The safety and clinical impact of ultra-low-dose FDG-PET imaging in pregnancy-associated breast cancer: the experience of a major tertiary oncology referral centre in the UK and suggested imaging protocol
Bibliographic record
Abstract
BACKGROUND: Pregnancy-associated breast cancer (PABC) is a complex condition affecting 1 in 3000 pregnancies worldwide. While clinical management has improved, the optimal staging approach for PABC remains uncertain. 18 F-fluorodeoxyglucose PET (FDG-PET) imaging is a standard diagnostic tool for many cancers. However, its use in PABC staging is controversial due to potential radiation risks to the foetus. METHODS: This retrospective case series analysed clinical data from six patients with high-risk PABC who underwent FDG-PET imaging for staging between 2022 and 2023. FDG-PET was based on locally implemented ultra-low-dose imaging protocols. The radiation doses to the foetus were dosimetrically estimated based on dose-per-unit activity values and correlated with postpartum neonatal outcomes. RESULTS: The median foetal radiation dose was 0.975 mGy (range 0.6-1.5 mGy) and was below the threshold for deterministic toxicities. PET imaging upstaged nodal involvement in 33% of patients and influenced treatment decisions. FDG-PET imaging provided valuable staging information in all cases. No adverse foetal effects were observed. CONCLUSION: Ultra-low-dose FDG-PET imaging is a valuable tool providing accurate staging information to guide treatment decisions. The low radiation dose associated with this technique makes it a clinically acceptable modality for cancer staging in pregnant women. A larger case series is needed to precisely quantify foetal radiation doses and assess long-term safety.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".