Radiomics-Based Prediction of Microvascular Invasion Grade in Nodular Hepatocellular Carcinoma Using Contrast-Enhanced Magnetic Resonance Imaging

doi:10.2147/JHC.S461420

科研成果详情

题名	Radiomics-Based Prediction of Microvascular Invasion Grade in Nodular Hepatocellular Carcinoma Using Contrast-Enhanced Magnetic Resonance Imaging
作者	Zhang, Zhao; Jia, Xiu-Fen; Chen, Xiao-Yu; Chen, Yong-Hua; Pan, Ke-Hua
发表日期	2024
发表期刊	JOURNAL OF HEPATOCELLULAR CARCINOMA 影响因子和分区
语种	英语
原始文献类型	Article
关键词	hepatocellular carcinoma magnetic resonance imaging microvascular invasion radiomics
其他关键词	PREOPERATIVE PREDICTION
摘要	Objective: The aim of this study is to develop and verify a magnetic resonance imaging (MRI)-based radiomics model for predicting the microvascular invasion grade (MVI) before surgery in individuals diagnosed with nodular hepatocellular carcinoma (HCC). Methods: A total of 198 patients were included in the study and were randomly stratified into two groups: a training group consisting of 139 patients and a test group comprising 59 patients. The tumor lesion was manually segmented on the largest cross-sectional slice using ITK SNAP, with agreement reached between two radiologists. The selection of radiomics features was carried out using the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm. Radiomics models were then developed through maximum correlation, minimum redundancy, and logistic regression analyses. The performance of the models in predicting MVI grade was assessed using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. Results: There were no notable statistical differences in sex, age, BMI (body mass index), tumor size, and location between the training and test groups. The AP and PP radiomic model constructed for predicting MVI grade demonstrated an AUC of 0.83 (0.75- 0.88) and 0.73 (0.64-0.80) in the training group and an AUC of 0.74 (0.61-0.85) and 0.62 (0.48-0.74) in test group, respectively. The combined model consists of imaging data and clinical data (age and AFP), achieved an AUC of 0.85 (0.78-0.91) and 0.77 (0.64-0.87) in the training and test groups, respectively. Conclusion: A radiomics model utilizing -contrast -enhanced MRI demonstrates strong predictive capability for differentiating MVI grades in individuals with nodular HCC. This model could potentially function as a dependable and resilient tool to support hepatologists and radiologists in their preoperative decision -making processes.
资助项目	Foundation of Wenzhou Science & Technology Bureau [Y2020170]
出版者	DOVE MEDICAL PRESS LTD
ISSN	2253-5969
EISSN	2253-5969
卷号	11 页码:1185-1192
DOI	10.2147/JHC.S461420
页数	8
WOS类目	Oncology
WOS研究方向	Oncology
WOS记录号	WOS:001257471200001
收录类别	SCIE ; SCOPUS ; PUBMED
URL	查看原文
PubMed ID	38933179
SCOPUSEID	2-s2.0-85196627233
通讯作者地址	[Pan, Ke-Hua]Wenzhou Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Xuefu North Rd, Wenzhou 325000, Peoples R China.
Scopus学科分类	Oncology;Hepatology
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/215326
专题	第一临床医学院（信息与工程学院）、附属第一医院_影像医学与核医学_放射科
通讯作者	Pan, Ke-Hua
作者单位	Wenzhou Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Xuefu North Rd, Wenzhou 325000, Peoples R China
第一作者单位	第一临床医学院（信息与工程学院）、附属第一医院_影像医学与核医学_放射科
通讯作者单位	第一临床医学院（信息与工程学院）、附属第一医院_影像医学与核医学_放射科
第一作者的第一单位	第一临床医学院（信息与工程学院）、附属第一医院_影像医学与核医学_放射科
推荐引用方式 GB/T 7714	Zhang, Zhao,Jia, Xiu-Fen,Chen, Xiao-Yu,et al. Radiomics-Based Prediction of Microvascular Invasion Grade in Nodular Hepatocellular Carcinoma Using Contrast-Enhanced Magnetic Resonance Imaging[J]. JOURNAL OF HEPATOCELLULAR CARCINOMA,2024,11:1185-1192.
APA	Zhang, Zhao, Jia, Xiu-Fen, Chen, Xiao-Yu, Chen, Yong-Hua, & Pan, Ke-Hua. (2024). Radiomics-Based Prediction of Microvascular Invasion Grade in Nodular Hepatocellular Carcinoma Using Contrast-Enhanced Magnetic Resonance Imaging. JOURNAL OF HEPATOCELLULAR CARCINOMA, 11, 1185-1192.
MLA	Zhang, Zhao,et al."Radiomics-Based Prediction of Microvascular Invasion Grade in Nodular Hepatocellular Carcinoma Using Contrast-Enhanced Magnetic Resonance Imaging".JOURNAL OF HEPATOCELLULAR CARCINOMA 11(2024):1185-1192.