Lymphomatoid Papulosis With T-cell Receptor–Gamma Delta Expression
Bibliographic record
Abstract
Lymphomatoid papulosis (LyP) has several histopathologic presentations. LyP featuring gamma-delta (γδ) T-cell receptor expression may masquerade as and may be misdiagnosed as aggressive cutaneous T-cell lymphoma, particularly primary cutaneous γδ T-cell lymphoma (PCGDTL) or γδ mycosis fungoides. We performed a clinicopathologic analysis of the largest series of LyP featuring γδ T-cell expression. We identified 26 patients with a diagnosis of LyP with γδ T cells from our institutions, as well as through a comprehensive review of the literature, and characterized these cases. Most cases were treated with topical steroids or not treated at all. The majority of cases showed a CD4 - CD8 + phenotype and featured at least one cytotoxic marker. Histopathologic features included an intraepidermal or dermal infiltrate with large cells and frequent angiotropism. One case was initially misdiagnosed as PCGDTL, requiring further therapy. Our case series, the largest international cohort of γδ T cell predominant LyP cases, confirms marked clinicopathologic heterogeneity that may contribute to misdiagnosis, reasserting the need to identify classic clinical features, CD30 + T-cell components, and markers of cytotoxicity when dealing with this differential diagnosis. A limitation of this study includes somewhat limited follow-up, histologic, and immunophenotypic information for some cases.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".