Zheng Xin, Xu Yun-Jun, Huang Jingcheng, Cai Shengxian, Wang Wanwan
Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Department of Radiology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, China.
Med Phys. 2023 Oct;50(10):6079-6095. doi: 10.1002/mp.16597. Epub 2023 Jul 30.
Microvascular invasion (MVI) is a major risk factor, for recurrence and metastasis of hepatocellular carcinoma (HCC) after radical surgery and liver transplantation. However, its diagnosis depends on the pathological examination of the resected specimen after surgery; therefore, predicting MVI before surgery is necessary to provide reference value for clinical treatment. Meanwhile, predicting only the existence of MVI is not enough, as it ignores the degree, quantity, and distribution of MVI and may lead to MVI-positive patients suffering due to inappropriate treatment. Although some studies have involved M2 (high risk of MVI), majority have adopted the binary classification method or have not included radiomics.
To develop three-class classification models for predicting the grade of MVI of HCC by combining enhanced computed tomography radiomics features with clinical risk factors.
The data of 166 patients with HCC confirmed by surgery and pathology were analyzed retrospectively. The patients were divided into the training (116 cases) and test (50 cases) groups at a ratio of 7:3. Of them, 69 cases were MVI positive in the training group, including 45 cases in the low-risk group (M1) and 24 cases in the high-risk group (M2), and 47 cases were MVI negative (M0). In the training group, the optimal subset features were obtained through feature selection, and the arterial phase radiomics model, portal venous phase radiomics model, delayed phase radiomics model, three-phase radiomics model, clinical imaging model, and combined model were developed using Linear Support Vector Classification. The test group was used for validation, and the efficacy of each model was evaluated through the receiver operating characteristic curve (ROC).
The clinical imaging features of MVI included alpha-fetoprotein, tumor size, tumor margin, peritumoral enhancement, intratumoral artery, and low-density halo. The area under the curve (AUC) of the ROC values of the clinical imaging model for M0, M1, and M2 were 0.831, 0.701, and 0.847, respectively, in the training group and 0.782, 0.534, and 0.785, respectively, in the test group. After combined radiomics analyis, the AUC values for M0, M1, and M2 in the test group were 0.818, 0.688, and 0.867, respectively. The difference between the clinical imaging model and the combined model was statistically significant (p = 0.029).
The clinical imaging model and radiomics model developed in this study had a specific predictive value for HCC MVI grading, which can provide precise reference value for preoperative clinical diagnosis and treatment. The combined application of the two models had a high predictive efficacy.
微血管侵犯（MVI）是肝细胞癌（HCC）根治性手术和肝移植术后复发和转移的主要危险因素。然而，其诊断依赖于术后切除标本的病理检查；因此，术前预测MVI对于为临床治疗提供参考价值是必要的。同时，仅预测MVI的存在是不够的，因为它忽略了MVI的程度、数量和分布，可能导致MVI阳性患者因治疗不当而遭受痛苦。尽管一些研究涉及M2（MVI高风险），但大多数采用二元分类方法或未纳入放射组学。
通过将增强计算机断层扫描放射组学特征与临床危险因素相结合，建立预测HCC微血管侵犯分级的三类分类模型。
回顾性分析166例经手术和病理证实的HCC患者的数据。患者按7:3的比例分为训练组（116例）和测试组（50例）。其中，训练组69例MVI阳性，包括低风险组（M1）45例和高风险组（M2）24例，47例MVI阴性（M0）。在训练组中，通过特征选择获得最佳子集特征，并使用线性支持向量分类法建立动脉期放射组学模型、门静脉期放射组学模型、延迟期放射组学模型、三期放射组学模型、临床影像模型和联合模型。测试组用于验证，并通过受试者工作特征曲线（ROC）评估各模型的效能。
MVI的临床影像特征包括甲胎蛋白、肿瘤大小、肿瘤边缘、瘤周强化、瘤内动脉和低密度晕。训练组中，M0、M1和M2的临床影像模型ROC值的曲线下面积（AUC）分别为0.831、0.701和0.847，测试组分别为0.782、0.534和0.785。经过联合放射组学分析，测试组中M0、M1和M2的AUC值分别为0.818、0.688和0.867。临床影像模型与联合模型之间的差异具有统计学意义（p = 0.029）。
本研究建立的临床影像模型和放射组学模型对HCC MVI分级具有一定的预测价值，可为术前临床诊断和治疗提供精确的参考价值。两种模型联合应用具有较高的预测效能。
