Innovative and Customized 3-Dimensional-Printed Mold Brachytherapy Treatment of Genital Extramammary Paget’s Disease
Bibliographic record
Abstract
Purpose: Extramammary Paget's disease (EMPD) is a rare intraepithelial neoplasm that affects apocrine gland-rich areas of the perineal region. Surgical treatment is the standard of care but is associated with significant morbidity. Nonsurgical options, such as surface mold brachytherapy (SMBT) with custom 3-dimensional (3D)-printed applicators, offer an innovative, organ-preserving alternative. Our aim was to evaluate the efficacy, safety, and cosmetic outcomes of custom 3D-printed SMBT in patients diagnosed with EMPD. Methods and Materials: A retrospective case series involving 9 patients treated for EMPD between November 2019 and April 2023 was included. Patient demographics, clinical characteristics, treatment parameters, and outcomes were analyzed. Primary outcomes included clinical response rates, acute and late toxicities (graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 5), and patient-reported cosmetic outcomes. Results: All 9 patients (6 men and 3 women) achieved a complete clinical response, with 1 patient experiencing marginal recurrence at 13.6 months posttreatment. Acute grade 3 dermatitis occurred in all patients and resolved, on average, within 54 days (range, 35-72). Late toxicities included hypopigmentation (4 patients) and telangiectasia (3 patients). Cosmetic outcomes were rated as excellent or very good by 89% of patients. Conclusions: This study represents the first report on the use of custom 3D-printed SMBT for EMPD. This technique demonstrates excellent local control, manageable toxicity, and favorable cosmetic outcomes, offering a promising alternative to surgery in this anatomically complex area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".