Bo Kyoung Seo
고려대학교 의과대학 · 의학
Bo Kyoung Seo 교수의 연구실은 유방암의 조기 진단과 정밀의료를 목표로, 영상유전자학( radiogenomics )과 머신러닝 기반 영상 분석 기술을 융합한 연구를 수행하고 있습니다. 주요 연구 방향은 저선량 유방 CT를 활용한 종양 혈관성 지표 측정, 영상 특성과 유전자형·예후 생물학적 마커 간의 연관성 분석, 그리고 아시안 여성 대상 유방촬영 영상에 기반한 딥러닝 기반 암 위험 예측 모델 개발입니다. 특히 영상 영상 특성과 임상·분자적 특성을 연결하는 다중모달리식 접근이 두드러집니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
At CT, SMM appears as a result of malignant neoplasms, inflammation, or vascular disorders.
Real-time compound and pulse-inversion harmonic imaging procedures are superior to conventional sonography in terms of both lesion conspicuity and the further characterization of breast nodules. Real-time compound imaging is the best technique for evaluation of the margin and internal echotexture of nodules, while pulse-inversion harmonic imaging is very effective for the evaluation of the posterior echo patterns.
Low-dose perfusion CT in the prone position is feasible to quantify tumor vascularity in breast cancers, and CT perfusion indexes are significantly correlated with prognostic biomarkers and molecular subtypes of breast cancer.
This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01-1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating charac
The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, vali
