MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma

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) 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
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:141: 105058-105058 被引量:21
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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