IRF8 in Conjunction With CD123 and CD20 to Distinguish Lupus Erythematosus Panniculitis From Subcutaneous Panniculitis-like T-Cell Lymphoma
Bibliographic record
Abstract
Distinguishing lupus erythematosus panniculitis (LEP) from subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is a diagnostic challenge with important clinical implications. Immunohistochemical expression of interferon regulatory factor 8 (IRF8) has been shown to highlight cells with plasmacytoid dendritic cell differentiation. Considering that the presence of plasmacytoid dendritic cells highlighted by CD123 immunolabeling is a well-described feature that supports LEP over SPTCL, we hypothesized that IRF8 immunohistochemistry can be used as a diagnostic test to improve accuracy in differentiating LEP from SPTCL. In this study, we assessed the expression of IRF8, CD123, and CD20 in 35 cutaneous biopsies from 31 distinct patients, which included 22 cases of LEP and 13 cases of SPTCL. We found that clusters of IRF8-positive cells within the dermis, and away from subcutaneous fat, could discriminate LEP from SPTCL ( P =0.005). Similarly, CD123-positive clusters in any location were observed in LEP but absent in all cases of SPTCL. In addition, we found that dermal CD20-predominant lymphoid aggregates could help discriminate LEP from SPTCL ( P =0.022). As individual assays, IRF8, CD123, and CD20 were highly specific (100%, 100%, and 92%, respectively) though poorly sensitive (45%, 29%, and 50%, respectively). However, a panel combining IRF8, CD123, and CD20, with at least 1 positive marker was more accurate than any individual marker by receiver operating characteristic curve analysis. Our study provides a rationale for potentially including IRF8 as part of an immunohistochemical panel composed of other currently available markers used to differentiate LEP from SPTCL.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".