AI-Based Risk Classification and Histopathological Subtype Prediction of Basal Cell Carcinoma Using Dermoscopic Images (BCC-AI)
Risk Classification and Prediction of Histopathological Subtypes in Basal Cell Carcinoma Using a CNN-Based Artificial Intelligence Model on Dermoscopic Images
Descripción general del estudio
Estado
Condiciones
Descripción detallada
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.
Tipo de estudio
Inscripción (Estimado)
Contactos y Ubicaciones
Estudio Contacto
- Nombre: tugce nur izbudak kara, MD
- Número de teléfono: +905395976598
- Correo electrónico: eizbudak@icloud.com
Ubicaciones de estudio
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Istanbul
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Istanbul, Istanbul, Turquía (Türkiye), 34000
- Reclutamiento
- Istanbul Training and Research Hospital
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Contacto:
- tugce nur izbudak kara, MD
- Número de teléfono: +905395976598
- Correo electrónico: eizbudak@icloud.com
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Contacto:
- Ayse Esra Koku Aksu, MD
- Número de teléfono: +905059126069
- Correo electrónico: esraaksu@gmail.com
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Investigador principal:
- Ayse Esra Koku Aksu, MD
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Sub-Investigador:
- Tugce Nur Izbudak Kara, MD
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Sub-Investigador:
- Duygu Yamen, MD
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Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
- Niño
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
Método de muestreo
Población de estudio
Descripción
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 de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
Cohortes e Intervenciones
Grupo / Cohorte |
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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.
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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.
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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
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Accuracy of artificial intelligence-based classification of basal cell carcinoma risk groups
Periodo de tiempo: Baseline
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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.
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Baseline
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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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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
Periodo de tiempo: baseline
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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.
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baseline
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Diagnostic accuracy (accuracy, sensitivity, specificity, F1-score and ROC-AUC) of artificial intelligence compared with dermatologists for basal cell carcinoma risk classification
Periodo de tiempo: baseline
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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.
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baseline
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Colaboradores e Investigadores
Patrocinador
Investigadores
- Investigador principal: Ayse Esra Koku Aksu, MD, Istanbul Training and Research Hospital
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Actual)
Finalización primaria (Estimado)
Finalización del estudio (Estimado)
Fechas de registro del estudio
Enviado por primera vez
Primero enviado que cumplió con los criterios de control de calidad
Publicado por primera vez (Actual)
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
Última actualización enviada que cumplió con los criterios de control de calidad
Última verificación
Más información
Términos relacionados con este estudio
Palabras clave
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- SBU-IEAH-DERM-BCCAI-163
- IEAH-EC-163 (Otro identificador: Istanbul Training and Research Hospital Non-Interventional Clinical Research Ethics Committee)
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
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