- Book学术

发布求助

文献互助智能选刊最新文献

IF 2.9 3区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

BMC Medical Imaging Pub Date : 2025-03-31 DOI:10.1186/s12880-025-01646-9

Zhu Zhu, Kaiying Wu, Jian Lu, Sunxian Dai, Dabo Xu, Wei Fang, Yixing Yu, Wenhao Gu

{"title":"Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study.","authors":"Zhu Zhu, Kaiying Wu, Jian Lu, Sunxian Dai, Dabo Xu, Wei Fang, Yixing Yu, Wenhao Gu","doi":"10.1186/s12880-025-01646-9","DOIUrl":null,"url":null,"abstract":"Background: Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) images, we developed a novel radiomics model. It combined bi-regional features and two machine learning algorithms. The aim of this study was to validate its potential value for preoperative prediction of MVI.Methods: This retrospective study included 304 HCC patients (training cohort, 216 patients; testing cohort, 88 patients) from three hospitals. Intratumoral and peritumoral volumes of interest were delineated in arterial phase, portal venous phase, and hepatobiliary phase images. Conventional radiomics (CR) and deep learning radiomics (DLR) features were extracted based on FeAture Explorer software and the 3D ResNet-18 extractor, respectively. Clinical variables were selected using univariate and multivariate analyses. Clinical, CR, DLR, CR-DLR, and clinical-radiomics (Clin-R) models were built using support vector machines. The predictive capacity of the models was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.Results: The bi-regional CR-DLR model showed more gains and gave better predictive performance than the single-regional models or single-machine learning models. Its AUC, accuracy, sensitivity, and specificity were 0.844, 76.9%, 87.8%, and 69.1% in the training cohort and 0.740, 73.9%, 50%, and 84.5% in the testing cohort. Alpha-fetoprotein (odds ratio was 0.32) and maximum tumor diameter (odds ratio was 1.270) were independent predictors. The AUCs of the clinical model and the Clin-R model were 0.655 and 0.672, respectively. There was no significant difference in the AUCs between all the models (P > 0.005).Conclusion: Based on Gd-EOB-DTPA-enhanced MRI images, we focused on developing a radiomics model that combines bi-regional features and two machine learning algorithms (CR and DLR). The application of the new model will provide a more accurate and non-invasive diagnostic solution for medical imaging. It will provide valuable information for clinical personalized treatment, thereby improving patient prognosis.Clinical trial number: Not applicable.","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"105"},"PeriodicalIF":2.9000,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956329/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12880-025-01646-9","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

摘要

背景：微血管侵犯（MVI）是肝细胞癌（HCC）术后早期复发的重要风险因素。基于钆-乙氧基苄基-二乙烯三胺五乙酸（Gd-EOB-DTPA）增强磁共振成像（MRI）图像，我们开发了一种新型放射组学模型。它结合了双区域特征和两种机器学习算法。本研究旨在验证该模型在术前预测 MVI 方面的潜在价值：这项回顾性研究纳入了来自三家医院的 304 名 HCC 患者（训练队列，216 名患者；测试队列，88 名患者）。在动脉相、门静脉相和肝胆相图像中划定了感兴趣的瘤内和瘤周体积。传统放射组学（CR）和深度学习放射组学（DLR）特征分别基于FeAture Explorer软件和3D ResNet-18提取器提取。通过单变量和多变量分析选择临床变量。使用支持向量机建立了临床、CR、DLR、CR-DLR 和临床放射组学（Clin-R）模型。模型的预测能力通过接收者操作特征曲线下面积（AUC）、准确性、灵敏度和特异性进行评估：结果：与单区域模型或单机学习模型相比，双区域 CR-DLR 模型收益更大，预测性能更好。在训练队列中，其AUC、准确性、灵敏度和特异性分别为0.844、76.9%、87.8%和69.1%；在测试队列中，其AUC、准确性、灵敏度和特异性分别为0.740、73.9%、50%和84.5%。甲胎蛋白（几率比为 0.32）和肿瘤最大直径（几率比为 1.270）是独立的预测因素。临床模型和 Clin-R 模型的 AUC 分别为 0.655 和 0.672。所有模型之间的AUC无明显差异（P > 0.005）：基于 Gd-EOB-DTPA 增强 MRI 图像，我们重点开发了一种放射组学模型，该模型结合了双区域特征和两种机器学习算法（CR 和 DLR）。新模型的应用将为医学成像提供更准确、无创的诊断解决方案。它将为临床个性化治疗提供有价值的信息，从而改善患者的预后：不适用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study.

Background: Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) images, we developed a novel radiomics model. It combined bi-regional features and two machine learning algorithms. The aim of this study was to validate its potential value for preoperative prediction of MVI.

Methods: This retrospective study included 304 HCC patients (training cohort, 216 patients; testing cohort, 88 patients) from three hospitals. Intratumoral and peritumoral volumes of interest were delineated in arterial phase, portal venous phase, and hepatobiliary phase images. Conventional radiomics (CR) and deep learning radiomics (DLR) features were extracted based on FeAture Explorer software and the 3D ResNet-18 extractor, respectively. Clinical variables were selected using univariate and multivariate analyses. Clinical, CR, DLR, CR-DLR, and clinical-radiomics (Clin-R) models were built using support vector machines. The predictive capacity of the models was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results: The bi-regional CR-DLR model showed more gains and gave better predictive performance than the single-regional models or single-machine learning models. Its AUC, accuracy, sensitivity, and specificity were 0.844, 76.9%, 87.8%, and 69.1% in the training cohort and 0.740, 73.9%, 50%, and 84.5% in the testing cohort. Alpha-fetoprotein (odds ratio was 0.32) and maximum tumor diameter (odds ratio was 1.270) were independent predictors. The AUCs of the clinical model and the Clin-R model were 0.655 and 0.672, respectively. There was no significant difference in the AUCs between all the models (P > 0.005).

Conclusion: Based on Gd-EOB-DTPA-enhanced MRI images, we focused on developing a radiomics model that combines bi-regional features and two machine learning algorithms (CR and DLR). The application of the new model will provide a more accurate and non-invasive diagnostic solution for medical imaging. It will provide valuable information for clinical personalized treatment, thereby improving patient prognosis.

Clinical trial number: Not applicable.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.60

自引率

3.70%

发文量

198

审稿时长

27 weeks

期刊介绍： BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.