Dermatologic Mimickers of Paget’s Disease of the Breast: A Systematic Review
Bibliographic record
Abstract
Mammary Paget disease (MPD) is a rare form of breast cancer that accounts for just 1% to 4% of all cases and is often associated with underlying malignancies such as ductal carcinoma in situ and invasive ductal carcinoma. Its clinical presentation frequently mimics benign dermatologic conditions or malignant melanoma, leading to diagnostic confusion and significant treatment delays. This review explores the diagnostic challenges and patterns of misdiagnosis in MPD, as well as the consequences of delayed recognition. A comprehensive search of Embase and MEDLINE identified 29 studies reporting on 32 cases of MPD, all of which were initially misdiagnosed-most commonly as melanoma (44.4%) and atopic dermatitis (25.0%). The average diagnostic delay was 2.3 years. Most lesions were unilateral (93.8%) and involved the nipple-areolar complex (87.5%). Imaging modalities demonstrated limited sensitivity, reinforcing the importance of early biopsy for timely diagnosis. Surgical intervention was the predominant treatment approach, employed in 75% of cases, and no recurrences were reported during a mean follow-up of 1.3 years. These findings underscore the urgent need for heightened clinical suspicion, earlier tissue sampling, and the development of standardized diagnostic pathways to reduce misdiagnosis and improve outcomes in patients with MPD.
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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) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".