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

Istanbul Training and Research Hospital

Status: Enrolling

Conditions: Basal Cell Carcinoma

Study type: Observational

Funder types: Other

Identifiers

NCT07677124

SBU-IEAH-DERM-BCCAI-163

IEAH-EC-163 (Other Identifier)

Details and patient eligibility

About

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.

Full description

Basal cell carcinoma (BCC) is the most common skin malignancy and comprises histopathological subtypes with different biological behaviors, recurrence risks, and treatment implications. Accurate identification of high-risk and low-risk subtypes is important for clinical decision-making. Dermoscopy improves diagnostic accuracy in BCC; however, prediction of histopathological risk categories based solely on dermoscopic findings remains challenging.

This retrospective observational study will use archived clinical and dermoscopic images, histopathology reports, and clinical records of patients with histopathologically confirmed BCC. All data will be anonymized before analysis. Images containing identifiable patient information will be excluded.

A convolutional neural network (CNN)-based artificial intelligence model will be developed using clinical and dermoscopic images. Images will undergo preprocessing, including standardization of image size, normalization procedures, and removal of potentially identifiable information. The dataset will be divided into training, validation, and test sets while maintaining separation at the patient level to avoid data leakage.

The primary outcome is the diagnostic performance of the CNN model for classification of BCC into low-risk and high-risk histopathological groups. Secondary outcomes include prediction of histopathological subtypes and comparison of model performance with dermatologist assessments. Histopathological diagnosis will serve as the reference standard.

Model performance will be evaluated using accuracy, sensitivity, specificity, precision, recall, F1 score, and area under the receiver operating characteristic curve (ROC-AUC). Comparisons between the artificial intelligence model and physician assessments will be performed using appropriate statistical methods. Interobserver agreement may also be assessed when applicable.

Enrollment: 2,500 estimated patients

Sex: All

Ages: Under 100 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• 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.

Exclusion criteria

• 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.

Trial design

2,500 participants in 2 patient groups

Low-Risk Basal Cell Carcinoma

Description:

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

Description:

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.