Characterization of non‐calcified predominant plaque using deep learning and radiomics analyses of coronary computed tomography angiography images  

作　　者：Xin Jin Yuze Li Fei Yan Tao Li Xinghua Zhang Ye Liu Li Yang Huijun Chen 

机构地区：[1]Radiology,Peking University Cancer Hospital,First Medical Center of Chinese PLA General Hospital,Beijing,China [2]Tsinghua University School of Medicine,Beijing,China [3]First Medical Center of Chinese PLA General Hospital,Beijing,China

出　　处：《iRADIOLOGY》2024年第3期260-263,共4页融合影像学(英文)

摘　　要：Background:To use an automated system exploiting the advantages of both a neural network and radiomics for analysis of non‐calcified predominant pla-que(NCPP).Methods:This study retrospectively included 234 patients.Using the work-flow of the previous study,the coronary artery was first segmented,images containing plaques were then extracted,and a classifier was built to identify non‐calcified predominant plaques.Radiomics feature analysis and a visuali-zation tool were used to better distinguish NCPP from other plaques.Results:Twenty‐six representative radiomics features were selected.Dense-Net achieved an area under the curve of 0.889,which was significantly larger(p=0.001)than that obtained using a gradient‐boosted decision tree(0.859).The feature variances and energy features in calcified predominant plaque were both different from those in NCPP.Conclusions:Our automated system provided high‐accuracy analysis of vulnerable plaques using a deep learning approach and predicted useful fea-tures of NCPP using a radiomics‐based approach.

关 键 词：atherosclerosis PLAQUES radiomics CORONARY COMPUTED tomography ANGIOGRAPHY 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]