Abstract PD2-16: Risk of breast cancer after a false-positive screening mammogram in relation to mammographic abnormality: A population-based study in British Columbia
Bibliographic record
Abstract
Abstract Background Breast cancer screening by mammography has been shown to reduce breast cancer mortality, however false positive recall rates have consistently been identified as a harm of organized screening. The extent to which these recalled women are at increased risk of future breast cancer remains unclear. Methods The British Columbia Cancer Agency Screening Mammography Program (SMP), the first organized breast screening program in Canada offers screening mammography for women aged 40-74 since 1988. All women who had two or more screening mammograms between1988-2013 within the SMP were included in the study. They were followed until a breast cancer diagnosis, last screen date +5 years, or end of follow-up on Dec 31, 2013, whichever came first. The relative risk (RR) of breast cancer for women with a false-positive test compared with women with negative tests was estimated with Poisson regression, adjusted for age, and five calendar periods. Results A total of 772,289 women with 4.82 million screening mammograms and a median follow up of 11.8 years were included. There were 238,860 women with false positive findings and 26,950 cancers of which 16,084 screen detected and 10,866 non screen detected. Women without any false positive mammogram had a cancer incidence rate of 245/100 000 person-years at risk, whereas women with a false-positive test had an absolute rate of 447/100 000 person-years at risk. The adjusted RR [Value (95% CI)] of breast cancer after the first false-positive test was 1.73(1.68-1.77) for all, 1.65(1.61-1.70) for invasive, and 2.13(2.01-2.27) for in situ cancers respectively. The RR remained increased beyond 8 years after the false-positive test. Of the 5157 screen detected cancers after the first false positive test, 3358 (65%) were on the ipsilateral breast while 1799 (35%) were on the contralateral breast. Women with only one, two, three or four+ false positive test(s) had RR of 1.88(1.83-1.93), 1.42(1.35-1.49), 1.17(1.05-1.30), and 1.08(0.87-1.34) respectively for all cancers. Women with breast density >50% at the time of false positive test had a twofold risk of breast cancer with a RR of 2.07(1.99-2.14), while those with breast density <50% had a RR of 1.58(1.54-1.63). When stratified for mammographic features found on the first false positive mammogram, architectural distortion plus mass had the highest RR 4.68(3.16-6.93) for invasive cancers while calcifications alone and calcifications plus asymmetry had highest RR 5.57(4.88-6.36) and 4.07(2.49-6.66) for in situ cancers. Conclusion False positive mammogram correlates with an increased risk of developing breast cancer. 65% of the screen detected breast cancers post false positive mammogram occur in the ipsilateral breast. Mammographic abnormality features of the false positive mammogram are found to be predictors for the type of future breast cancer. Mammographic features at the time of recall predicts risk of subsequent cancer and may warrant increased surveillance. Citation Format: Rajapakshe R, Miao H, Sam J, Farnquist B, Hartman M. Risk of breast cancer after a false-positive screening mammogram in relation to mammographic abnormality: A population-based study in British Columbia [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD2-16.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".