First Central Clinical College of Tianjin Medical University, Tianjin 300192, PR China.
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, PR China.
Eur J Radiol. 2019 Aug;117:33-40. doi: 10.1016/j.ejrad.2019.05.010. Epub 2019 May 10.
To assess whether radiomics signature can identify aggressive behavior and predict recurrence of hepatocellular carcinoma (HCC) after liver transplantation.
Our study consisted of a training dataset (n = 93) and a validation dataset (40) with clinically confirmed HCC after liver transplantation from October 2011 to December 2016. Radiomics features were extracted by delineating regions-of-interest (ROIs) around the lesion in four phases of CT images. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The association between radiomics signature and recurrence-free survival (RFS) was assessed. Preoperative clinical characteristics potentially associated with RFS were evaluated to develop a clinical model. A combined model incorporating clinical risk factors and radiomics signature was built.
The stable radiomics features associated with the recurrence of HCC were simply found in arterial phase and portal phase. The prediction model based on the radiomics features extracted from the arterial phase showed better prediction performance than the portal vein phase or the fusion signature combining both of arterial and portal vein phase. A radiomics nomogram based on combined model consisting of the radiomics signature and clinical risk factors showed good predictive performance for RFS with a C-index of 0.785 (95% confidence interval [CI]: 0.674-0.895) in the training dataset and 0.789 (95% CI: 0.620-0.957) in the validation dataset. The calibration curves showed agreement in both training (p = 0.121) and validation cohorts (p = 0.164).
Radiomics signature extracted from CT images may be a potential imaging biomarker for liver cancer invasion and enable accurate prediction of HCC recurrence after liver transplantation.
评估影像组学特征能否识别侵袭性行为，并预测肝癌（HCC）患者肝移植术后复发。
本研究纳入了 2011 年 10 月至 2016 年 12 月期间经临床证实的肝移植术后 HCC 患者的训练数据集（n=93）和验证数据集（n=40）。通过勾画 CT 图像 4 个时相病灶的感兴趣区（ROI）提取影像组学特征。采用最小绝对收缩和选择算子（LASSO）Cox 回归模型生成影像组学特征。评估影像组学特征与无复发生存期（RFS）的相关性。评估与 RFS 相关的术前临床特征，以建立临床模型。构建包含临床危险因素和影像组学特征的联合模型。
与 HCC 复发相关的稳定影像组学特征仅在动脉期和门静脉期找到。基于动脉期提取的影像组学特征构建的预测模型比门静脉期或融合动脉期和门静脉期特征的融合模型具有更好的预测性能。基于联合模型（包括影像组学特征和临床危险因素）构建的影像组学列线图在训练数据集和验证数据集中预测 RFS 的 C 指数分别为 0.785（95%置信区间[CI]：0.674-0.895）和 0.789（95%CI：0.620-0.957），具有良好的预测性能。校准曲线在训练组（p=0.121）和验证组（p=0.164）中均显示出一致性。
从 CT 图像中提取的影像组学特征可能是肝癌侵袭的潜在影像学生物标志物，可准确预测肝癌患者肝移植术后的复发。
