Zhu Zhu, Wu Kaiying, Lu Jian, Dai Sunxian, Xu Dabo, Fang Wei, Yu Yixing, Gu Wenhao
Department of Radiology, The First People's Hospital of Taicang, Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215400, China.
Department of Radiology, The Third Affiliated Hospital of Nantong University, The Third People's Hospital of Nantong, Nantong, Jiangsu, 226000, China.
BMC Med Imaging. 2025 Mar 31;25(1):105. doi: 10.1186/s12880-025-01646-9.
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.
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.
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).
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.
Not applicable.
微血管侵犯（MVI）是肝细胞癌（HCC）术后早期复发的重要危险因素。基于钆塞酸二钠（Gd-EOB-DTPA）增强磁共振成像（MRI）图像，我们开发了一种新型的放射组学模型。它结合了双区域特征和两种机器学习算法。本研究的目的是验证其对MVI术前预测的潜在价值。
这项回顾性研究纳入了来自三家医院的304例HCC患者（训练队列216例，测试队列88例）。在动脉期、门静脉期和肝胆期图像中勾勒出肿瘤内和肿瘤周围的感兴趣体积。分别基于FeAture Explorer软件和3D ResNet-18提取器提取传统放射组学（CR）和深度学习放射组学（DLR）特征。通过单因素和多因素分析选择临床变量。使用支持向量机建立临床、CR、DLR、CR-DLR和临床放射组学（Clin-R）模型。通过受试者操作特征曲线下面积（AUC）、准确性、敏感性和特异性评估模型的预测能力。
双区域CR-DLR模型比单区域模型或单机学习模型有更多优势且预测性能更好。其在训练队列中的AUC、准确性、敏感性和特异性分别为0.844、76.9%、87.8%和69.1%，在测试队列中分别为0.740、73.9%、50%和84.5%。甲胎蛋白（比值比为0.32）和最大肿瘤直径（比值比为1.270）是独立预测因素。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无显著差异（P>0.005）。
基于Gd-EOB-DTPA增强MRI图像，我们致力于开发一种结合双区域特征和两种机器学习算法（CR和DLR）的放射组学模型。新模型的应用将为医学影像提供更准确、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，从而改善患者预后。
不适用。
