Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI
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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.
KeywordsHepatocellular cancer Radiomics Magnetic resonance imaging Gd-EOB-DTPA
- 3D VIBE
Three-dimensional volume interpolated breath-hold test
Akaike information criterion
Area under receiver operating characteristic curve
Fast low angle shot
Half-Fourier single-shot turbo spin-echo
Intra-class correlation coefficient
Least absolute shrinkage and selection operator
Magnetic resonance imaging
Negative predictive value
Positive predictive value
- Rad score
Region of interest
Volume of interest
This study was funded by the National Natural Science Foundation of China (81571750), National Natural Science Foundation of China (81771908), National Natural Science Foundation of China (81770608), Natural Science Foundation of Guangdong Province (2015A030311039), Guangzhou Science and Technology Program key projects (201704020215), and the Kelin Outstanding Young Scientist of the First Affiliated Hospital of Sun Yet-sen University (2017).
Compliance with ethical standards
The scientific guarantor of this publication is Ming Kuang.
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
Two of the authors (Qian Zhou and Bin Li) have significant statistical expertise.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• diagnostic or prognostic study
• performed at one institution
- 27.Lundsgaard Hansen M, Fallentin E, Axelsen T et al (2016) Interobserver and intraobserver reproducibility with volume dynamic contrast enhanced computed tomography (DCE-CT) in gastroesophageal junction cancer. Diagnostics (Basel). https://doi.org/10.3390/diagnostics6010008
- 28.Grossmann P, Stringfield O, El-Hachem N et al (2017) Defining the biological basis of radiomic phenotypes in lung cancer. Elife. https://doi.org/10.7554/eLife.23421