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
Visão geral do estudo
Status
Condições
Descrição detalhada
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 estudo
Inscrição (Estimado)
Contactos e Locais
Contato de estudo
- Nome: tugce nur izbudak kara, MD
- Número de telefone: +905395976598
- E-mail: eizbudak@icloud.com
Locais de estudo
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Istanbul
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Istanbul, Istanbul, Turquia (Türkiye), 34000
- Recrutamento
- Istanbul Training and Research Hospital
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Contato:
- tugce nur izbudak kara, MD
- Número de telefone: +905395976598
- E-mail: eizbudak@icloud.com
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Contato:
- Ayse Esra Koku Aksu, MD
- Número de telefone: +905059126069
- E-mail: esraaksu@gmail.com
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Investigador principal:
- Ayse Esra Koku Aksu, MD
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Subinvestigador:
- Tugce Nur Izbudak Kara, MD
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Subinvestigador:
- Duygu Yamen, MD
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Critérios de participação
Critérios de elegibilidade
Idades elegíveis para estudo
- Filho
- Adulto
- Adulto mais velho
Aceita Voluntários Saudáveis
Método de amostragem
População do estudo
Descrição
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.
Plano de estudo
Como o estudo é projetado?
Detalhes do projeto
Coortes e Intervenções
Grupo / Coorte |
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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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O que o estudo está medindo?
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
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Accuracy of artificial intelligence-based classification of basal cell carcinoma risk groups
Prazo: 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 resultados secundários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
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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
Prazo: 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
Prazo: 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
Datas de registro do estudo
Datas Principais do Estudo
Início do estudo (Real)
Conclusão Primária (Estimado)
Conclusão do estudo (Estimado)
Datas de inscrição no estudo
Enviado pela primeira vez
Enviado pela primeira vez que atendeu aos critérios de CQ
Primeira postagem (Real)
Atualizações de registro de estudo
Última Atualização Postada (Real)
Última atualização enviada que atendeu aos critérios de controle de qualidade
Última verificação
Mais Informações
Termos relacionados a este estudo
Palavras-chave
Termos MeSH relevantes adicionais
Outros números de identificação do estudo
- SBU-IEAH-DERM-BCCAI-163
- IEAH-EC-163 (Outro identificador: Istanbul Training and Research Hospital Non-Interventional Clinical Research Ethics Committee)
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