Cancers Detected on Supplemental Breast Ultrasound in Women With Dense Breasts: Update From a Canadian Centre
Bibliographic record
Abstract
Objective: Breast ultrasound is one of several tools proposed for supplemental screening of women with dense breasts but is not widely available in Canada. Methods: An IRB-approved, evaluation of ultrasound-guided breast biopsies prompted by screening breast ultrasound performed from August 1, 2021, to December 31, 2022, offered to asymptomatic women with category C and D breast tissue density after normal screening mammography (2D) in the provincial organized screening program, or surveillance diagnostic mammography after breast cancer. Risk factors, stage (AJCC 8th ed), incremental cancer detection rate (ICDR), biopsy rates, and positive predictive values for biopsy (PPV3) were evaluated. Results: 5257 women were screened, yielding 247 women (ages 34-82, median age of 56) who underwent biopsies (281 masses), 32 of whom were diagnosed with breast cancer, 27 invasive and 5 DCIS for PPV3 13.0% (32/247), and ICDR 6.1/1000. Ductal cancers found were stage 0 in 5/32 (15.6%), stage 1A in 18/32 (56.3%), and stage 1B in 2/32 (6.3%), 1 ductal/lobular cancer was stage 3B (3.1%), 5 lobular cancers (16.6%) were stage 1A (1), 1B (2), and 2B (2) and 1 adenoid cystic carcinoma was stage 2A (3.1%); 3 cancers were found on incident and 29 on prevalent screens, 27 (84.4%) in category c and 20 (62.5%) in women with no personal or first-degree family history of breast cancer. Conclusion: A high ICDR for screening breast ultrasound of 6.1/1000 was found. Clinical Relevance Statement: In women with dense breasts screened with 2D mammography where access to supplemental screening with MRI and contrast mammography is limited, supplemental screening ultrasound can play a significant role in cancer detection with a high ICDR in women in both category c and d densities that is higher than in jurisdictions offering annual screening mammography, or where MRI is used for surveillance after cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".