Sensitivity of surveillance mammography for contralateral breast cancers and ipsilateral recurrences in survivors under 50
Bibliographic record
Abstract
11009 Background: Annual mammography is currently the gold standard for surveillance of breast cancer survivors for both ipsilateral recurrence and contralateral new primaries. The risk of either of these events occurring has been found to be inversely proportional to age and is at least 1% per year if breast cancer was diagnosed before age 50. (Chen et al 1999) (Komoike et al 2006). However, the sensitivity of screening mammography is significantly lower in young women. We therefore sought to determine the effectiveness of surveillance mammography according to age of diagnosis using a prospective database. Methods: The Henrietta Banting Database prospectively follows all new cases of operable breast cancer diagnosed from 1987 - 2004 at Women’s College Hospital. From the 1992 women in the database, we selected all patients diagnosed before age 60 who subsequently developed non-metastatic contralateral cancers (n=71 ) or ipsilateral recurrences (n=11 ). Records were obtainable for 59 of these 82 patients. Results: Method of detection and stage of diagnosis are listed in table below for the 59 patients according to age of diagnosis of second cancers. Overall, mammography was less effective at detecting contralateral and recurrent breast cancers in younger women, detecting only 24% of these cancers in women under 50 compared to 74% in patients 50 and older (Chi-Square, p<0.001). In addition, 77% of patients under 50 whose second cancers were detected by methods other than mammography had negative surveillance mammograms in the 15 months prior to diagnosis. Conclusion: Since mammography detected only 24% of second primary breast cancers/ recurrences in survivors < age 50 and almost one third of all detected cancers were locally advanced, more sensitive imaging modalities such as breast MRI should be considered for surveillance of young survivors. [Table: see text] No significant financial relationships to disclose.
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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 | 0.002 | 0.009 |
| 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 | 0.000 | 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".