Imaging Cancer in Pregnancy
Bibliographic record
Abstract
Pregnancy-associated cancer (PAC) is defined as cancer that is detected during pregnancy and up to 1 year postpartum. Although rare (~1:1000 pregnancies), PAC is increasing owing to postponed childbearing and advanced maternal age at conception. Cancer-related symptoms masked by physiologic gestational changes may delay diagnosis. Imaging, clinical management, and treatment require a carefully choreographed multidisciplinary team approach. The risk-benefit of every imaging modality, the strategies to balance the safety of mother and fetus, and the support of the patient and family at every step are crucial. US and MRI are preferred imaging modalities that lack ionizing radiation. Radiation dose concerns should be addressed, noting that most imaging examinations (including mammography, radiography, CT, and technetium 99m–labeled sulfur colloid sentinel lymph node staging) are performed at radiation levels below thresholds at which deterministic side effects are seen. Dose estimates should be provided after each examination. The use of iodinated intravenous contrast material is safe during pregnancy, but gadolinium-based contrast material should be avoided. Accurate diagnosis and staging combined with gestational age affect decisions about surgery and chemotherapy. Whole-body MRI with diffusion-weighted sequences is ideal to screen for primary and metastatic sites, determine disease stage, identify biopsy targets, and guide further cancer site–specific imaging. The authors provide an update of the imaging triage, safety considerations, cancer-specific imaging, and treatment options for cancer in pregnancy. An invited commentary by Silverstein and Van Loon is available online. Online supplemental material is available for this article. ©RSNA, 2022
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".