Tumor Characteristics Associated With Mammographic Detection of Breast Cancer in the Ontario Breast Screening Program
Bibliographic record
Abstract
BACKGROUND: Few studies have compared the prognostic value of tumor characteristics by type of breast cancer diagnosed in the interval between mammographic screenings with screen-detected breast cancers. METHODS: We conducted a case-case study within the cohort of women (n = 431 480) in the Ontario Breast Screening Program who were aged 50 years and older and were screened between January 1, 1994, and December 31, 2002. Interval cancers, defined as breast cancers diagnosed within 24 months after a negative screening mammogram, were designated as true interval cancers (n = 288) or missed interval cancers (n = 87) if they were not identified at the time of screening but were identified in retrospect. Screen-detected breast cancers (n = 450) were selected to match interval cancers. Tumors were evaluated for stage, grade, mitotic index, histology, and expression of hormone receptors and odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by conditional logistic regression. RESULTS: Both true and missed interval cancers were of higher stage and grade than matched screen-detected breast cancers. However, true interval cancers had a higher mitotic index (OR = 3.13, 95% CI = 1.81 to 5.42), a higher percentage of nonductal histology (OR = 1.94, 95% CI = 1.05 to 3.59), and were more likely to be both estrogen receptor-negative (OR = 2.09, 95% CI = 1.32 to 3.30) and progesterone receptor-negative (OR = 2.49, 95% CI = 1.68 to 3.70) compared with matched screen-detected tumors. CONCLUSIONS: In this study, interval cancers were of higher stage and grade compared with screen-detected cancers. True interval cancers were more likely to have additional adverse prognostic features of estrogen and progesterone receptor negativity and nonductal morphology. The findings suggest a need for more sensitive screening modalities to detect true interval breast cancers and different approaches for early detection of fast-growing tumors.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".