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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".