A study of CD117 expression in dermatofibrosarcoma protuberans and cellular dermatofibroma
Bibliographic record
Abstract
BACKGROUND: Dermatofibrosarcoma protuberans (DFSP) is a relatively uncommon spindle cell tumor of the skin. It is locally aggressive and can be a therapeutic challenge. There are case reports of partial response of DFSP to the tyrosine kinase inhibitor STI571 (Imatinib), despite the reported negativity of the tumor cells for CD117. At least one publication reported focal CD117 positivity of DFSP cells, and we would like to clarify the issue. Cellular dermatofibroma (CDF) can mimic DFSP, but typical cases are easily differentiated from DFSP by their staining pattern for CD34 and factor 13a. We also report our experience with CD117 staining of typical CDFs. METHODS: Thirty-seven cases of clear-cut DFSP and 13 cases of clear-cut CDF were retrieved from the archives of Sunnybrook Health Sciences Center between 2000 and 2005. RESULTS: All DFSPs were CD34 (+), factor 13a (-) and CD117 (-). All CDFs were factor 13a (+), CD34 (-) and CD117 (-). CONCLUSIONS: Our study on a relatively large number of cases confirms the negativity of DFSP and CDF for CD117. Therefore, if adjuvant therapy is attempted with drugs such as STI571 (Imatinib), the eligibility of patients should not be based on immunohistochemical assessment of CD117 expression.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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 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".