인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Purpose Predicting clinical complete response (CR) to neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC) remains a clinical challenge. Existing CT-based radiomics studies have shown promise, but MRI-derived radiomics using machine learning (ML) has not been systematically explored. This study aimed to develop and validate ML-based radiomics models using multiparametric MRI and clinical data to predict CR in MIBC patients receiving NAC. Materials and methods MIBC patients eligible for platinum-based NAC were prospectively included. Tumor regions were manually segmented from pre-treatment MRI sequences (CE-T1WI, T2WI, DWI, ADC maps). Radiomics features and clinical variables were extracted. Least Absolute Shrinkage and Selection Operator (LASSO) was used for feature selection, and multiple ML classifiers were trained using stratified fivefold cross-validation. The area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, precision, and F1 scores were calculated. Results Among 52 patients, 19 (36.5%) achieved CR. Of 177 extracted features, CE-T1WI-derived models achieved the best performance. The support vector machine (SVM) yielded the highest AUC-ROC of 0.88, with sensitivity, specificity, and precision of 0.82, 0.79, and 0.79, respectively. The K-Nearest Neighbors (KNN) model performed comparably (AUC = 0.87). Clinical feature-based models also performed strongly (RF, AUC = 0.86). Conclusions ML-based radiomics models derived from multiparametric MRI sequences and clinical features hold substantial potential for predicting clinical CR to the NAC in MIBC patients. These results suggest that MR images can provide reliable insights into treatment response, offering a noninvasive and effective tool for clinical decision-making. This is the first prospective ML-based MRI radiomics study in this domain. We present this work as a proof-of-concept requiring external multicenter validation in a larger dataset to confirm these findings.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
