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

Cutaneous Lymphoproliferative Diseases, Mycosis Fungoides of Skin (Diagnosis), PLEVA-PLC Spectrum, Primary Cutaneous B-Cell Lymphoma (CBCL), Pseudolymphoma, T Cell Dyscrasia

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.

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.

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