Clinical trial · Observational
Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis: A Training and Validation Study
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Cutaneous lymphoproliferative diseases (CLPDs) are a group of skin disorders that range from benign conditions, such as pseudolymphomas, to malignant forms like cutaneous T-cell and B-cell lymphomas. Mycosis fungoides is the most common malignant type, but diagnosis is often difficult because many benign skin conditions can mimic lymphoma. Current diagnostic methods rely on microscopic examination of biopsies, which can be subjective and vary between pathologists. This study aims to develop and validate a deep learning model that uses digitized biopsy images and clinical data to distinguish malignant CLPDs from benign ones. By applying artificial intelligence to dermatopathology, the project seeks to improve diagnostic accuracy, reduce variability, and support clinicians in making timely treatment decisions. The novelty of this work lies in applying advanced AI methods to a rare and challenging group of skin diseases, with the potential to enhance patient care in both specialized centers and resource-limited settings.
Conditions
Conditions (6)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cutaneous Lymphoproliferative Diseases | — | UNRESOLVED | — |
| Mycosis Fungoides of Skin (Diagnosis) | — | UNRESOLVED | — |
| PLEVA-PLC Spectrum | — | UNRESOLVED | — |
| Primary Cutaneous B-Cell Lymphoma (CBCL) | Primary Cutaneous B-Cell Non-Hodgkin Lymphoma | ALIAS | 0.85 |
| Pseudolymphoma | — | UNRESOLVED | — |
| T Cell Dyscrasia | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-assisted histopathology image analysis | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (5)
- label
- MF
- description
- Patients diagnosed histopathologically as mycosis fungoides
- interventionNames
- Diagnostic Test: AI-assisted histopathology image analysis
- label
- PLC/PLEVA
- description
- Patients diagnosed histopathologically as PLC or PLEVA
- interventionNames
- Diagnostic Test: AI-assisted histopathology image analysis
- label
- TCD
- description
- Patients diagnosed histopathologically as T cell dyscrasia
- interventionNames
- Diagnostic Test: AI-assisted histopathology image analysis
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * Archived slides of patients with a confirmed histopathological diagnosis of malignant CLPDs (e.g., mycosis fungoides at all stages, cutaneous B-cell lymphoma, primary cutaneous anaplastic large cell lymphoma, lymphomatoid papulosis), based on WHO-EORTC criteria. * Archived slides of patients with benign CLPDs that mimic MF clinically and histologically (e.g., pseudolymphoma, pityriasis lichenoides chronica, pityriasis lichenoides et varioliformis acuta \[PLEVA\]). * Availability of adequate quality hematoxylin and eosin (H\&E) stained slides. * Availability of relevant clinical data (age, sex, disease duration, distribution of lesions, drug history). Exclusion Criteria: * Slides with significant artifacts (folding, tearing, poor staining) that prevent adequate image analysis. * Cases with insufficient clinical or pathological data for definitive diagnosis. * Cases with secondary cutaneous CLPDs
References
Publications (20)
- BACKGROUNDZama D, Borghesi A, Ranieri A, Manieri E, Pierantoni L, Andreozzi L, Dondi A, Neri I, Lanari M, Calegari R. Perspectives and Challenges of Telemedicine and Artificial Intelligence in Pediatric Dermatology. Children (Basel). 2024 Nov 19;11(11):1401. doi: 10.3390/children11111401. PMID 39594976
- BACKGROUNDValencia Ocampo OJ, Julio L, Zapata V, Correa LA, Vasco C, Correa S, Velasquez-Lopera MM. Mycosis Fungoides in Children and Adolescents: A Series of 23 Cases. Actas Dermosifiliogr (Engl Ed). 2020 Mar;111(2):149-156. doi: 10.1016/j.ad.2019.04.004. Epub 2019 Jul 2. English, Spanish. PMID 31277835
- BACKGROUNDRashad, N. M., Abdelnapi, N. Mm., Seddik, A. F., & Sayedelahl, M. A. (2025). Automating skin cancer screening: A deep learning. Journal of Engineering and Applied Science, 72(1), 6. https://doi.org/10.1186/s44147-024-00573-w
- BACKGROUNDFloridi, L. (2019). Establishing the rules for building trustworthy AI. Nature Machine Intelligence, 1(6), 261-262. https://doi.org/10.1038/s42256-019-0055-y
- BACKGROUNDFoss FM, Girardi M. Mycosis Fungoides and Sezary Syndrome. Hematol Oncol Clin North Am. 2017 Apr;31(2):297-315. doi: 10.1016/j.hoc.2016.11.008. PMID 28340880
- BACKGROUNDGomolin A, Netchiporouk E, Gniadecki R, Litvinov IV. Artificial Intelligence Applications in Dermatology: Where Do We Stand? Front Med (Lausanne). 2020 Mar 31;7:100. doi: 10.3389/fmed.2020.00100. eCollection 2020. PMID 32296706
- BACKGROUNDHodak E, Geskin L, Guenova E, Ortiz-Romero PL, Willemze R, Zheng J, Cowan R, Foss F, Mangas C, Querfeld C. Real-Life Barriers to Diagnosis of Early Mycosis Fungoides: An International Expert Panel Discussion. Am J Clin Dermatol. 2023 Jan;24(1):5-14. doi: 10.1007/s40257-022-00732-w. Epub 2022 Nov 18. PMID 36399227
- BACKGROUNDJartarkar SR. Artificial intelligence: Its role in dermatopathology. Indian J Dermatol Venereol Leprol. 2023 Jul-Aug;89(4):549-552. doi: 10.25259/IJDVL_725_2021.