Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China.
Department of Liver Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China.
Abdom Radiol (NY). 2021 Aug;46(8):3845-3854. doi: 10.1007/s00261-021-03034-7. Epub 2021 Mar 18.
To develop a prediction model that combined magnetic resonance images (MRI)-based radiomics features with clinical factors to predict recurrence-free survival (RFS) of hepatocellular carcinoma (HCC) patients treated with surgical resection.
HCC patients treated with surgical resection (n = 153) were randomly divided into training (n = 107) and validation (n = 46) datasets. The volumes of interest were manually outlined around the lesion and additional 2 mm and 5 mm peritumoral areas were created with automated dilatation in MRI to extract tumoral (T) and peritumoral (PT) radiomics features. The radiomics models were constructed using least absolute shrinkage and selection operator Cox regression. The combined model incorporated clinical factors and radiomics features using multivariable Cox regression based on the Akaike information criterion principle. Predictive performance of different models were evaluated by receiver operating characteristic (ROC) curves, decision curves, and calibration curves.
Among the radiomics models, similar performance was observed in the 2 mm and 5 mm PT models (C-index both 0.657), which were better than the T model or T + PT model (C-index 0.607 and 0.641, respectively) in the validation dataset, whereas the model combined with the three identified clinical risk factors showed the best performance (C-index 0.725). Results of the ROC curves, decision curves, and the calibration curves indicated that the combined model and the derived nomogram had better prediction performance, greater clinical benefits, and fair calibration efficiency.
The prediction model that combined MRI radiomics signatures with clinical factors can effectively predict the prognosis of patients with HCC treated with surgical resection.
开发一种联合基于磁共振成像（MRI）的放射组学特征与临床因素的预测模型，以预测接受手术切除治疗的肝细胞癌（HCC）患者的无复发生存（RFS）。
接受手术切除治疗的 HCC 患者（n=153）被随机分为训练集（n=107）和验证集（n=46）。手动围绕病灶勾画感兴趣区，并在 MRI 中使用自动扩张创建病灶（T）和瘤周（PT）额外的 2mm 和 5mm 区域，以提取肿瘤（T）和瘤周（PT）放射组学特征。使用最小绝对收缩和选择算子 Cox 回归构建放射组学模型。基于赤池信息量准则原理，使用多变量 Cox 回归将临床因素和放射组学特征纳入联合模型。通过接受者操作特征（ROC）曲线、决策曲线和校准曲线评估不同模型的预测性能。
在放射组学模型中，2mm 和 5mm PT 模型的性能相似（C 指数均为 0.657），在验证集中优于 T 模型或 T+PT 模型（C 指数分别为 0.607 和 0.641），而联合三个确定的临床危险因素的模型显示出最佳性能（C 指数为 0.725）。ROC 曲线、决策曲线和校准曲线的结果表明，联合模型和衍生的列线图具有更好的预测性能、更大的临床获益和良好的校准效率。
联合 MRI 放射组学特征与临床因素的预测模型可以有效预测接受手术切除治疗的 HCC 患者的预后。
