Bibliographic record
Abstract
Abstract Potential clinical applications of mammographic density include: 1) the prediction of outcome of breast disease, 2) mammographic screening, 3) research and practice of cancer prevention, and 4) improved prediction of risk in individuals. 1) Breast cancer characteristics and clinical outcomes. Extensive percent mammographic density (PMD) is associated with increased risks for the development of most of the histological non-obligate precursors of breast cancer, including ductal carcinoma in situ (DCIS), atypical hyperplasia, hyperplasia without atypia, and columnar cell lesions (CLLs). PMD is associated with an increased risk of both ER+ and ER− tumors, and with larger tumors of higher histological grade. Risk of contralateral second breast cancer is also increased by extensive PMD. However, among women with breast cancer, risk of death from the disease does not appear to be increased by PMD. 2) Mammographic screening. Women with extensive PMD are both at higher risk of breast cancer and at greater risk that cancer will not be detected by mammography. This heterogeneity is not currently recognized in the design of screening programs. Women with extensive PMD, might benefit from screening more often than once every 2 to 3 years and with modalities such as MR or UST in addition to mammography. Conversely, for women with radiolucent breast tissue and a negative screening mammogram, in whom risk is lower and detection easier, re-screening less frequently than every 2 to 3 years with mammography might be safe. Research into these issues is required. 3) Breast cancer prevention trials. Unlike most other risk factors for breast cancer, PMD can be changed), suggesting that it might be used as a surrogate marker in clinical trials of breast cancer prevention. Clinical trials of breast cancer prevention require large numbers of subjects and long periods of observation and thus are expensive. Smaller, shorter, and less expensive trials of prevention strategies would be possible if there were a surrogate marker that allow the identification of interventions that would reduce breast cancer incidence. To be used as a surrogate for breast cancer, a biomarker such as PMD should meet the following criteria: (a) the marker should be associated with risk of breast cancer, (b) the marker should be changed by the intervention, and (c) the change in the marker should mediate the effect of the intervention on breast cancer risk. The extent to which these criteria can currently be met will be discussed. 4) Improved risk prediction. PMD is inversely associated with age, while breast cancer incidence increases with age. We used data from 3250 healthy women aged 15–80 to estimate cumulative breast density (CBD) in the population, which increases with age, and compared this with age-specific incidence of breast cancer in Canada. Log CBD and log breast cancer incidence had a linear association (r = 0.98; p = 6.0 E−9) that was unchanged by adjustment for log age (r = 0.97; p = 8.0 E−7). Log CBD alone was a better predictor of age-specific log breast cancer incidence (r2 = 0.97) than age alone (r2 = 0.88). The observed strong association between CBD and breast cancer incidence suggest that the factors responsible for CBD may be causally related to the age-specific incidence of breast cancer. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr MS2-2.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".