: a reconstruction of the first 2 min with 3 iterations and 28 subsets for reference, and reconstructions simulating 100%, 20%, 10%, and 5% of the original dose. General image quality and artifacts, image sharpness, image noise, and lesion detectability were analyzed using a 4-point scale. Qualitative parameters were compared using the nonparametric Friedman test for multiple samples and the Wilcoxon signed-rank test for paired samples. Different groups of independent samples were compared using the Mann–Whitney U test. Results: Overall, 355 lesions (71 lesions with 5 different reconstructions each) were evaluated. The 20-min reconstruction with 100% injected dose showed the best results in all categories. For general image quality and artifacts, image sharpness, and noise, the reconstructions with a simulated dose of 20% and 10% were significantly better than the 2-min reconstructions (P ≤ 0.001). Furthermore, 20%, 10%, and 5% reconstructions did not yield results different from those of the 2-min reconstruction for detectability of the primary lesion. For 10% of the injected dose, a calculated mean dose of 22.6 ± 5.5 MBq (range, 17.9–36.9 MBq) would have been applied, resulting in an estimated whole-body radiation burden of 0.5 ± 0.1 mSv (range, 0.4–0.7 mSv). Conclusion: Ten percent of the standard dose of 18F-FDG (reduction of ≤90%) results in clinically acceptable PET image quality in time-of-flight PET/MRI. The calculated radiation exposure would be comparable to the effective dose of a single digital mammogram. A reduction of radiation burden to this level might justify partial-body examinations with PET/MRI for dedicated indications.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".