AI-Based Risk Classification and Histopathological Subtype Prediction of Basal Cell Carcinoma Using Dermoscopic Images

NCT07677124 | Recruiting

• 2 other identifiers: SBU-IEAH-DERM-BCCAI-163IEAH-EC-163
• Study Type: observational
• Target: 2,500
• Locations: 1 country
• Sites: 1

Brief Summary

This retrospective observational study aims to develop and evaluate a convolutional neural network (CNN)-based artificial intelligence model for risk classification and histopathological subtype prediction of basal cell carcinoma (BCC) using clinical and dermoscopic images. Histopathologically confirmed BCC cases from a dermatology archive will be included. The primary objective is to assess the diagnostic performance of the CNN model in classifying BCC as low-risk or high-risk. Secondary objectives include predicting histopathological subtypes and comparing the model's performance with that of dermatology physicians. Histopathological diagnosis will serve as the reference standard. All archived data will be anonymized before analysis.

Conditions

Basal Cell Carcinoma

Keywords

Basal Cell Carcinoma; Artificial Intelligence; Convolutional Neural Network; Dermoscopy; Skin Cancer; Histopathological Subtypes; Risk Classification

Outcome Measures

Primary Outcomes (1)

• Accuracy of artificial intelligence-based classification of basal cell carcinoma risk groups

  Diagnostic accuracy of the convolutional neural network model in distinguishing low-risk and high-risk basal cell carcinoma using dermoscopic images, compared with histopathological diagnosis as the reference standard.

  Baseline

Secondary Outcomes (2)

• Diagnostic accuracy (accuracy, sensitivity, specificity, F1-score and ROC-AUC) of convolutional neural network for histopathological subtype prediction of basal cell carcinoma using dermoscopic images

  baseline

• Diagnostic accuracy (accuracy, sensitivity, specificity, F1-score and ROC-AUC) of artificial intelligence compared with dermatologists for basal cell carcinoma risk classification

  baseline

Study Arms (2)

Low-Risk Basal Cell Carcinoma

Patients with histopathologically confirmed low-risk basal cell carcinoma, including nodular, superficial, pigmented, adenoid, solid, and nodulocystic subtypes. Clinical and dermoscopic images will be used for artificial intelligence-based risk classification and subtype prediction.

High-Risk Basal Cell Carcinoma

Patients with histopathologically confirmed high-risk basal cell carcinoma, including infiltrative, micronodular, morpheaform, and basosquamous subtypes. Clinical and dermoscopic images will be used for artificial intelligence-based risk classification and histopathological subtype prediction.

Eligibility Criteria

• Age: 0 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of patients with histopathologically confirmed basal cell carcinoma who have dermoscopic images of sufficient quality for artificial intelligence analysis and documented histopathological subtype information. Archived clinical and dermoscopic images collected at Istanbul Training and Research Hospital will be retrospectively analyzed.

You may qualify if:

• Patients with histopathologically confirmed basal cell carcinoma.
• Cases with a specified histopathological subtype.
• Availability of dermoscopic images with sufficient image quality and resolution for artificial intelligence analysis.

You may not qualify if:

• Cases without histopathological confirmation of basal cell carcinoma.
• Cases with unspecified histopathological subtype.
• Images with insufficient quality or resolution for artificial intelligence analysis.
• Cases without available dermoscopic images.

Sponsors & Collaborators

• Istanbul Training and Research Hospital: lead

Study Sites (1)

Istanbul Training and Research Hospital

Istanbul, Istanbul, 34000, Turkey (Türkiye)

RECRUITING

MeSH Terms

Conditions

Carcinoma, Basal Cell; Skin Neoplasms

Condition Hierarchy (Ancestors)

Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Basal Cell; Neoplasms by Site; Skin Diseases; Skin and Connective Tissue Diseases

Study Officials

• Ayse Esra Koku Aksu, MD

  Istanbul Training and Research Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Tugce Nur Izbudak Kara, MD

CONTACT

+905395976598; eizbudak@icloud.com

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD

Study Record Dates

• First Submitted: June 19, 2026
• First Posted: June 30, 2026
• Study Start: May 22, 2026
• Primary Completion (Estimated): May 22, 2027
• Study Completion (Estimated): May 22, 2027
• Last Updated: June 30, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will share

Locations

TU (1)