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
연구 개요
상태
정황
상세 설명
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.
연구 유형
등록 (추정된)
연락처 및 위치
연구 연락처
- 이름: tugce nur izbudak kara, MD
- 전화번호: +905395976598
- 이메일: eizbudak@icloud.com
연구 장소
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Istanbul
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Istanbul, Istanbul, 터키 (Türkiye), 34000
- 모병
- Istanbul Training and Research Hospital
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연락하다:
- tugce nur izbudak kara, MD
- 전화번호: +905395976598
- 이메일: eizbudak@icloud.com
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연락하다:
- Ayse Esra Koku Aksu, MD
- 전화번호: +905059126069
- 이메일: esraaksu@gmail.com
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수석 연구원:
- Ayse Esra Koku Aksu, MD
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부수사관:
- Tugce Nur Izbudak Kara, MD
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부수사관:
- Duygu Yamen, MD
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참여기준
자격 기준
공부할 수 있는 나이
- 어린이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
샘플링 방법
연구 인구
설명
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.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
코호트 및 개입
그룹/코호트 |
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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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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Accuracy of artificial intelligence-based classification of basal cell carcinoma risk groups
기간: 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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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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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
기간: 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
기간: 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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공동 작업자 및 조사자
수사관
- 수석 연구원: Ayse Esra Koku Aksu, MD, Istanbul Training and Research Hospital
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (추정된)
연구 완료 (추정된)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
추가 관련 MeSH 약관
기타 연구 ID 번호
- SBU-IEAH-DERM-BCCAI-163
- IEAH-EC-163 (기타 식별자: Istanbul Training and Research Hospital Non-Interventional Clinical Research Ethics Committee)
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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