增强造影CT影像特征结合临床因素预测肝细胞癌经导管动脉化疗栓塞的疗效与预后
Contrast-Enhanced CT Imaging Features Combined with Clinical Factors to Predict the Efficacy and Prognosis for Transarterial Chemoembolization of Hepatocellular Carcinoma
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影响因子:3.9
分区:医学2区 / 核医学2区
发表日期:2023 Sep
作者:
Zhongqi Sun, Zhongxing Shi, Yanjie Xin, Sheng Zhao, Hao Jiang, Jinping Li, Jiaping Li, Huijie Jiang
DOI:
10.1016/j.acra.2022.12.031
摘要
准确预测肝细胞癌（HCC）患者对经导管动脉化疗栓塞（TACE）的治疗反应对精准治疗至关重要。本研究旨在建立一种综合模型（DLRC），结合增强CT（CECT）影像与临床因素，以预测HCC患者对TACE的反应。本回顾性研究纳入399例中期肝细胞癌患者。基于动脉期增强CT影像，建立深度学习和放射组学特征。采用相关性分析和最小绝对收缩与选择算子（LASSO）回归分析进行特征筛选。利用多变量逻辑回归构建包含深度学习放射组学特征和临床因素的DLRC模型。通过受试者工作特征曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估模型性能。绘制Kaplan-Meier生存曲线评估随访队列（n=261）的总生存期。DLRC模型由19个定量放射组学特征、10个深度学习特征和3个临床因素构成。在训练集和验证集中的AUC分别为0.937（95% CI：0.912-0.962）和0.909（95% CI：0.850-0.968），优于单一特征或两种特征结合的模型（p < 0.05）。分层分析显示，DLRC在不同亚组间无统计学差异（p > 0.05），DCA确认其具有更大的临床净益。此外，多变量Cox回归显示，DLRC模型输出为独立的总生存风险因素（风险比：1.20，95% CI：1.03-1.40；p=0.019）。该模型在预测TACE反应方面表现出优异的准确性，可作为精准治疗的重要工具。
Abstract
Accurate prediction of treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) is critical for precision treatment. This study aimed to develop a comprehensive model (DLRC) that incorporates contrast-enhanced computed tomography (CECT) images and clinical factors to predict the response to TACE in patients with HCC.A total of 399 patients with intermediate-stage HCC were included in this retrospective study. Deep learning and radiomic signatures were established based on arterial phase CECT images, Correlation analysis and the least absolute shrinkage and selection (LASSO) regression analysis were applied for features selection. The DLRC model incorporating deep learning radiomic signatures and clinical factors was developed using multivariate logistic regression. The area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the models. Kaplan-Meier survival curves based on the DLRC were plotted to assess overall survival in the follow-up cohort (n = 261).The DLRC model was developed using 19 quantitative radiomic features, 10 deep learning features, and 3 clinical factors. The AUC of the DLRC model was 0.937 (95% confidence interval [CI], 0.912-0.962) and 0.909 (95% CI, 0.850-0.968) in the training and validation cohorts, respectively, outperforming models established with two signatures or a single signature (p < 0.05). Stratified analysis showed that the DLRC was not statistically different between subgroups (p > 0.05), and the DCA confirmed the greater net clinical benefit. In addition, multivariable cox regression revealed that DLRC model outputs were independent risk factors for the overall survival (hazard ratios: 1.20, 95% CI: 1.03-1.40; p = 0.019).The DLRC model exhibited a remarkable accuracy in predicting response to TACE, and it can be utilized as a potent tool for precision treatment.