Mammographic microcalcifications and breast cancer tumorigenesis: a radiologic-pathologic analysis
Bibliographic record
Abstract
BACKGROUND: Microcalcifications (MCs) are tiny deposits of calcium in breast soft tissue. Approximately 30% of early invasive breast cancers have fine, granular MCs detectable on mammography; however, their significance in breast tumorigenesis is controversial. This study had two objectives: (1) to find associations between mammographic MCs and tumor pathology, and (2) to compare the diagnostic value of mammograms and breast biopsies in identifying malignant MCs. METHODS: A retrospective chart review was performed for 937 women treated for breast cancer during 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 over-expression and presence of MCs were collected. Chi-square tests were performed for categorical variables and t-tests were performed for continuous variables. All p-values less than 0.05 were considered statistically significant. RESULTS: A total of 937 patient charts were included. About 38.3% of the patients presented with mammographic MCs on routine mammographic screening. Patients were more likely to have MCs if they were HER-2 positive (52.9%; p < 0.001). There was a significant association between MCs and peri-menopausal status with a mean age of 50 (64%; p = 0.012). Patients with invasive ductal carcinomas (40.9%; p = 0.001) were more likely to present with MCs than were patients with other tumor histologies. Patients with a heterogeneous breast density (p = 0.031) and multifocal breast disease (p = 0.044) were more likely to have MCs on mammograms. There was a positive correlation between MCs and tumor grade (p = 0.057), with grade III tumors presenting with the most MCs (41.3%). A total of 52.2% of MCs were missed on mammograms which were visible on pathology (p < 0.001). CONCLUSION: This is the largest study suggesting the appearance of MCs on mammograms is strongly associated with HER-2 over-expression, invasive ductal carcinomas, peri-menopausal status, heterogeneous breast density and multifocal disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".