Clinicopathological Features of HER2 Expressing Lobular Carcinoma of Breast
Bibliographic record
Abstract
Background: Invasive lobular carcinoma (ILC) accounts for approximately 10% of invasive breast carcinomas and is the most common special subtype. Most ILCs express estrogen receptors (ERs) and progesterone receptors (PRs) but typically lack ERBB2 (human epidermal growth factor receptor 2 (HER2)) overexpression. HER2-positive ILC is rare, understudied, and often linked to aggressive clinical and histopathologic features. This study aimed to examine the clinicopathologic characteristics of HER2-positive ILC to ensure proper classification and management. Methods: A retrospective review was conducted on 48 cases, including 28 HER2-positive ILC and 20 pleomorphic invasive lobular carcinoma (p-ILC) cases without HER2 overexpression. Histological features assessed included nuclear pleomorphism, signet ring cell morphology, and apocrine features. Hormone receptor status and clinical outcomes were also analyzed. Results: All HER2-positive ILC cases exhibited at least one pleomorphic histological feature. Hormone receptor positivity was lower in HER2-positive ILC compared to p-ILC without HER2 overexpression. However, overall survival did not significantly differ between the two groups. Conclusion: HER2 overexpression in ILC is frequently associated with pleomorphic features. p-ILC, regardless of HER2 status, portends a worse prognosis. Identifying these features in HER2-positive ILC and classifying them as pleomorphic lobular carcinoma, a more aggressive ILC variant, is crucial for closer patient follow-up.
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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 | 0.000 |
| 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".