Breast Imaging: what women & healthcare professionals need to know
Bibliographic record
Abstract
Women in Radiology should be aware of the importance of early detection of breast cancer, the most common cancer in women. This knowledge is essential to advocate for high quality breast imaging for women, including themselves and their patients. The imaging modalities used in breast imaging have dramatically changed the way in which breast cancer may be diagnosed, and their use affects the stage at which it is diagnosed. Breast cancer may be screen-detected, either with mammography, digital breast tomosynthesis, breast ultrasound, breast MRI or contrast-enhanced mammography, and is typically diagnosed at stage 1. Incidental detection with Chest CT, abdominal CT or MRI or by PET CT may also lead to a diagnosis of breast cancer. When detected because of symptoms in women who have not undergone routine screening or as an interval cancer in women after a normal screen typically because of the masking effect of dense breast tissue, breast cancer is typically diagnosed at a more advanced stage, stage IIA or greater. A review of the imaging modalities currently used to diagnose breast cancer is provided and includes the advantages and limitations of each modality and the ways to optimize the imaging quality for detection of breast cancer. Up-to-date recommendations aimed to minimize the harms of delayed diagnosis of breast cancer are summarized to improve the health of women in Radiology and their patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".