Zeng Yuli, Wu Huiqin, Zhu Yanqiu, Li Chao, Du Dongyang, Song Yang, Su Sulian, Qin Jie, Jiang Guihua
Department of Medical Imaging, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, Guangdong, China.
Department of Radiology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Front Oncol. 2025 Mar 3;15:1510071. doi: 10.3389/fonc.2025.1510071. eCollection 2025.
To investigate the predictive value of radiomics models based on intra-tumoral ecological diversity (iTED) and temporal characteristics for assessing microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).
We retrospectively analyzed the data of 398 HCC patients who underwent dynamic contrast-enhanced MRI with Gd-EOB-DTPA (training set: 318; testing set: 80). The tumors were segmented into five distinct habitats using case-level clustering and a Gaussian mixture model was used to determine the optimal clusters based on the Bayesian information criterion to produce an iTED feature vector for each patient, which was used to assess intra-tumoral heterogeneity. Radiomics models were developed using iTED features from the arterial phase (AP), portal venous phase (PVP), and hepatobiliary phase (HBP), referred to as M, M, and M, respectively. Additionally, temporal features were derived by subtracting the PVP features from the AP features, creating a delta-radiomics model (M). Conventional radiomics features were also extracted from the AP, PVP, and HBP images, resulting in three models: M, M, and M. A clinical-radiological model (CR model) was constructed, and two fusion models were generated by combining the radiomics or/and CR models using a stacking algorithm (fusion_R and fusion_CR). Model performance was evaluated using AUC, accuracy, sensitivity, and specificity.
The M model demonstrated higher sensitivity compared to the M and M models. No significant differences in performance were observed across different imaging phases for either conventional radiomics ( = 0.096-0.420) or iTED features ( = 0.106-0.744). Similarly, for images from the same phase, we found no significant differences between the performance of conventional radiomics and iTED features (AP: = 0.158; PVP: = 0.844; HBP: = 0.157). The fusion_R and fusion_CR models enhanced MVI discrimination, achieving AUCs of 0.823 (95% CI: 0.816-0.831) and 0.830 (95% CI: 0.824-0.835), respectively.
Delta radiomics features are temporal and predictive of MVI, providing additional predictive information for MVI beyond conventional AP and PVP features. The iTED features provide an alternative perspective in interpreting tumor characteristics and hold the potential to replace conventional radiomics features to some extent for MVI prediction.
探讨基于瘤内生态多样性（iTED）和时间特征的放射组学模型对肝细胞癌（HCC）患者微血管侵犯（MVI）的预测价值。
我们回顾性分析了398例接受钆塞酸二钠（Gd-EOB-DTPA）动态对比增强MRI检查的HCC患者的数据（训练集：318例；测试集：80例）。使用病例级聚类将肿瘤分割为五个不同的区域，并使用高斯混合模型根据贝叶斯信息准则确定最佳聚类，为每位患者生成一个iTED特征向量，用于评估瘤内异质性。利用动脉期（AP）、门静脉期（PVP）和肝胆期（HBP）的iTED特征分别构建放射组学模型，分别称为M、M和M。此外，通过从AP特征中减去PVP特征得出时间特征，创建一个增量放射组学模型（M）。还从AP、PVP和HBP图像中提取传统放射组学特征，得到三个模型：M、M和M。构建了一个临床-放射学模型（CR模型），并使用堆叠算法将放射组学模型或/和CR模型组合生成两个融合模型（fusion_R和fusion_CR）。使用AUC、准确性、敏感性和特异性评估模型性能。
与M和M模型相比，M模型显示出更高的敏感性。对于传统放射组学（ = 0.096 - 0.420）或iTED特征（ = 0.106 - 0.744），在不同成像期的性能均未观察到显著差异。同样，对于同一期的图像，我们发现传统放射组学和iTED特征的性能之间没有显著差异（AP： = 0.158；PVP： = 0.844；HBP： = 0.157）。融合_R和融合_CR模型增强了对MVI的鉴别能力，AUC分别达到0.823（95% CI：0.816 - 0.831）和0.830（95% CI：0.824 - 0.835）。
增量放射组学特征具有时间性且可预测MVI，为MVI提供了超越传统AP和PVP特征的额外预测信息。iTED特征为解释肿瘤特征提供了一个替代视角，并在一定程度上有潜力取代传统放射组学特征用于MVI预测。
