Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis: A Training and Validation Study

July 14, 2026 updated by: Kariman Mostafa Sayed Mansour, Cairo University

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.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Detailed Description

Cutaneous lymphoproliferative diseases (CLPDs) encompass both benign and malignant disorders, ranging from pseudolymphomas to cutaneous T-cell and B-cell lymphomas. Mycosis fungoides (MF) is the most common malignant subtype, but diagnosis is often challenging because benign inflammatory conditions can closely mimic lymphoma. Histopathological examination remains the gold standard, yet interpretation is subjective and prone to inter-observer variability. This highlights the need for standardized diagnostic tools, including artificial intelligence (AI) solutions.

This study is a retrospective diagnostic accuracy investigation using routinely collected data. Archived hematoxylin and eosin (H&E) stained slides of patients with MF and other CLPDs will be retrieved from the Dermatopathology Unit at Kasr Al-Aini Hospitals, Cairo University. Slides of benign mimickers such as pseudolymphoma and pityriasis lichenoides will also be included. Cases with poor slide quality or insufficient data will be excluded.

Digitized images will be captured using both high-resolution microscope cameras and standardized smartphone devices to evaluate feasibility. Experienced dermatopathologists will annotate regions of interest, and relevant clinical data will be extracted to build a structured database. Deep learning models, particularly convolutional neural networks (CNNs), will be trained and validated on these datasets. Preprocessing techniques such as color normalization, stain separation, and data augmentation will be applied to enhance robustness.

The primary outcomes are diagnostic accuracy, sensitivity, specificity, and predictive values of the AI models in differentiating malignant from benign CLPDs, and in staging MF. Secondary outcomes include comparison with expert dermatopathologists, assessment of smartphone-based imaging, and evaluation across magnification levels. More than 500 slides collected over the past five years will be used, divided into training, validation, and testing sets.

By integrating AI into dermatopathology, this study aims to reduce diagnostic variability, improve accuracy, and explore novel imaging approaches. The work represents one of the first applications of deep learning to CLPDs, with potential to enhance patient care in both specialized centers and resource-limited healthcare settings.

Study Type

Observational

Enrollment (Estimated)

463

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Egypt
      • Cairo, Egypt
        • Kasr Al-Aini Hospitals, Cairo University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

Archived slides of patients diagnosed with malignant CLPDs (e.g., mycosis fungoides, cutaneous B-cell lymphoma) and benign mimickers (e.g., pseudolymphoma, pityriasis lichenoides chronica, PLEVA) were identified from the pathology database of Kasr Al-Aini Hospitals, Cairo University.

Cases were selected based on WHO-EORTC diagnostic criteria and availability of adequate quality H&E slides plus relevant clinical data.

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
MF
Patients diagnosed histopathologically as mycosis fungoides
Diagnostic test: AI-assisted histopathology image analysis

Development and validation of a deep learning model using digitized hematoxylin and eosin (H&E) stained slides and clinical data to differentiate malignant CLPDs from benign mimickers.

Comparator: Standard histopathological diagnosis by experienced dermatopathologists.
PLC/PLEVA
Patients diagnosed histopathologically as PLC or PLEVA
Diagnostic test: AI-assisted histopathology image analysis

Development and validation of a deep learning model using digitized hematoxylin and eosin (H&E) stained slides and clinical data to differentiate malignant CLPDs from benign mimickers.

Comparator: Standard histopathological diagnosis by experienced dermatopathologists.
TCD
Patients diagnosed histopathologically as T cell dyscrasia
Diagnostic test: AI-assisted histopathology image analysis

Development and validation of a deep learning model using digitized hematoxylin and eosin (H&E) stained slides and clinical data to differentiate malignant CLPDs from benign mimickers.

Comparator: Standard histopathological diagnosis by experienced dermatopathologists.
BCL
Patients diagnosed histopathologically as B cell lymphoma
Diagnostic test: AI-assisted histopathology image analysis

Development and validation of a deep learning model using digitized hematoxylin and eosin (H&E) stained slides and clinical data to differentiate malignant CLPDs from benign mimickers.

Comparator: Standard histopathological diagnosis by experienced dermatopathologists.
Pseudolymphoma
Patients diagnosed histopathologically as pseudo lymphoma
Diagnostic test: AI-assisted histopathology image analysis

Development and validation of a deep learning model using digitized hematoxylin and eosin (H&E) stained slides and clinical data to differentiate malignant CLPDs from benign mimickers.

Comparator: Standard histopathological diagnosis by experienced dermatopathologists.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic accuracy of AI model
Time Frame: Baseline (In retrospective diagnostic studies, the moment the AI evaluates the historical slide is considered the patients's baseline).
Accuracy, sensitivity, specificity, and positive predictive value of the trained AI models in differentiating benign CLPDs from malignant types.
Baseline (In retrospective diagnostic studies, the moment the AI evaluates the historical slide is considered the patients's baseline).

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Comparison with dermatopathologists
Time Frame: Baseline (In retrospective diagnostic studies, the moment the AI evaluates the historical slide is considered the patients's baseline).
Compare AI model diagnostic accuracy with that of experienced dermatopathologists
Baseline (In retrospective diagnostic studies, the moment the AI evaluates the historical slide is considered the patients's baseline).
Smartphone imaging feasibility
Time Frame: Baseline (In retrospective diagnostic studies, the moment the AI evaluates the historical slide is considered the patients's baseline).
Assess feasibility and diagnostic accuracy of AI models using smartphone-captured histopathology images.
Baseline (In retrospective diagnostic studies, the moment the AI evaluates the historical slide is considered the patients's baseline).

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Cairo University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2026

Primary Completion (Estimated)

November 30, 2026

Study Completion (Estimated)

December 30, 2026

Study Registration Dates

First Submitted

June 30, 2026

First Submitted That Met QC Criteria

July 14, 2026

First Posted (Actual)

July 15, 2026

Study Record Updates

Last Update Posted (Actual)

July 15, 2026

Last Update Submitted That Met QC Criteria

July 14, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • MD-152-2025

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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