Prediction of Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization: A Real-World Study Based on Non-Contrast Computed Tomography Radiomics and General Image Features.
Prediction of Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization: A Real-World Study Based on Non-Contrast Computed Tomography Radiomics and General Image Features.
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肝细胞癌对经导管动脉化疗栓塞反应的预测：基于非对比计算机断层扫描放射组学和一般图像特征的真实世界研究。
DOI：
10.2147/jhc.s316117
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发表时间：
2021
影响因子：
4.1
通讯作者：
Tu F
中科院分区：
文献类型：
--
作者：
Guo Z;Zhong N;Xu X;Zhang Y;Luo X;Zhu H;Zhang X;Wu D;Qiu Y;Tu F
基于非造影计算机断层扫描（NC-CT）放射组学和临床特征，构建肝细胞癌（HCC）患者经导管动脉化疗栓塞（TACE）治疗的短期缓解和总生存期预测模型。回顾性招募了94例在首次TACE治疗前1周接受CT扫描的HCC患者，并随机分为培训组（n = 47）和验证组（n = 47）。使用MaZda软件提取NC-CT放射组学数据，并通过logistic回归从放射组学和临床特征计算复合模型。通过检查受试者工作特征曲线下面积（AUC）比较不同模型的性能。采用生存分析评价预后。提取30个NC-CT放射组学特征并进行分析。使用四个NC-CT游程矩阵（RLM）特征和一般图像特征（包括肿瘤的最大直径（cm）和肿瘤数量（n））形成复合模型。TACE反应模型的AUC分别为0.840和0.815，而训练组和验证组中6级和12级的AUC分别为0.754和0.750。使用模型的截止值将肝癌患者分为两组：一组是TACE反应导致良好生存的组，另一组是TACE无反应导致预后不良的组。NC-CT的放射组学特征可预测TACE缓解。由NC-CT放射组学和临床特征生成的复合模型是有效的，并直接预测TACE反应和总生存期。该模型可重复使用，操作方便。
To construct a predictive model of short-term response and overall survival for transcatheter arterial chemoembolization (TACE) treatment in hepatocellular carcinoma (HCC) patients based on non-contrast computed tomography (NC-CT) radiomics and clinical features. Ninety-four HCC patients who underwent CT scanning 1 week before the first TACE treatment were retrospectively recruited and divided randomly into a training group (n = 47) and a validation group (n = 47). NC-CT radiomics data were extracted using MaZda software, and the compound model was calculated from radiomics and clinical features by logistic regression. The performance of the different models was compared by examining the area under the receiver operating characteristic curve (AUC). The prediction of prognosis was evaluated using survival analysis. Thirty NC-CT radiomic features were extracted and analyzed. The compound model was formed using four NC-CT run-length matrix (RLM) features and general image features, which included the maximum diameter (cm) of the tumor and the number of tumors (n). The AUCs of the model for TACE response were 0.840 and 0.815, whereas the AUCs of the six-and-twelve grade were 0.754 and 0.750 in the training and validation groups, respectively. HCC patients were divided into two groups using the cutoff value of the model: a group in which the TACE-response led to good survival and a group in which TACE-nonresponse caused poor prognosis. Radiomic features from NC-CT predicted TACE-response. The compound model generated by NC-CT radiomics and clinical features is effective and directly predicts TACE-response and overall survival. The model may be used repeatedly and is easy to operate.