Invasive lobular carcinoma of the male breast – a systematic review with an illustrative case study
Bibliographic record
Abstract
Male breast cancer is rare, comprising only 1% of all mammary cancers; invasive ductal carcinoma is by far the commonest subtype in both men and women. Though lobular breast cancer is the second most common subtype seen in women, such cancers are extremely uncommon in men, and this is likely related to the lack of lobular development in the male breast. Thus, due to the rarity of this subtype among breast cancers, compounded by the overall rarity of breast cancer in men, current understanding of the pathogenesis of this disease and its management is largely derived from case series and extrapolation of information from the larger cohort of female patients. This paper provides a systematic review on invasive lobular carcinoma of the male breast in the context of an illustrative case study. A comprehensive analysis of the National Cancer Institute's Surveillance, Epidemiology, and End Results Data 1973-2013 leading to an exploration of the pathogenesis, epidemiology, clinical presentation, diagnosis, tumor characteristics, and management of lobular breast carcinoma in men is also discussed. Lobular subtype of breast cancer remains an enigmatic elusive disease that needs additional research to unravel its overall pathogenesis and molecular profile to provide insight for improved therapeutic management options.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".