Radiation Therapy for Cutaneous Blastic Plasmacytoid Dendritic Cell Neoplasm: A Case Report and Review of the Literature
Bibliographic record
Abstract
Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a clinically aggressive hematologic malignancy derived from plasmacytoid dendritic cells. It commonly presents as cutaneous lesions. To date, no standard treatment protocol for BPDCN exists. Traditionally treated similarly to acute leukemia or lymphoma, its prognosis remains poor. Radiation therapy is employed for isolated skin lesions, for patients that are ineligible for chemotherapy due to age or comorbidities and for post-chemotherapy recurrence. However, very limited reports are available on radiotherapy for BPDCN. We present a case involving a 94-year-old BPDCN patient treated with radiation therapy, highlighting an atypical situation of two separate radiotherapy sessions with different dosages for isolated skin lesions. Initially, 45 Gy was administered in 15 fractions (45 Gy/15 Fr), followed by a second session of 30 Gy in 10 fractions (30 Gy/10 Fr) after disease recurrence. This case is unique in detailing radiation therapy for the exceedingly rare BPDCN, particularly dose fractionation. The findings indicate that 45 Gy/15 Fr can provide adequate local control, while even a lower dose of 30 Gy/10 Fr may be effective. This case report contributes to the limited literature by proposing potential therapeutic approaches and dosage guidelines to refine future BPDCN treatment protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".