Guixue Liu PhD, Zhehan Shen MD, Huanhuan Chong PhD, Jiahao Zhou MD, Tianyi Zhang MD, Yikun Wang MD, Di Ma PhD, Yuchen Yang PhD, Yongjun Chen PhD, Huafeng Wang PhD, Ingolf Sack PhD, Jing Guo PhD, Ruokun Li PhD, Fuhua Yan PhD
{"title":"Three-Dimensional Multifrequency MR Elastography for Microvascular Invasion and Prognosis Assessment in Hepatocellular Carcinoma","authors":"Guixue Liu PhD, Zhehan Shen MD, Huanhuan Chong PhD, Jiahao Zhou MD, Tianyi Zhang MD, Yikun Wang MD, Di Ma PhD, Yuchen Yang PhD, Yongjun Chen PhD, Huafeng Wang PhD, Ingolf Sack PhD, Jing Guo PhD, Ruokun Li PhD, Fuhua Yan PhD","doi":"10.1002/jmri.29276","DOIUrl":null,"url":null,"abstract":"<div>\n \n <section>\n \n <h3> Background</h3>\n \n <p>Pretreatment identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is important when selecting treatment strategies.</p>\n </section>\n \n <section>\n \n <h3> Purpose</h3>\n \n <p>To improve models for predicting MVI and recurrence-free survival (RFS) by developing nomograms containing three-dimensional (3D) MR elastography (MRE).</p>\n </section>\n \n <section>\n \n <h3> Study Type</h3>\n \n <p>Prospective.</p>\n </section>\n \n <section>\n \n <h3> Population</h3>\n \n <p>188 patients with HCC, divided into a training cohort (<i>n</i> = 150) and a validation cohort (<i>n</i> = 38). In the training cohort, 106/150 patients completed a 2-year follow-up.</p>\n </section>\n \n <section>\n \n <h3> Field Strength/Sequence</h3>\n \n <p>1.5T 3D multifrequency MRE with a single-shot spin-echo echo planar imaging sequence, and 3.0T multiparametric MRI (mp-MRI), consisting of diffusion-weighted echo planar imaging, T2-weighted fast spin echo, in-phase out-of-phase T1-weighted fast spoiled gradient-recalled dual-echo and dynamic contrast-enhanced gradient echo sequences.</p>\n </section>\n \n <section>\n \n <h3> Assessment</h3>\n \n <p>Multivariable analysis was used to identify the independent predictors for MVI and RFS. Nomograms were constructed for visualization. Models for predicting MVI and RFS were built using mp-MRI parameters and a combination of mp-MRI and 3D MRE predictors.</p>\n </section>\n \n <section>\n \n <h3> Statistical Tests</h3>\n \n <p>Student's <i>t</i>-test, Mann–Whitney U test, chi-squared or Fisher's exact tests, multivariable analysis, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan–Meier analysis and log rank tests. <i>P</i> < 0.05 was considered significant.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Tumor <i>c</i> and liver <i>c</i> were independent predictors of MVI and RFS, respectively. Adding tumor <i>c</i> significantly improved the diagnostic performance of mp-MRI (AUC increased from 0.70 to 0.87) for MVI detection. Of the 106 patients in the training cohort who completed the 2-year follow up, 34 experienced recurrence. RFS was shorter for patients with MVI-positive histology than MVI-negative histology (27.1 months vs. >40 months). The MVI predicted by the 3D MRE model yielded similar results (26.9 months vs. >40 months). The MVI and RFS nomograms of the histologic-MVI and model-predicted MVI-positive showed good predictive performance.</p>\n </section>\n \n <section>\n \n <h3> Data Conclusion</h3>\n \n <p>Biomechanical properties of 3D MRE were biomarkers for MVI and RFS. MVI and RFS nomograms were established.</p>\n </section>\n \n <section>\n \n <h3> Level of Evidence</h3>\n \n <p>2</p>\n </section>\n \n <section>\n \n <h3> Technical Efficacy</h3>\n \n <p>Stage 2</p>\n </section>\n </div>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmri.29276","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jmri.29276","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Pretreatment identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is important when selecting treatment strategies.
Purpose
To improve models for predicting MVI and recurrence-free survival (RFS) by developing nomograms containing three-dimensional (3D) MR elastography (MRE).
Study Type
Prospective.
Population
188 patients with HCC, divided into a training cohort (n = 150) and a validation cohort (n = 38). In the training cohort, 106/150 patients completed a 2-year follow-up.
Field Strength/Sequence
1.5T 3D multifrequency MRE with a single-shot spin-echo echo planar imaging sequence, and 3.0T multiparametric MRI (mp-MRI), consisting of diffusion-weighted echo planar imaging, T2-weighted fast spin echo, in-phase out-of-phase T1-weighted fast spoiled gradient-recalled dual-echo and dynamic contrast-enhanced gradient echo sequences.
Assessment
Multivariable analysis was used to identify the independent predictors for MVI and RFS. Nomograms were constructed for visualization. Models for predicting MVI and RFS were built using mp-MRI parameters and a combination of mp-MRI and 3D MRE predictors.
Statistical Tests
Student's t-test, Mann–Whitney U test, chi-squared or Fisher's exact tests, multivariable analysis, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan–Meier analysis and log rank tests. P < 0.05 was considered significant.
Results
Tumor c and liver c were independent predictors of MVI and RFS, respectively. Adding tumor c significantly improved the diagnostic performance of mp-MRI (AUC increased from 0.70 to 0.87) for MVI detection. Of the 106 patients in the training cohort who completed the 2-year follow up, 34 experienced recurrence. RFS was shorter for patients with MVI-positive histology than MVI-negative histology (27.1 months vs. >40 months). The MVI predicted by the 3D MRE model yielded similar results (26.9 months vs. >40 months). The MVI and RFS nomograms of the histologic-MVI and model-predicted MVI-positive showed good predictive performance.
Data Conclusion
Biomechanical properties of 3D MRE were biomarkers for MVI and RFS. MVI and RFS nomograms were established.