Basal Cell CancerAI\bccNCT07786376
AI in Histipathological Diagnosis of Bcc
An observational study of Basal Cell Cancer, run by Al-Azhar University at 1 site in Egypt.
Trial summary
The goal of this study is to evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm in the histopathological diagnosis of bcc compared to certified dermatopathologists Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Basal Cell Cancer
- How many people
- 50 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Oct 2026to 1 Dec 2027
- 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
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: H\&E stained glass slides of bcc cases will be collected from the pathology archive of Al-hussein dermatopathology unit .
Who may be able to join
- Histopathological slides diagnosed as BCC.
- Slides with adequate staining and preservation allowing clear visualization of dermatopathological features.
Who cannot join
- Slides with poor staining quality or significant artifacts interfering with histopathological interpretation.
- Slides that were damaged, faded, or inadequately preserved.
- Cases with uncertain or inconclusive original diagnoses.
- Slides that could not be successfully digitized due to technical limitations ex very short or too long slides.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Evaluation of Artificial Intelligence Algorithms Performance in the Histopathological Diagnosis of Basal Cell Carcinoma
The goal of this study is to evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm in the histopathological diagnosis of bcc compared to certified dermatopathologists
Read the detailed description
Background Basal cell carcinoma (BCC) is the most commonly diagnosed skin cancer worldwide and the predominant form of non-melanoma skin cancers (NMSCs), with an escalating global incidence. Histopathology remains the gold standard for diagnosis; however, manual analysis is labor-intensive, time-consuming, and subject to increasing pressure amid a global shortage of board-certified dermatopathologists. Digital pathology and whole-slide imaging (WSI), combined with advanced artificial intelligence (AI) models such as vision transformers and large language models (e.g., HistoGPT), offer a transformative solution to automate and streamline dermatopathological diagnostics.
Aim of the Work This study aims to evaluate the diagnostic performance and processing efficiency of the AI algorithm HistoGPT in the histopathological diagnosis of basal cell carcinoma compared to certified dermatopathologists.
Methodology This retrospective, blinded, comparative study will be conducted using archived H\&E-stained glass slides retrieved from the pathology archive of the Al-Hussein Dermatopathology Unit between 2010 and 2019. Slides meeting the inclusion criteria will be digitized into high-resolution Whole Slide Images (WSIs) at 40× magnification using the Leica Aperio GT450 scanner. The digitized WSIs will be processed and analyzed through the HistoGPT cloud platform to automatically generate diagnostic reports and classifications. The AI-generated findings will be systematically compared against the reference standard diagnoses established by a panel of certified dermatopathologists under the supervision of Prof. Hussein Hasb El-Nabi. Diagnostic accuracy, concordance, and turnaround time will be evaluated.
Statistical Analysis Data will be analyzed using SPSS (version 26.0) or R-programming. Categorical variables will be compared using appropriate statistical tests, and a $p$-value of $< 0.05$ will be considered statistically significant.
- Conditions
- Basal Cell Cancer
- Keywords
- AI-bcc-histopathology
- Medical subject headings
- Neoplasms, Basal Cell
What is being measured
The primary outcome is the main question the study is designed to answer.
the diagnostic accuracy of the artificial intelligence algorithm, evaluated primarily by its sensitivity and specificity in correctly identifying basal cell carcinoma (BCC) from histopathological images.
Measured: one year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Aug 2026
- Study start1 Oct 2026 (estimated)
- Primary completion1 Oct 2027 (estimated)
- Study completion1 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Basal Cell CancerStudies of the same condition, open and under way.
- More trials run by Al-Azhar 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
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Al-Azhar University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Wafaa Hamada Abdu, Al-Azhar University
- Investigators
- wafaa hamada abdu, master, Principal investigator, alazhar univristy for boys
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07786376
- Sponsor's protocol ID
- (MD)301
- Acronym
- AI\bcc
- Status last verified
- Aug 2026
- First submitted
- 20 Aug 2026
- Last updated
- 26 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 26 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of Artificial Intelligence Algorithms Performance in the Histopathological Diagnosis of Basal Cell Carcinoma
- ClinicalTrials.gov ID
- NCT07786376
- Acronym / sponsor ID
- AI\bcc / (MD)301
- Conditions
- Basal Cell Cancer
- Keywords
- AI-bcc-histopathology
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- Al-Azhar UniversityUniversity, hospital or other
- Study start
- 1 Oct 2026 (estimated)
- Primary completion
- 1 Oct 2027 (estimated)
- Study completion
- 1 Dec 2027 (estimated)
- First posted
- 26 Aug 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.
The goal of this study is to evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm in the histopathological diagnosis of bcc compared to certified dermatopathologists
Detailed description
Background Basal cell carcinoma (BCC) is the most commonly diagnosed skin cancer worldwide and the predominant form of non-melanoma skin cancers (NMSCs), with an escalating global incidence. Histopathology remains the gold standard for diagnosis; however, manual analysis is labor-intensive, time-consuming, and subject to increasing pressure amid a global shortage of board-certified dermatopathologists. Digital pathology and whole-slide imaging (WSI), combined with advanced artificial intelligence (AI) models such as vision transformers and large language models (e.g., HistoGPT), offer a transformative solution to automate and streamline dermatopathological diagnostics.
Aim of the Work This study aims to evaluate the diagnostic performance and processing efficiency of the AI algorithm HistoGPT in the histopathological diagnosis of basal cell carcinoma compared to certified dermatopathologists.
Methodology This retrospective, blinded, comparative study will be conducted using archived H\&E-stained glass slides retrieved from the pathology archive of the Al-Hussein Dermatopathology Unit between 2010 and 2019. Slides meeting the inclusion criteria will be digitized into high-resolution Whole Slide Images (WSIs) at 40× magnification using the Leica Aperio GT450 scanner. The digitized WSIs will be processed and analyzed through the HistoGPT cloud platform to automatically generate diagnostic reports and classifications. The AI-generated findings will be systematically compared against the reference standard diagnoses established by a panel of certified dermatopathologists under the supervision of Prof. Hussein Hasb El-Nabi. Diagnostic accuracy, concordance, and turnaround time will be evaluated.
Statistical Analysis Data will be analyzed using SPSS (version 26.0) or R-programming. Categorical variables will be compared using appropriate statistical tests, and a $p$-value of $< 0.05$ will be considered statistically significant.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- H\&E stained glass slides of bcc cases will be collected from the pathology archive of Al-hussein dermatopathology unit .
Key inclusion criteria (2 listed)
- Histopathological slides diagnosed as BCC.
- Slides with adequate staining and preservation allowing clear visualization of dermatopathological features.
Key exclusion criteria (4 listed)
- Slides with poor staining quality or significant artifacts interfering with histopathological interpretation.
- Slides that were damaged, faded, or inadequately preserved.
- Cases with uncertain or inconclusive original diagnoses.
- Slides that could not be successfully digitized due to technical limitations ex very short or too long slides.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
the diagnostic accuracy of the artificial intelligence algorithm, evaluated primarily by its sensitivity and specificity in correctly identifying basal cell carcinoma (BCC) from histopathological images.
Time frame: one year
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 26 Aug 2026.
- Individual participant dataSharing planned: Undecided
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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.
wafaa hamada abdu, master principal investigator
alazhar univristy for boys
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 Basal Cell Cancer, run by Al-Azhar 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 2 inclusion and 4 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: opening soon, as of the sponsor's last update on 26 Aug 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?
Contact wafaa hamada abdu, master (+20 1061583899 ext. +20 1014494376, wh8119712@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
the diagnostic accuracy of the artificial intelligence algorithm, evaluated primarily by its sensitivity and specificity in correctly identifying basal cell carcinoma (BCC) from histopathological images. (time frame: one year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 26 Aug 2026.