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
研究概览
地位
条件
详细说明
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.
研究类型
注册 (估计的)
联系人和位置
学习联系方式
- 姓名：tugce nur izbudak kara, MD
- 电话号码：+905395976598
- 邮箱：eizbudak@icloud.com
学习地点
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Istanbul
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Istanbul、Istanbul、土耳其（türkiye）、34000
- 招聘中
- Istanbul Training and Research Hospital
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接触：
- tugce nur izbudak kara, MD
- 电话号码：+905395976598
- 邮箱：eizbudak@icloud.com
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接触：
- Ayse Esra Koku Aksu, MD
- 电话号码：+905059126069
- 邮箱：esraaksu@gmail.com
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首席研究员：
- Ayse Esra Koku Aksu, MD
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副研究员：
- Tugce Nur Izbudak Kara, MD
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副研究员：
- Duygu Yamen, MD
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参与标准
资格标准
适合学习的年龄
- 孩子
- 成人
- 年长者
接受健康志愿者
取样方法
研究人群
描述
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.
学习计划
研究是如何设计的？
设计细节
队列和干预
团体/队列 |
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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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研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Accuracy of artificial intelligence-based classification of basal cell carcinoma risk groups
大体时间：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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次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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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
大体时间：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
大体时间：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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合作者和调查者
调查人员
- 首席研究员：Ayse Esra Koku Aksu, MD、Istanbul Training and Research Hospital
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (估计的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
其他研究编号
- SBU-IEAH-DERM-BCCAI-163
- IEAH-EC-163 (其他标识符：Istanbul Training and Research Hospital Non-Interventional Clinical Research Ethics Committee)
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)？
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