HER2 Positive and HER2 Negative Classical Type Invasive Lobular Carcinomas: Comparison of Clinicopathologic Features
Bibliographic record
Abstract
Human epidermal growth factor receptor 2 (HER2) positive (+) classical type invasive lobular carcinoma (cILC) of the breast is extremely rare and its clinicopathologic features have not been well characterized. We compared features of HER2(+) and HER2 negative (−) cILCs. A total of 29 cases were identified from the clinical database at our institution from 2011-2019; 9 were HER2(+) cILC tumors and 20 were HER2(−) cILC tumors. The results reveal that HER2(+) cILC group had significantly increased Ki-67 expression and reduced estrogen receptor (ER) expression compared to HER2(−) cILC group (both p < 0.05). In addition, HER2(+) cILCs tended to be diagnosed at a younger age and more common in the left breast, and appeared to have a higher frequency of nodal or distant metastases. These clinicopathologic features suggest HER2(+) cILC tumors may have more aggressive behavior than their HER2(−) counterpart although both groups of tumors showed similar morphologic features. Future directions of the study: (1) To conduct a multi-institutional study with a larger case series of HER2(+) cILC to further characterize its clinicopathologic features; (2) to compare molecular profiles by next generation sequencing (NGS) assay between HER2(+) cILC and HER2(−) cILC cases to better understand tumor biology of this rare subset of HER2(+) breast cancer; and (3) to compare molecular characteristics of HER2(+) cILC and HER2(+) high grade breast cancer in conjunction with status of tumor response to anti-HER2 therapy to provide insight to management of this special type of low grade breast cancer to avoid unnecessary treatment and related toxicity
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".