Abstract：
OBJECTIVE: To develop and validate a clinical-radiomics model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of Vessels encapsulating tumor clusters (VETC)- microvascular invasion (MVI) and prognosis of hepatocellular carcinoma (HCC).
METHODS: 219 HCC patients from Institution 1 were split into internal training and validation groups, with 101 patients from Institution 2 assigned to external validation. Histologically confirmed VETC-MVI pattern categorizing HCC into VM-HCC+ (VETC+/MVI+, VETC-/MVI+, VETC+/MVI-) and VM-HCC- (VETC-/MVI-). The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI. Six radiomics models (intratumor and peritumor in AP, PP, and DP of DCE-MRI) and one clinical model were developed for assessing VM-HCC. Establishing intra-tumoral and peri-tumoral models through combining intratumor and peritumor features. The best-performing radiomics model and the clinical model were then integrated to create a Combined model.
RESULTS: In institution 1, pathological VM-HCC+ were confirmed in 88 patients (training set: 61, validation set: 27). In internal testing, the Combined model had an AUC of 0.85 (95% CI: 0.76-0.93), which reached an AUC of 0.75 (95% CI: 0.66-0.85) in external validation. The model\'s predictions were associated with early recurrence and progression-free survival in HCC patients.
CONCLUSIONS: The clinical-radiomics model offers a non-invasive approach to discern VM-HCC and predict HCC patients\' prognosis preoperatively, which could offer clinicians valuable insights during the decision-making phase.
METHODS: 219 HCC patients from Institution 1 were split into internal training and validation groups, with 101 patients from Institution 2 assigned to external validation. Histologically confirmed VETC-MVI pattern categorizing HCC into VM-HCC+ (VETC+/MVI+, VETC-/MVI+, VETC+/MVI-) and VM-HCC- (VETC-/MVI-). The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI. Six radiomics models (intratumor and peritumor in AP, PP, and DP of DCE-MRI) and one clinical model were developed for assessing VM-HCC. Establishing intra-tumoral and peri-tumoral models through combining intratumor and peritumor features. The best-performing radiomics model and the clinical model were then integrated to create a Combined model.
RESULTS: In institution 1, pathological VM-HCC+ were confirmed in 88 patients (training set: 61, validation set: 27). In internal testing, the Combined model had an AUC of 0.85 (95% CI: 0.76-0.93), which reached an AUC of 0.75 (95% CI: 0.66-0.85) in external validation. The model\'s predictions were associated with early recurrence and progression-free survival in HCC patients.
CONCLUSIONS: The clinical-radiomics model offers a non-invasive approach to discern VM-HCC and predict HCC patients\' prognosis preoperatively, which could offer clinicians valuable insights during the decision-making phase.
摘要：
目的：开发并验证动态对比增强MRI（DCE-MRI）的临床影像组学模型，用于术前区分包裹血管的肿瘤簇（VETC）-微血管侵犯（MVI）和预后肝细胞癌（HCC）。
方法：将来自机构1的219名HCC患者分为内部培训和验证组，来自机构2的101名患者被分配到外部验证。组织学证实的VETC-MVI模式将HCC分类为VM-HCC+(VETC+/MVI+，VETC-/MVI+,VETC+/MVI-)和VM-HC-(VETC-/MVI-)。在动脉中手动分割肿瘤内和瘤周区域，门静脉和延迟期(AP，PP,和DP,分别)的DCE-MRI。六个影像组学模型(AP的瘤内和瘤周，PP,和DCE-MRI的DP）和一种临床模型用于评估VM-HCC。通过结合瘤内和瘤周特征建立瘤内和瘤周模型。然后将表现最佳的放射组学模型和临床模型整合以创建组合模型。
结果：在机构1中，在88例患者中证实了病理性VM-HCC（训练集：61，验证集：27）。在内部测试中,联合模型的AUC为0.85（95%CI：0.76-0.93），在外部验证中达到0.75的AUC（95%CI：0.66-0.85）。该模型的预测与HCC患者的早期复发和无进展生存期相关。
结论：临床影像组学模型提供了一种非侵入性方法来辨别VM-HCC并预测HCC患者术前预后，这可以在决策阶段为临床医生提供有价值的见解。
方法：将来自机构1的219名HCC患者分为内部培训和验证组，来自机构2的101名患者被分配到外部验证。组织学证实的VETC-MVI模式将HCC分类为VM-HCC+(VETC+/MVI+，VETC-/MVI+,VETC+/MVI-)和VM-HC-(VETC-/MVI-)。在动脉中手动分割肿瘤内和瘤周区域，门静脉和延迟期(AP，PP,和DP,分别)的DCE-MRI。六个影像组学模型(AP的瘤内和瘤周，PP,和DCE-MRI的DP）和一种临床模型用于评估VM-HCC。通过结合瘤内和瘤周特征建立瘤内和瘤周模型。然后将表现最佳的放射组学模型和临床模型整合以创建组合模型。
结果：在机构1中，在88例患者中证实了病理性VM-HCC（训练集：61，验证集：27）。在内部测试中,联合模型的AUC为0.85（95%CI：0.76-0.93），在外部验证中达到0.75的AUC（95%CI：0.66-0.85）。该模型的预测与HCC患者的早期复发和无进展生存期相关。
结论：临床影像组学模型提供了一种非侵入性方法来辨别VM-HCC并预测HCC患者术前预后，这可以在决策阶段为临床医生提供有价值的见解。
