Malignant Melanoma With Osteoclast-Like Differentiation
Bibliographic record
Abstract
Osteoclast-like giant cells are frequently encountered in nonskeletal malignancies; however, the evidence to date suggests that they represent a tissue response to the lesion rather than neoplastic differentiation. We describe a case of metastatic melanoma demonstrating osteoclast-like differentiation in the lung. The lung nodule was diagnosed as a metastatic melanoma by histological features and confirmed by immunohistochemistry. Resection specimen showed numerous multinucleated giant cells exhibiting osteoclast-like morphology dispersed throughout the lesion. Both the neoplastic melanocytes and giant cells were reactive for HMB-45, Melan-A, and S100. In addition, the multinucleated neoplastic giant cells were also reactive for the monocyte/macrophage lineage markers CD68 and CD163, and alkaline phosphatase, an enzyme present in normal osteoclasts. The neoplastic melanocytes and the multinucleated neoplastic giant cells were also reactive for microphthalmia-associated transcription factor, a protein required for the development of both melanocytes and osteoclasts. Collectively, a co-expression of monocyte/macrophage markers along with melanocytic markers and alkaline phosphatase in the multinucleated neoplastic giant cells in metastatic melanoma suggest that malignant melanocytes are capable of differentiating into osteoclast-like cells and consequently aid invasion into various structures and eliciting the aggressive behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".