Diagnostic Challenge of Nodal Nevi Mimicking Metastatic Melanoma in Axillary Lymph Nodes Following Neoadjuvant Therapy for Breast Cancer: A Case Report
Bibliographic record
Abstract
Nodal nevi are benign melanocytic proliferations within lymph nodes that can closely mimic metastatic melanoma, posing a significant diagnostic challenge, particularly in breast cancer patients undergoing lymph node dissection after neoadjuvant chemotherapy. Accurate differentiation between nodal nevi and true melanoma metastases is essential to avoid misdiagnosis and overtreatment. Immunohistochemical (IHC) markers such as preferentially expressed antigen in melanoma (PRAME), p16, human melanoma black-45 (HMB-45), and Ki-67 are critical tools for diagnostic clarification. We present a diagnostically challenging case of multiple infiltrative nodal nevi in a 59-year-old female with triple-negative invasive ductal carcinoma, no special type, of the breast. The patient had a prior history of dysplastic nevus on the upper trunk and presented with a 1.5 cm palpable mass in the left breast and a 5 cm left axillary mass. Following neoadjuvant chemotherapy, both lesions demonstrated a clinical reduction in size. She subsequently underwent a partial mastectomy and axillary lymph node dissection. Histologic examination revealed no residual invasive carcinoma in the breast. However, four axillary lymph nodes contained atypical melanocytic-appearing cells in the subcapsular sinuses with extension into the nodal parenchyma, raising the differential diagnosis of residual carcinoma versus metastatic melanoma. Initial IHC showed these atypical cells to be melanocytic in origin (SOX10 and melanoma cocktail positive; AE1/AE3 negative). While initial interpretation favored metastatic melanoma, further IHC workup demonstrated low proliferative activity (Ki-67 <1%), diffuse p16 positivity, and negativity for both HMB-45 and PRAME. These findings, along with dermatopathology consultation, supported a diagnosis of multiple nodal nevi rather than melanoma. This case underscores the diagnostic pitfall posed by infiltrative nodal nevi, particularly when they mimic melanoma in the setting of breast cancer. It highlights the importance of comprehensive immunohistochemical panels, including PRAME, p16, HMB-45, and Ki-67, and the value of second opinions and dermatopathology consultation in avoiding diagnostic error.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".