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
調査の概要
状態
条件
詳細な説明
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.
研究の種類
入学 (推定)
連絡先と場所
研究連絡先
- 名前：tugce nur izbudak kara, MD
- 電話番号：+905395976598
- メール：eizbudak@icloud.com
研究場所
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Istanbul
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Istanbul、Istanbul、トルコ（Türkiye）、34000
- 募集
- Istanbul Training and Research Hospital
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コンタクト：
- tugce nur izbudak kara, MD
- 電話番号：+905395976598
- メール：eizbudak@icloud.com
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コンタクト：
- Ayse Esra Koku Aksu, MD
- 電話番号：+905059126069
- メール：esraaksu@gmail.com
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主任研究者：
- Ayse Esra Koku Aksu, MD
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副調査官：
- Tugce Nur Izbudak Kara, MD
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副調査官：
- Duygu Yamen, MD
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参加基準
適格基準
就学可能な年齢
- 子
- 大人
- 高齢者
健康ボランティアの受け入れ
サンプリング方法
調査対象母集団
説明
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.
研究計画
研究はどのように設計されていますか？
デザインの詳細
コホートと介入
グループ/コホート |
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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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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Accuracy of artificial intelligence-based classification of basal cell carcinoma risk groups
時間枠：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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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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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
時間枠：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
時間枠：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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協力者と研究者
捜査官
- 主任研究者：Ayse Esra Koku Aksu, MD、Istanbul Training and Research Hospital
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
追加の関連 MeSH 用語
その他の研究ID番号
- SBU-IEAH-DERM-BCCAI-163
- IEAH-EC-163 (その他の識別子：Istanbul Training and Research Hospital Non-Interventional Clinical Research Ethics Committee)
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
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