Current patterns of care and outcomes for dermatofibrosarcoma protuberans: An international multi‐institutional collaborative
Bibliographic record
Abstract
BACKGROUND: Dermatofibrosarcoma protuberans (DFSP) is a cutaneous sarcoma with an infiltrative growth pattern that makes it challenging to clear margins. High quality data regarding DFSP natural history, management, and outcomes are limited. METHODS: Data were retrospectively collected for adult DFSP patients who underwent resection at 10 institutions in eight countries. Demographics, tumor characteristics, treatment strategies, and outcomes were analyzed. RESULTS: Analysis included 347 patients consisting of young (median, 42 years), White (76.2%), males (54.2%) with truncal lesions (57.3%). The majority (76.8%) were symptomatic at presentation. Preoperative imaging was used in 55.9% of cases. Diagnosis was established with excisional biopsy in 50.9% versus incisional biopsy in 25.0% of cases. Despite planned margins of >1.0 cm in 67.4% of cases, only 69.0% of patients achieved R0 resection. Twenty-two percent of patients underwent at least one re-excision. R0 resection was achieved at a second procedure in 80.2% and a third procedure in 86.2%. Ultimately, R0 resection was feasible in 89.5% of all patients. Fibrosarcomatous transformation (FST) was observed in 12.6%. In total, 6.6% (N = 23) recurred (17 local, six distant). Of the six distant recurrences, 50.0% had FST. With a median follow-up of 47.0 months, disease-specific survival rate was 98.8%. In multivariable analysis, R0 margins at index resection were associated with wider circumferential margins and non-FST histology. CONCLUSIONS: In this international, multicenter collaborative, DFSP practice patterns were heterogeneous but achieved favorable recurrence rates and survival. Multiple excisions to clear margins remain commonplace and can inform future efforts to optimize margin selection.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".