P5-08-04: 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 882 women treated for breast cancer between 2000–2010 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 826 patient charts were included; 56 (6.4%) patients had metastatic carcinoma and were excluded from analysis. Only 37.0% (326/882) of the patients presented with mammographic MCs. Patients were more likely to have MCs if they were HER-2 positive (51%) as opposed to being HER-2 negative (33.4%) (p=0.001). There was a significant association between MCs and being perimenopausal with a mean age of 50 (65.2%) (p=0.012). Patients with invasive ductal carcinomas (39.7%) 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=0.051), with grade III tumors (41.85%) presenting with the most MCs, followed by grade II (37.95%) and grade I (29.8%). 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 that suggests the appearance of MCs on mammograms is strongly associated with HER-2 overexpression, invasive ductal carcinoma and perimenopausal status. Since HER-2 is implicated in mediating aggressive tumor growth and metastasis, future studies should investigate the molecular pathways underlying HER-2 overexpression and MC development. This would help better understand the role of MCs in breast cancer tumorigenesis. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-08-04.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".