Cutaneous Lymphoproliferative DiseasesNCT07705386
Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis: A Training and Validation Study
An observational study of Cutaneous Lymphoproliferative Diseases and Mycosis Fungoides of Skin (Diagnosis), run by Cairo University at 1 site in Egypt.
Trial summary
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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Cutaneous Lymphoproliferative Diseases, Mycosis Fungoides of Skin (Diagnosis), T Cell Dyscrasia
- What is being tested?
- AI-assisted histopathology image analysis
- How many people
- 463 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Jan 2026to 30 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Child, Adult, Older adult
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.
Who may be able to join
- 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).
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read the 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.
- Conditions
- Cutaneous Lymphoproliferative Diseases, Mycosis Fungoides of Skin (Diagnosis), T Cell Dyscrasia, PLEVA-PLC Spectrum, Pseudolymphoma, Primary Cutaneous B-Cell Lymphoma (CBCL)
- Being tested
- AI-assisted histopathology image analysis
- Medical subject headings
- Disease, Pseudolymphoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted histopathology image analysisDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| MF | Patients diagnosed histopathologically as mycosis fungoides Receives: Diagnostic Test: AI-assisted histopathology image analysis | |
| PLC/PLEVA | Patients diagnosed histopathologically as PLC or PLEVA Receives: Diagnostic Test: AI-assisted histopathology image analysis | |
| TCD | Patients diagnosed histopathologically as T cell dyscrasia Receives: Diagnostic Test: AI-assisted histopathology image analysis | |
| BCL | Patients diagnosed histopathologically as B cell lymphoma Receives: Diagnostic Test: AI-assisted histopathology image analysis | |
| Pseudolymphoma | Patients diagnosed histopathologically as pseudo lymphoma Receives: Diagnostic Test: AI-assisted histopathology image analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of AI model
Accuracy, sensitivity, specificity, and positive predictive value of the trained AI models in differentiating benign CLPDs from malignant types.
Measured: Baseline (In retrospective diagnostic studies, the moment the AI evaluates the historical slide is considered the patients's baseline).
Secondary outcomes (2)
Comparison with dermatopathologists
Compare AI model diagnostic accuracy with that of experienced dermatopathologists
Measured: Baseline (In retrospective diagnostic studies, the moment the AI evaluates the historical slide is considered the patients's baseline).
Smartphone imaging feasibility
Assess feasibility and diagnostic accuracy of AI models using smartphone-captured histopathology images.
Measured: Baseline (In retrospective diagnostic studies, the moment the AI evaluates the historical slide is considered the patients's baseline).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Jul 2026
- Study start1 Jan 2026
- Primary completion30 Nov 2026 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (20)Papers the sponsor has linked to this study.
- Other trials for Cutaneous Lymphoproliferative DiseasesStudies of the same condition, open and under way.
- More trials run by Cairo UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 463 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kariman Mostafa Sayed Mansour, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07705386
- Sponsor's protocol ID
- MD-152-2025
- Status last verified
- Jan 2026
- First submitted
- 30 Jun 2026
- Last updated
- 15 Jul 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Zama 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.
PubMed 39594976Valencia 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.
PubMed 31277835Rashad, 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
Floridi, 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
Foss 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.
PubMed 28340880Gomolin 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.
PubMed 32296706Hodak 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.
PubMed 36399227Jartarkar 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.
PubMed 36688886Kempf W, Mitteldorf C, Cerroni L, Willemze R, Berti E, Guenova E, Scarisbrick JJ, Battistella M. Classifications of cutaneous lymphomas and lymphoproliferative disorders: An update from the EORTC cutaneous lymphoma histopathology group. J Eur Acad Dermatol Venereol. 2024 Aug;38(8):1491-1503. doi: 10.1111/jdv.19987. Epub 2024 Apr 6.
PubMed 38581201Kent MN, Olsen TG, Feeser TA, Tesno KC, Moad JC, Conroy MP, Kendrick MJ, Stephenson SR, Murchland MR, Khan AU, Peacock EA, Brumfiel A, Bottomley MA. Diagnostic Accuracy of Virtual Pathology vs Traditional Microscopy in a Large Dermatopathology Study. JAMA Dermatol. 2017 Dec 1;153(12):1285-1291. doi: 10.1001/jamadermatol.2017.3284.
PubMed 29049424Ottevanger R, de Bruin DT, Willemze R, Jansen PM, Bekkenk MW, de Haas ERM, Horvath B, van Rossum MM, Sanders CJG, Veraart JCJM, Vermeer MH, Quint KD. Incidence of mycosis fungoides and Sezary syndrome in the Netherlands between 2000 and 2020. Br J Dermatol. 2021 Aug;185(2):434-435. doi: 10.1111/bjd.20048. Epub 2021 May 4. No abstract available.
PubMed 33690948Polesie S, McKee PH, Gardner JM, Gillstedt M, Siarov J, Neittaanmaki N, Paoli J. Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey. Front Med (Lausanne). 2020 Oct 20;7:591952. doi: 10.3389/fmed.2020.591952. eCollection 2020.
PubMed 33195357Tsianakas A, Kienast AK, Hoeger PH. Infantile-onset cutaneous T-cell lymphoma. Br J Dermatol. 2008 Dec;159(6):1338-41. doi: 10.1111/j.1365-2133.2008.08794.x. Epub 2008 Aug 19.
PubMed 18717674Willemze R. Cutaneous lymphoproliferative disorders: Back to the future. J Cutan Pathol. 2024 Jun;51(6):468-476. doi: 10.1111/cup.14609. Epub 2024 Mar 18.
PubMed 38499969Fatima S, Siddiqui S, Tariq MU, Ishtiaque H, Idrees R, Ahmed Z, Ahmed A. Mycosis Fungoides: A Clinicopathological Study of 60 Cases from a Tertiary Care Center. Indian J Dermatol. 2020 Mar-Apr;65(2):123-129. doi: 10.4103/ijd.IJD_602_18.
PubMed 32180598
Study at a glance
From the registry record, last updated by the sponsor on 15 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis: A Training and Validation Study
- ClinicalTrials.gov ID
- NCT07705386
- Acronym / sponsor ID
- MD-152-2025
- Conditions
- Cutaneous Lymphoproliferative Diseases, Mycosis Fungoides of Skin (Diagnosis), T Cell Dyscrasia, PLEVA-PLC Spectrum, Pseudolymphoma, Primary Cutaneous B-Cell Lymphoma (CBCL)
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 463 participants (estimated)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- 30 Nov 2026 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 15 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| MF | – | Diagnostic Test: AI-assisted histopathology image analysisPatients diagnosed histopathologically as mycosis fungoides |
| PLC/PLEVA | – | Diagnostic Test: AI-assisted histopathology image analysisPatients diagnosed histopathologically as PLC or PLEVA |
| TCD | – | Diagnostic Test: AI-assisted histopathology image analysisPatients diagnosed histopathologically as T cell dyscrasia |
| BCL | – | Diagnostic Test: AI-assisted histopathology image analysisPatients diagnosed histopathologically as B cell lymphoma |
| Pseudolymphoma | – | Diagnostic Test: AI-assisted histopathology image analysisPatients diagnosed histopathologically as pseudo lymphoma |
Who may qualify
- Sex
- All
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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).
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-assisted histopathology image analysisDevelopment 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. | Diagnostic test | BCL, MF, PLC/PLEVA, Pseudolymphoma, TCD |
Primary outcome
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.
2 secondary outcomes
Comparison with dermatopathologists
Time frame: 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).
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jul 2026.
- Background: PubMed 39594976Zama 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.
- Background: PubMed 31277835Valencia 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.
- 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
- Background: PubMed 28340880Foss 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.
- Background: PubMed 32296706Gomolin 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.
- Background: PubMed 36399227Hodak 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.
- Background: PubMed 36688886Jartarkar 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.
- Background: PubMed 38581201Kempf W, Mitteldorf C, Cerroni L, Willemze R, Berti E, Guenova E, Scarisbrick JJ, Battistella M. Classifications of cutaneous lymphomas and lymphoproliferative disorders: An update from the EORTC cutaneous lymphoma histopathology group. J Eur Acad Dermatol Venereol. 2024 Aug;38(8):1491-1503. doi: 10.1111/jdv.19987. Epub 2024 Apr 6.
- Background: PubMed 29049424Kent MN, Olsen TG, Feeser TA, Tesno KC, Moad JC, Conroy MP, Kendrick MJ, Stephenson SR, Murchland MR, Khan AU, Peacock EA, Brumfiel A, Bottomley MA. Diagnostic Accuracy of Virtual Pathology vs Traditional Microscopy in a Large Dermatopathology Study. JAMA Dermatol. 2017 Dec 1;153(12):1285-1291. doi: 10.1001/jamadermatol.2017.3284.
- Background: PubMed 33690948Ottevanger R, de Bruin DT, Willemze R, Jansen PM, Bekkenk MW, de Haas ERM, Horvath B, van Rossum MM, Sanders CJG, Veraart JCJM, Vermeer MH, Quint KD. Incidence of mycosis fungoides and Sezary syndrome in the Netherlands between 2000 and 2020. Br J Dermatol. 2021 Aug;185(2):434-435. doi: 10.1111/bjd.20048. Epub 2021 May 4. No abstract available.
- Background: PubMed 33195357Polesie S, McKee PH, Gardner JM, Gillstedt M, Siarov J, Neittaanmaki N, Paoli J. Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey. Front Med (Lausanne). 2020 Oct 20;7:591952. doi: 10.3389/fmed.2020.591952. eCollection 2020.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cutaneous Lymphoproliferative Diseases and Mycosis Fungoides of Skin (Diagnosis), run by Cairo University at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 15 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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).). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07705386, which also lists 20 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Jul 2026; AzTrials last refreshed its copy on 8 Nov 2026.