Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI

医学 磁共振成像 神经组阅片室 放射科 无线电技术 肝细胞癌 介入放射学 肝细胞癌 超声波 核医学 内科学 神经学 精神科
作者
Shi‐Ting Feng,Yingmei Jia,Bing Liao,Bingsheng Huang,Qian Zhou,Xin Li,Kaikai Wei,Lili Chen,Bin Li,Wei Wang,Shuling Chen,Xiaofang He,Haibo Wang,Sui Peng,Zebin Chen,Mimi Tang,Zhihang Chen,Yang Hou,Zhenwei Peng,Ming Kuang
出处
期刊:European Radiology [Springer Science+Business Media]
卷期号:29 (9): 4648-4659 被引量:162
标识
DOI:10.1007/s00330-018-5935-8
摘要

Preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular cancer (HCC) is important for surgery strategy making. We aimed to develop and validate a combined intratumoural and peritumoural radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in primary HCC patients. This study included a training cohort of 110 HCC patients and a validating cohort of 50 HCC patients. All the patients underwent preoperative Gd-EOB-DTPA-enhanced MRI examination and curative hepatectomy. The volumes of interest (VOIs) around the hepatic lesions including intratumoural and peritumoural regions were manually delineated in the hepatobiliary phase of MRI images, from which quantitative features were extracted and analysed. In the training cohort, machine-learning method was applied for dimensionality reduction and selection of the extracted features. The proportion of MVI-positive patients was 38.2% and 40.0% in the training and validation cohort, respectively. Supervised machine learning selected ten features to establish a predictive model for MVI. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity of the combined intratumoural and peritumoural radiomics model in the training and validation cohort were 0.85 (95% confidence interval (CI), 0.77–0.93), 88.2%, 76.2%, and 0.83 (95% CI, 0.71–0.95), 90.0%, 75.0%, respectively. We evaluate quantitative Gd-EOB-DTPA-enhanced MRI image features of both intratumoural and peritumoural regions and provide an effective radiomics-based model for the prediction of MVI in HCC patients, and may therefore help clinicians make precise decisions regarding treatment before the surgery. • An effective radiomics model for prediction of microvascular invasion in HCC patients is established. • The radiomics model is superior to the radiologist in prediction of MVI. • The radiomics model can help clinicians in pretreatment decision making.
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