UNASSIGNED:本研究旨在开发和验证基于gadobenate增强磁共振成像(MRI)的术前模型,用于预测大小≤5cm的肝细胞癌(HCC)患者的微血管侵犯(MVI)。为临床医生优化治疗方案提供术前指导。
UNASSIGNED:回顾性纳入2016年7月至2020年12月经病理证实的HCC和术前gadobenate增强MRI的164例患者。使用单变量和多变量逻辑回归(前向LR)分析来确定MVI的预测因子,并建立模型。采用四折交叉验证对模型进行验证,这是通过列线图可视化的。模型的预测性能进行了评价,基于判别,校准,和临床效用。
未经证实:甲胎蛋白升高(HR1.849,95%CI:1.193,2.867,P=0.006),非典型增强模式(HR3.441,95%CI:1.523,7.772,P=0.003),HBP的瘤周高血压(HR7.822,95%CI:3.317,18.445,P<0.001),HBP低血压(HR3.258,95%CI:1.381,7.687,P=0.007)是MVI的独立危险因素,构成HBP模型。平均曲线下面积(AUC),灵敏度,特异性,HBP模型的精度值如下:0.830(95%CI:0.784,0.876),训练集中为0.71,0.78,0.81;0.826(95%CI:0.765,0.887),测试集0.8、0.7、0.79。决策曲线分析(DCA)曲线显示HBP模型取得了较大的临床效益。
未经批准:总而言之,Gd-BOPTA增强MRI的HBP成像特征在预测HCC的MVI中起重要作用。术前模型,主要根据Gadobenate增强MRI的HBP成像特征,能够很好地预测≤5cm大小的HCC的MVI。该模型可以帮助临床医生术前评估HCC患者的MVI风险,从而指导临床医生优化治疗方案。
UNASSIGNED: The present study aimed to develop and validate a preoperative model based on gadobenate-enhanced magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) size of ≤5 cm. In order to provide preoperative guidance for clinicians to optimize treatment options.
UNASSIGNED: 164 patients with pathologically confirmed HCC and preoperative gadobenate-enhanced MRI from July 2016 to December 2020 were retrospectively included. Univariate and multivariate logistic regression (forward LR) analyses were used to determine the predictors of MVI and the model was established. Four-fold cross validation was used to verify the model, which was visualized by nomograms. The predictive performance of the model was evaluated based on discrimination, calibration, and clinical utility.
UNASSIGNED: Elevated alpha-fetoprotein (HR 1.849, 95% CI: 1.193, 2.867, P=0.006), atypical enhancement pattern (HR 3.441, 95% CI: 1.523, 7.772, P=0.003), peritumoral hypointensity on HBP (HR 7.822, 95% CI: 3.317, 18.445, P<0.001), and HBP hypointensity (HR 3.258, 95% CI: 1.381, 7.687, P=0.007) were independent risk factors to MVI and constituted the HBP model. The mean area under the curve (AUC), sensitivity, specificity, and accuracy values for the HBP model were as follows: 0.830 (95% CI: 0.784, 0.876), 0.71, 0.78, 0.81 in training set; 0.826 (95% CI:0.765, 0.887), 0.8, 0.7, 0.79 in test set. The decision curve analysis (DCA) curve showed that the HBP model achieved great clinical benefits.
UNASSIGNED: In conclusion, the HBP imaging features of Gd-BOPTA-enhanced MRI play an important role in predicting MVI for HCC. A preoperative model, mainly based on HBP imaging features of gadobenate-enhanced MRI, was able to excellently predict the MVI for HCC size of ≤5cm. The model may help clinicians preoperatively assess the risk of MVI in HCC patients so as to guide clinicians to optimize treatment options.