Peng Yue, Wu Songxiong, Xiong Bing, Chen Fuqiang, Zaki Nazar, Wu Ruodai, Qin Wenjian
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
ILIVER. 2025 Apr 26;4(2):100165. doi: 10.1016/j.iliver.2025.100165. eCollection 2025 Jun.
Accurate preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for treatment planning. This study aimed to develop and validate a multi-phase magnetic resonance imaging (MRI)-based radiomics model for predicting MVI in HCC patients.
This retrospective study included 110 HCC patients (training: = 77; validation: = 33) who underwent preoperative multi-phase MRI. Radiomics features were extracted from four MRI phases (non-contrast, arterial, portal, and hepatobiliary). Feature selection was performed using least absolute shrinkage and selection operator regression, and five machine learning classifiers were evaluated. Model performance was assessed using standard metrics including area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.
The four-phase radiomics model with logistic regression classifier showed optimal performance in both the training (AUC = 0.896; 95% confidence interval, 0.792-0.963) and validation cohorts (AUC = 0.889, 95% confidence interval, 0.781-0.982), outperforming the single-phase (AUC = 0.789), two-phase (AUC = 0.815), and three-phase models (AUC = 0.848) in the validation cohort. In the validation cohort, the model achieved balanced performance with sensitivity, specificity, accuracy, and precision all reaching 0.857.
The multi-phase MRI-based radiomics model significantly improves MVI prediction accuracy in HCC patients. This non-invasive approach could enhance preoperative assessment and treatment planning.
准确术前预测肝细胞癌（HCC）中的微血管侵犯（MVI）对于治疗方案规划至关重要。本研究旨在开发并验证一种基于多期磁共振成像（MRI）的放射组学模型，用于预测HCC患者的MVI。
这项回顾性研究纳入了110例接受术前多期MRI检查的HCC患者（训练组：n = 77；验证组：n = 33）。从四个MRI期相（平扫、动脉期、门脉期和肝胆期）提取放射组学特征。使用最小绝对收缩和选择算子回归进行特征选择，并评估了五个机器学习分类器。使用标准指标评估模型性能，包括受试者工作特征曲线下面积（AUC）、敏感性、特异性和准确性。
采用逻辑回归分类器的四期放射组学模型在训练组（AUC = 0.896；95%置信区间，0.792 - 0.963）和验证组（AUC = 0.889，95%置信区间，0.781 - 0.982）均表现出最佳性能，在验证组中优于单相模型（AUC = 0.789）、双相模型（AUC = 0.815）和三相模型（AUC = 0.848）。在验证组中，该模型实现了平衡性能，敏感性、特异性、准确性和精确性均达到0.857。
基于多期MRI的放射组学模型显著提高了HCC患者MVI预测的准确性。这种非侵入性方法可加强术前评估和治疗方案规划。
