Sun Jin-Jun, Qian Xian-Ling, Shi Yi-Bing, Fu Yu-Fei, Yang Chun, Ma Xi-Juan
Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China.
Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Prz Gastroenterol. 2023;18(2):161-167. doi: 10.5114/pg.2022.116668. Epub 2022 Sep 7.
Clinical features and magnetic resonance imaging (MRI)-related data are commonly employed in clinical settings and can be used to predict the microvascular invasion (MVI) status of intrahepatic cholangiocarcinoma (ICC) patients.
To generate a clinical and MRI-based model capable of predicting the MVI status of ICC patients.
Consecutive ICC patients evaluated from June 2015 to December 2018 were retrospectively enrolled in a training group to establish a predictive clinical MRI model. Consecutive ICC patients evaluated from January 2019 to June 2019 were prospectively enrolled in a validation group to test the reliability of this model.
In total, 143 patients were enrolled in the training group, of whom 46 (32.2%) and 96 (67.8%) were MVI-positive and MVI-negative, respectively. Logistics analyses revealed larger tumour size ( = 0.008) and intrahepatic duct dilatation ( = 0.01) to be predictive of MVI positivity, enabling the establishment of the following predictive model: -2.468 + 0.024 × tumour size + 1.094 × intrahepatic duct dilatation. The area under the receiver operating characteristic (ROC) curve (AUC) for this model was 0.738 ( < 0.001). An optimal cut-off value of -1.0184 was selected to maximize sensitivity (71.7%) and specificity (61.9%). When the data from the validation group were incorporated into the predictive model, the AUC value was 0.716 ( = 0.009).
Both larger tumour size and intrahepatic duct dilatation were predictive of MVI positivity in patients diagnosed with ICC, and the predictive model developed based on these variables can offer quantitative guidance for assessing the risk of MVI.
临床特征和磁共振成像（MRI）相关数据在临床环境中普遍使用，可用于预测肝内胆管癌（ICC）患者的微血管侵犯（MVI）状态。
构建一个基于临床和MRI的模型，以预测ICC患者的MVI状态。
回顾性纳入2015年6月至2018年12月评估的连续ICC患者作为训练组，以建立预测性临床MRI模型。前瞻性纳入2019年1月至2019年6月评估的连续ICC患者作为验证组，以测试该模型的可靠性。
训练组共纳入143例患者，其中46例（32.2%）MVI阳性，96例（67.8%）MVI阴性。逻辑分析显示，肿瘤较大（P = 0.008）和肝内胆管扩张（P = 0.01）可预测MVI阳性，从而建立以下预测模型：-2.468 + 0.024×肿瘤大小 + 1.094×肝内胆管扩张。该模型的受试者工作特征（ROC）曲线下面积（AUC）为0.738（P < 0.001）。选择最佳截断值-1.0184以最大化敏感性（71.7%）和特异性（61.9%）。将验证组数据纳入预测模型时，AUC值为0.716（P = 0.009）。
肿瘤较大和肝内胆管扩张均能预测ICC患者的MVI阳性，基于这些变量建立的预测模型可为评估MVI风险提供定量指导。
