肝细胞癌
无线电技术
接收机工作特性
医学
病态的
放射科
特征选择
特征(语言学)
癌
磁共振成像
人工智能
内科学
计算机科学
语言学
哲学
作者
Xuehu Wang,Shuping Wang,Xiaoping Yin,Yongchang Zheng
标识
DOI:10.1016/j.compbiomed.2021.105058
摘要
To distinguish combined hepatocellular cholangiocarcinoma (cHCC-CC), hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) before operation using MRI radiomics.This study retrospectively analyzed 196 liver cancers: 33 cHCC-CC, 88 HCC and 75 CC. They had confirmed by pathological analysis in the Affiliated Hospital of Hebei University. MRI lesions were manually segmented by a radiologist.1316 features were extracted from MRI lesions by Pyradiomics. Useful features were retained through two-level feature selection to establish a classification model. Receiver operating characteristic (ROC), area under curve (AUC) and F1-score were used to evaluate the performance of the model.Compared with low-order image features, the performance of the model based on high-order features was improved by about 10%. The model showed better performance in identifying HCC tumors during the delay phase (AUC = 0.91, sensitivity = 0.88, specificity = 0.89, accuracy = 0.89, F1-Score = 0.88).The classification ability of cHCC-CC, HCC and CC can be further improved by extracting MRI high-order features and using a two-level feature selection method.
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