Pediatric Cutaneous Hematologic Disorders: Cutaneous Lymphoma and Leukemia Cutis—Experience of a Tertiary-Care Pediatric Institution and Review of the Literature
Bibliographic record
Abstract
Background Cutaneous hematologic malignancies are rare in children, and the literature about them is still sparse. Objective The purpose of our study was to report our experience with pediatric cases of cutaneous hematologic disorders and describe their clinical and histological features. Methods Data were retrospectively collected from the histopathologic database of the CHU Sainte-Justine, University of Montreal, Montreal, Canada. All patients up to 18 years of age with a diagnosis of a primary cutaneous lymphoma (including lymphomatoid papulosis), secondary cutaneous lymphoma or cutaneous manifestations of leukemia, followed from 1980 to 2019 at our center were reviewed. Results Thirty-six patients were included. Age at presentation ranged from birth to 18 years of age (mean 7.83 ± 5.16; median 7.0). Ten different hematologic disorders were identified according to the WHO-EORTC classifications: lymphomatoid papulosis (10 cases), mycosis fungoides (6 cases), anaplastic large cell lymphoma (4 cases), pre-B acute lymphoid leukemia (5 cases), primary cutaneous marginal zone B-cell lymphoma (4 cases), primary cutaneous CD4 + medium T-cell lymphoproliferative disorder (1 case), extranodal NK/T-cell lymphoma (1 case), hydroa vacciniforme-like lymphoproliferative disorder (1 case), B-cell lymphoblastic lymphoma (1 case) and acute myeloid leukemia (3 cases). Conclusion The most common subtype of cutaneous hematologic disease in our single institution study was lymphomatoid papulosis (type A and type C), followed by mycosis fungoides. Recognition of this large clinical and histological spectrum by dermatologists is important because diagnosis is often established by biopsy of skin lesions, even in secondary cutaneous cases. Moreover, the clinicopathological correlation is of utmost importance for the final diagnosis of those pathologies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".