Abstract SS1-1: Breast cancer early detection strategies in absence of screening mammography
Bibliographic record
Abstract
Abstract In spite of the current controversy surrounding over-diagnosis, mammography remains the gold standard for breast cancer screening in the developed world. However, mammography is not suitable technology for low-income countries because of its high cost and the sophisticated infrastructure that is needed to realize its effectiveness. Breast self examination (BSE) may appear to be an appropriate screening procedure; however, randomized trials in the erstwhile Soviet Union and China have produced negative results largely because of non-compliance. Clinical breast examination (CBE) is also an attractive option for low-income countries. Robust evidence of effectiveness of CBE from randomized trials of head-to-head comparison of CBE with no screening is lacking. Indirect evidence from the Canadian NBSS suggests that, in women aged 50 – 59, mammography adds little to mortality reduction over and above that achieved by CBE alone. A randomized trial in Mumbai is underway comparing CBE + BSE teaching with no screening and the results are expected in 2016. Thus, at the present time, direct evidence is lacking to recommend CBE as a screening procedure in low-income countries. The incidence of breast cancer, although rising, remains substantially lower in low-income countries than that in the developed world. For example, in India, the crude incidence rate is 18.45 per 100,000 compared to 157.1 in the US. Low incidence rates raise serious questions about cost-effectiveness of screening. Lack of adequate and easily accessible breast cancer treatment facilities and that of reliable cancer registries are other barriers to screening in these countries. Lack of awareness about hazards of breast cancer is another major challenge since this leads to low motivation and poor compliance to screening. A high degree of compliance is essential at every level of screening for it to be successful. These include compliance to the screening test, attendance at the referral center for further investigations, compliance to the prescribed treatment if cancer is confirmed, completing the entire course of treatment which can be prolonged and attendance at regular follow-up. Although the Mumbai study recorded >70% compliance overall, this was achieved at a high man-power cost. Nearly 100 full-time personnel had to be engaged to make door-to-door visits on multiple occasions to persuade women to comply with the various steps of screening in addition to examining 75,000 women every two years and maintaining yearly surveillance on the control group. The study highlighted that for screening to be successful in low-awareness countries, a vertical programme is essential. A rough estimate based on the Mumbai experience suggests that a vertical programme to screen all women aged 35 – 64 in India, and to achieve ∼70% compliance, would require over US$ 100 million per year in man-power cost alone. Whether this money is better spent on strengthening breast cancer treatment facilities, for developing human resource in cancer care or for remedying the many deficiencies in the health-care delivery system in general is the moot question. Citation Format: Mitra I. Breast cancer early detection strategies in absence of screening mammography. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr SS1-1.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".