Abstract P2-01-05: Mammographic microcalcifications and breast cancer tumorigenesis: A radiologic-pathologic analysis
Bibliographic record
Abstract
Abstract Background: Microcalcifications (MCs) are tiny deposits of calcium in breast soft tissue. They serve as key diagnostic radiological features for localization of malignancy. Approximately 30% of early invasive breast cancers have fine, granular MCs detectable on mammography; however, their role in breast cancer tumorigenesis is currently unknown. The purpose of this study was to investigate the relationship between mammographic MCs and breast cancer pathology. Methods: A retrospective chart review was performed for 1015 women treated for breast cancer between 2000-2012 at St. Michael's Hospital. Demographic information (age and menopausal status), tumor pathology (size, histology, grade, nodal status and lymphovascular invasion), hormonal status (ER and PR), HER-2 overexpression and presence of MCs were collected for breast cancer patients. Chi-square tests were performed for categorical variables and t-tests were performed for continuous variables. All tests were two-sided and p-values less than 0.05 were considered statistically significant. Results: A total of 1015 patient charts were included; 78 (7.7%) patients had metastatic carcinoma and were excluded from analysis. About 38.3% (287/1015) of the patients presented with mammographic MCs. Patients were more likely to have MCs if they were HER-2 positive (52.9%) as opposed to being HER-2 negative (33.8%) (p<0.001). There was a significant association between MCs and having heterogeneous breast density (p = 0.031) and having multifocal disease (p = 0.044). Patients with invasive ductal carcinomas (40.9%) were more likely to present with MCs than were patients with other tumor histology (p = 0.001). There was a positive correlation between MCs and tumor grade (p = 0.057), with grade III tumors presenting with the most MCs (41.3%), followed by grade II (39.8%) and grade I (30.7%). There was no significant association between mean age, mean tumor size, ER and PR status with the presence of MCs. Conclusion: This is the largest study analyzing data over a 12 year period, suggesting that the appearance of MCs on mammograms is strongly associated with HER-2 overexpression, invasive ductal carcinoma, heterogeneous breast density and multifocal breast cancers. Since HER-2 is implicated in mediating aggressive tumor growth and metastasis, future studies should investigate the molecular pathways connecting HER-2 overexpression and MC development. This would help better understand the role of MCs in breast cancer tumorigenesis. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-01-05.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.013 | 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".