Salivary gland-type mammary carcinoma arising in microglandular adenosis: A case report and clinicopathological review of the literature
Bibliographic record
Abstract
INTRODUCTION: Microglandular adenosis (MGA) is a rare benign proliferative lesion lacking a myoepithelial cell layer; 27% of all reported cases have progressed to invasive carcinoma. Salivary gland-type carcinomas of the breast are also uncommon, representing 2% of all breast cancers. This wide spectrum of neoplasms tends to be triple negative and generally has an excellent prognosis. Given the rarity of salivary gland-type carcinomas of the breast arising from MGA, there are few reports of these cases in literature. As such, there is uncertainty regarding their diagnosis and treatment strategies. PRESENTATION OF CASE: We report the rare case of a 66-year-old woman who presented with a triple negative, invasive carcinoma with salivary gland-type features, arising from MGA. The patient underwent mastectomy with sentinel lymph node biopsy, followed by Taxotere and Cyclophosphamide (TC) chemotherapy and 50 Gy in 25 fractions of radiation to her chest wall. We reviewed the available literature on salivary gland-type breast carcinomas arising from MGA. DISCUSSION: Despite the generally unfavourable characteristics associated with carcinoma arising in microglandular adenosis (MGACA), most patients with MGACA have favourable outcomes. CONCLUSIONS: The findings of the present case and reviewed cases are consistent with the literature on MGA, atypical MGA (AMGA), and MGACA. Future study of this rare entity is warranted to establish a consensus surrounding its clinical significance and treatment methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".