Hematologic Malignancies of the Breast: A Contemporary Series Investigating Incidence, Presentation, Accuracy of Diagnosis on Core Needle Biopsy, and Hormone Receptor Expression
Bibliographic record
Abstract
BACKGROUND: Distinguishing breast hematologic malignancies in core needle biopsies from other entities can be challenging. Misclassification as a breast carcinoma could result in inappropriate treatment. The aim of this study was to characterize the types, incidence, and helpful diagnostic features of hematologic malignancies of the breast. DESIGN: All hematologic malignancies of the breast diagnosed at our institution from 2004 to 2017 were identified. Clinical notes, imaging, and slides were reviewed. Immunohistochemical analysis of estrogen receptor α (ERα), estrogen receptor β (ERβ), and androgen receptor (AR) was performed when tissue was available. RESULTS: In all, 43 hematologic malignancies from biopsies of 37 women and 6 men were identified. Core needle biopsies (35 or 81%) were more common than excisions (8 or 19%). For 14 patients (40%), the core biopsy was the first diagnosis of a hematologic malignancy. Diagnoses included 37 lymphomas (7 primary), 4 leukemias, and 2 myelomas. There was 1 misdiagnosis of carcinoma. Low positivity for hormone receptors was observed in a minority of lymphomas. A definitive diagnosis of hematologic malignancy was made in 31 (89%) of the core needle biopsies. Only 3 patients undergoing core biopsy required excision for diagnosis. CONCLUSIONS: Most of the hematologic malignancies of the breast are currently diagnosed on core needle biopsy and 40% of patients do not have a prior history. To avoid errors, pathologists need to be aware of diagnostic features and morphologic mimics. A hematologic malignancy should be considered if tumor cells are discohesive, carcinoma in situ is absent, and hormone expression is low or absent.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".