Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
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DOI：
10.1016/j.ejrad.2019.05.010
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发表时间：
2019-08-01
影响因子：
3.3
通讯作者：
Zheng, Hong
中科院分区：
文献类型：
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作者：
Guo, Donghui;Gu, Dongsheng;Zheng, Hong
目的：评估放射组学特征是否能够识别肝移植后的侵袭性行为并预测复发。方法：我们的研究包括一个训练数据集(n=93)和一个验证数据集(40个)，其中包括2011年10月至2016年12月肝移植后临床确诊的肝细胞癌。通过在CT图像的四个阶段中描绘病变周围的感兴趣区域(ROI)来提取放射组学特征。使用最小绝对收缩和选择算子(LASSO)COX回归模型生成放射组学签名。评估放射组学特征与无复发生存率(RFS)之间的关系。对可能与RFS相关的术前临床特征进行评估，以建立临床模型。建立临床危险因素与放射组学特征相结合的模型。结果：动脉期和门脉期表现出与肝癌复发相关的稳定的放射组学特征。基于动脉相放射组学特征的预测模型比门静脉期或结合动脉和门静脉期的融合信号具有更好的预测性能。基于放射组学特征和临床危险因素的组合模型的放射组学诺模图对RFS具有良好的预测性能，在训练数据集中的C指数为0.785(95%可信区间：0.674-0.895)，在验证数据集中的C指数为0.789(95%CI：0.620-0.957)。校正曲线在训练(p=0.121)和验证队列(p=0.164)中都显示出一致性。结论：从CT图像中提取的放射组学特征可能是一个潜在的肝癌侵袭的成像生物标志物，并且能够准确地预测肝移植后的肝细胞癌复发。
Objectives: To assess whether radiomics signature can identify aggressive behavior and predict recurrence of hepatocellular carcinoma (HCC) after liver transplantation.Methods: 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.Results: 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).Conclusions: 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.