Screening Sensitivity According to Breast Cancer Location
Bibliographic record
Abstract
Purpose To examine the relation between breast cancer location and screening mammogram sensitivity, and assess whether this association is modified by body mass index (BMI) or breast density. Methods This study is based on all interval cancers (n = 481) and a random sample of screen-detected cancers (n = 481) diagnosed in Quebec Breast Cancer Screening Program participants in 2007. Film-screening mammograms, diagnostic mammograms, and ultrasound reports (when available) were requested for these cases. The breast cancer was then localised in mediolateral oblique (MLO) and craniocaudal (CC) projections of the breast by 1 experienced radiologist. The association between cancer location and screening sensitivity was assessed by logistic regression. Adjusted sensitivity and sensitivity ratios were obtained by marginal standardisation. Results A total of 369 screen-detected and 268 interval cancers could be localised in MLO and/or CC projections. The 2-year sensitivity reached 68%. Overall, sensitivity was not statistically associated with location of the cancer. However, sensitivity seems lower in MLO posterior inferior area for women with BMI ≥ 25 kg/m 2 compared to sensitivity in central area for women with lower BMI (adjusted sensitivity ratio: 0.58, 95% confidence interval [CI]: 0.17–0.98). Lower sensitivity was also observed in subareolar areas for women with breast density ≥ 50% compared to the central areas for women with lower breast density (for MLO and CC projections, adjusted sensitivity ratio and 95% CI of, respectively, 0.54 [0.13–0.96] and 0.46 [0.01–0.93]). Conclusions Screening sensitivity seems lower in MLO posterior inferior area in women with higher BMI and in subareolar areas in women with higher breast density. When interpreting screening mammograms, radiologists need to pay special attention to these areas.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".