Clinical Image Quality in Daily Practice of Breast Cancer Mammography Screening
Bibliographic record
Abstract
OBJECTIVE: To assess the quality of screening mammograms performed in daily practice in the Quebec Breast Cancer Screening Program. SUBJECTS AND METHODS: Clinical image quality of a random subsample of 197 screening mammograms performed in 2004-2005 was independently evaluated by 2 radiologists based on the criteria by Canadian Association of Radiologists (CAR). When disagreement occurred for overall judgement or positioning score, the mammograms were reviewed by a third radiologist. Cohen's kappas for interrater agreement were computed. Multivariable robust Poisson regression models were used to study associations of overall quality and positioning with body mass index (BMI) and breast density. RESULTS: The CAR criteria were not satisfied for 49.7% of the mammograms. Positioning was the quality attribute most often deficient, with 37.2% of mammograms failing positioning. Interrater agreement ranged from slight (kappa = 0.02 for compression and sharpness) to fair (kappa = 0.30 for exposure). For overall quality, women with a BMI ≥ 30 kg/m(2) had a failure proportion of 67.5% compared with 34.9% for women with a BMI<25 kg/m(2) (risk ratio 2.1 [95% confidence interval, 1.5-3.0]). For positioning, women with a BMI ≥ 30 kg/m(2) had a failure proportion of 53.8% compared with 27.9% for women with a BMI < 25 kg/m(2) (risk ratio 1.9 [95% confidence interval, 1.2-3.1]). Effects of breast density on image quality differed among radiologists. CONCLUSION: Despite measures to ensure high-quality imaging, including CAR accreditation, approximately half of this random sample of screening mammograms failed the CAR quality standards. It would be important to define quality targets for screening mammograms carried out in daily practice to interpret such observations.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".