Gu Jingxiao, Bao Shanlei, Akemuhan Reaoxian, Jia Zhongzheng, Zhang Yu, Huang Chen
Department of Vascular Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, the, People's Republic of China.
Department of Radiology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China.
Mol Imaging Biol. 2023 Dec;25(6):1073-1083. doi: 10.1007/s11307-023-01870-1. Epub 2023 Nov 6.
The purpose of our project was to investigate the effectiveness of radiomic features based on contrast-enhanced computed tomography (CT) that can detect the expression of c-Met in hepatocellular carcinoma (HCC) and to validate its efficacy in predicting the outcome of sorafenib resistance.
In total, 130 patients (median age, 60 years) with pathologically confirmed HCC who underwent contrast material-enhanced CT from October 2012 to July 2020 were randomly divided into a training set (n = 91) and a test set (n = 39). Radiomic features were extracted from arterial phase (AP), portal venous phase (VP) and delayed phase (DP) images of every participant's enhanced CT images.
The entire group comprised 39 Met-positive and 91 Met-negative patients. The combined model, which included the clinical factors and the radiomic features, performed well in the training (area under the curve [AUC] = 0.878) and validation (AUC = 0.851) cohorts. The nomogram, which relied on the combined model, fits well in the calibration curves. Decision curve analysis (DCA) further confirmed that the clinical valuation of the nomogram achieved comparable accuracy in c-Met prediction. Among another 20 patients with HCC who had received sorafenib, the predicted high-risk group had shorter overall survival (OS) than the predicted low-risk group (p < 0.05).
A multivariate model acquired from three phases (AP, VP and DP) of enhanced CT, HBV-DNA and γ glutamyl transpeptidase isoenzyme II (GGT-II) could be considered a satisfactory preoperative marker of the expression of c-Met in patients with HCC. This approach may help in overcoming sorafenib resistance in advanced HCC.
我们项目的目的是研究基于对比增强计算机断层扫描（CT）的放射组学特征检测肝细胞癌（HCC）中c-Met表达的有效性，并验证其预测索拉非尼耐药结果的功效。
2012年10月至2020年7月期间，130例经病理证实的HCC患者（中位年龄60岁）接受了对比剂增强CT检查，这些患者被随机分为训练组（n = 91）和测试组（n = 39）。从每位参与者增强CT图像的动脉期（AP）、门静脉期（VP）和延迟期（DP）图像中提取放射组学特征。
整个组包括39例Met阳性和91例Met阴性患者。包含临床因素和放射组学特征的联合模型在训练队列（曲线下面积[AUC]=0.878）和验证队列（AUC = 0.851）中表现良好。依赖联合模型的列线图在校准曲线中拟合良好。决策曲线分析（DCA）进一步证实，列线图的临床评估在c-Met预测中达到了相当的准确性。在另外20例接受索拉非尼治疗的HCC患者中，预测的高危组总生存期（OS）比预测的低危组短（p<0.05）。
从增强CT的三个期（AP、VP和DP）、乙肝病毒脱氧核糖核酸（HBV-DNA）和γ-谷氨酰转肽酶同工酶II（GGT-II）获得的多变量模型可被视为HCC患者术前c-Met表达的满意标志物。这种方法可能有助于克服晚期HCC的索拉非尼耐药性。
