A Challenging Case of Granulomatous Mycosis Fungoides Mimicking Cutaneous Sarcoidosis
Bibliographic record
Abstract
Granulomatous mycosis fungoides was first described by Ackerman and Flaxman in 1970 and is characterized histologically by diffuse invasion of dermis by giant cell lymphocytes. Granulomatous mycosis fungoides is a subtype of cutaneous T-cell lymphoma which is a variant of non-Hodgkin’s lymphoma. It usually affects middle-aged adults, presenting as progressive skin lesions. Initial diagnosis can be extremely challenging as it mimics various dermatopathies both clinically and histologically. Despite advances in diagnostic techniques, diagnosis is often delayed, as granulomatous mycosis fungoides is a great imitator. We describe a unique case of a 51-year-old female presenting with cutaneous granulomatous infiltration, which was initially diagnosed as sarcoid granulomatous dermatitis; and after 6 years of evolution, was eventually determined to be granulomatous mycosis fungoides. We discuss the rare presentation of granulomatous mycosis fungoides misdiagnosed as sarcoid granulomatous dermatitis and the challenges encountered in confirming the lymphoma diagnosis. Clinicians and pathologists should consider this entity in differential diagnosis, when encountered with persistent granulomatous skin lesions, as extensive granulomatous lesions tend to obscure the underlying lymphoma. Immunophenotyping and molecular gene rearrangement studies can improve diagnostic accuracy. J Med Cases. 2019;10(4):106-109 doi: https://doi.org/10.14740/jmc3276
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".