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

29 juin 2026 mis à jour par: Tugce Nur Izbudak Kara, Istanbul Training and Research Hospital

Risk Classification and Prediction of Histopathological Subtypes in Basal Cell Carcinoma Using a CNN-Based Artificial Intelligence Model on Dermoscopic Images

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.

Aperçu de l'étude

Statut

Recrutement

Les conditions

Carcinome basocellulaire

Description détaillée

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.

Type d'étude

Observationnel

Inscription (Estimé)

2500

Contacts et emplacements

Cette section fournit les coordonnées de ceux qui mènent l'étude et des informations sur le lieu où cette étude est menée.

Coordonnées de l'étude

Nom: tugce nur izbudak kara, MD
Numéro de téléphone: +905395976598
E-mail: eizbudak@icloud.com

Lieux d'étude

Turquie (Türkiye)
- Istanbul
  - Istanbul, Istanbul, Turquie (Türkiye), 34000
    - Recrutement
    - Istanbul Training and Research Hospital
    - Contact:
      
      tugce nur izbudak kara, MD
      
      Numéro de téléphone: +905395976598
      
      E-mail: eizbudak@icloud.com
    - Contact:
      
      Ayse Esra Koku Aksu, MD
      
      Numéro de téléphone: +905059126069
      
      E-mail: esraaksu@gmail.com
    - Chercheur principal:
      
      Ayse Esra Koku Aksu, MD
    - Sous-enquêteur:
      
      Tugce Nur Izbudak Kara, MD
    - Sous-enquêteur:
      
      Duygu Yamen, MD

Critères de participation

Les chercheurs recherchent des personnes qui correspondent à une certaine description, appelée critères d'éligibilité. Certains exemples de ces critères sont l'état de santé général d'une personne ou des traitements antérieurs.

Critère d'éligibilité

Âges éligibles pour étudier

Enfant
Adulte
Adulte plus âgé

Accepte les volontaires sains

Non

Méthode d'échantillonnage

Échantillon non probabiliste

Population étudiée

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.

La description

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.

Plan d'étude

Cette section fournit des détails sur le plan d'étude, y compris la façon dont l'étude est conçue et ce que l'étude mesure.

Comment l'étude est-elle conçue ?

Détails de conception

Nombre de groupes / cohortes

Cohortes et interventions

Groupe / Cohorte
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.

Que mesure l'étude ?

Principaux critères de jugement

Mesure des résultats	Description de la mesure	Délai
Accuracy of artificial intelligence-based classification of basal cell carcinoma risk groups Délai: Baseline	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

Mesures de résultats secondaires

Mesure des résultats	Description de la mesure	Délai
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 Délai: baseline	Diagnostic performance of the convolutional neural network in predicting histopathological subtypes of basal cell carcinoma from dermoscopic images compared with histopathological diagnosis (reference standard). Diagnostic accuracy will be assessed using accuracy, sensitivity, specificity, precision, F1-score and ROC-AUC.	baseline
Diagnostic accuracy (accuracy, sensitivity, specificity, F1-score and ROC-AUC) of artificial intelligence compared with dermatologists for basal cell carcinoma risk classification Délai: baseline	Comparison of diagnostic performance between the artificial intelligence model and dermatologists in risk classification of basal cell carcinoma. Performance will be assessed using accuracy, sensitivity, specificity, precision, F1-score and ROC-AUC.	baseline

Collaborateurs et enquêteurs

C'est ici que vous trouverez les personnes et les organisations impliquées dans cette étude.

Parrainer

Istanbul Training and Research Hospital

Les enquêteurs

Chercheur principal: Ayse Esra Koku Aksu, MD, Istanbul Training and Research Hospital

Dates d'enregistrement des études

Ces dates suivent la progression des dossiers d'étude et des soumissions de résultats sommaires à ClinicalTrials.gov. Les dossiers d'étude et les résultats rapportés sont examinés par la Bibliothèque nationale de médecine (NLM) pour s'assurer qu'ils répondent à des normes de contrôle de qualité spécifiques avant d'être publiés sur le site Web public.

Dates principales de l'étude

Début de l'étude (Réel)

22 mai 2026

Achèvement primaire (Estimé)

22 mai 2027

Achèvement de l'étude (Estimé)

22 mai 2027

Dates d'inscription aux études

Première soumission

19 juin 2026

Première soumission répondant aux critères de contrôle qualité

29 juin 2026

Première publication (Réel)

30 juin 2026

Mises à jour des dossiers d'étude

Dernière mise à jour publiée (Réel)