Male Breast Cancer: Imaging Considerations for Diagnosis and Surveillance
Bibliographic record
Abstract
Male breast cancer accounts for less than 1% of all breast cancer cases. The important risk factors for the development of male breast cancer are family history, genetic mutations, obesity, liver disease, alcoholism, exogenous estrogen administration, and radiation exposure to the chest area. Despite its rarity, numerous studies have investigated the data on imaging considerations (mammogram, ultrasound, and magnetic resonance imaging (MRI)), but have addressed only certain aspects of male breast cancer. A comprehensive approach on the imaging characteristics, timing of imaging, prognostication based on imaging characteristics, and follow-up strategies in male breast cancer are still lacking. The purpose of this review article was to provide a comprehensive overview of the imaging findings, optimal timing to obtain imaging, and the appropriate follow-up strategies in male breast cancer survivors. This article also describes how imaging modalities can aid in determining prognosis. By addressing this knowledge gap, the article provides valuable insights for clinicians managing this uncommon yet clinically significant disease.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".