机构：[1]College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China[2]Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China[3]Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China[4]Affiliated Hospital of Hebei University, Baoding, 071000, China河北大学附属医院[5]Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC),Beijing, 100010, China
Object: To distinguish combined hepatocellular cholangiocarcinoma (cHCC-CC), hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) before operation using MRI radiomics. Method: 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.Results: 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). Conclusion: 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.
基金：
Natural Science Foundation of Hebei Province [H2020201021]; China Postdoctoral Fund [2018M631755]; Hebei Province High-level Talent Funding Project-Post-doctoral Research Projects Selective Funding [B2018003002]; Hebei University improve comprehensive strength special funds in the Midwest [801260201011]; National Natural Science Foundation of China [61401308, 61572063]; Natural Science Foundation of Gansu Province [18JR3RA029]; Hebei provincial government funded Provincial Medical Talents Project in 2020 [361007]; Medical discipline cultivation project of Hebei University in 2020 [2020B05]; Outstanding young scientific research and innovation team of Hebei University [605020521007]
第一作者机构：[1]College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China[2]Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China[3]Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
通讯作者：
通讯机构：[1]College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China[2]Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China[3]Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China[*1]College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
推荐引用方式(GB/T 7714)：
Wang Xuehu,Wang Shuping,Yin Xiaoping,et al.MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma[J].COMPUTERS IN BIOLOGY AND MEDICINE.2022,141:doi:10.1016/j.compbiomed.2021.105058.
APA：
Wang, Xuehu,Wang, Shuping,Yin, Xiaoping&Zheng, Yongchang.(2022).MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma.COMPUTERS IN BIOLOGY AND MEDICINE,141,
MLA：
Wang, Xuehu,et al."MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma".COMPUTERS IN BIOLOGY AND MEDICINE 141.(2022)
医学