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
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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":"{\"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). 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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. 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引用次数: 0
摘要
背景:目的:通过开发包含三维(3D)磁共振弹性成像(MRE)的提名图,改进预测肝细胞癌微血管侵犯(MVI)和无复发生存率(RFS)的模型:研究类型:前瞻性:188名HCC患者,分为训练队列(150人)和验证队列(38人)。在训练队列中,106/150 名患者完成了为期 2 年的随访:1.5T三维多频MRE采用单次自旋回波平面成像序列,3.0T多参数磁共振成像(mp-MRI)包括扩散加权回波平面成像、T2加权快速自旋回波、同相外T1加权快速破坏梯度回波双回波和动态对比增强梯度回波序列:采用多变量分析确定 MVI 和 RFS 的独立预测因素。构建了可视化提名图。使用 mp-MRI 参数以及 mp-MRI 和 3D MRE 预测因子的组合建立了 MVI 和 RFS 预测模型:学生 t 检验、曼-惠特尼 U 检验、卡方检验或费雪精确检验、多变量分析、接收者操作特征曲线下面积(AUC)、DeLong 检验、Kaplan-Meier 分析和对数秩检验。P 结果:肿瘤 c 和肝脏 c 分别是 MVI 和 RFS 的独立预测因子。添加肿瘤 c 能明显提高 mp-MRI 对 MVI 检测的诊断性能(AUC 从 0.70 提高到 0.87)。在完成2年随访的106名训练组患者中,有34人复发。MVI阳性组织学患者的RFS比MVI阴性组织学患者短(27.1个月对40个月)。三维 MRE 模型预测的 MVI 结果相似(26.9 个月 vs. >40 个月)。组织学MVI和模型预测的MVI阳性的MVI和RFS提名图显示出良好的预测性能:数据结论:三维 MRE 的生物力学特性是 MVI 和 RFS 的生物标志物。证据级别:2 技术效率:第 2 阶段。
Three-Dimensional Multifrequency MR Elastography for Microvascular Invasion and Prognosis Assessment in Hepatocellular Carcinoma
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.