研究动态
Articles below are published ahead of final publication in an issue. Please cite articles in the following format: authors, (year), title, journal, DOI.

对比增强CT成像特征结合临床因素以预测肝细胞癌经肝动脉化疗栓塞治疗的疗效和预后。

Contrast-Enhanced CT Imaging Features Combined with Clinical Factors to Predict the Efficacy and Prognosis for Transarterial Chemoembolization of Hepatocellular Carcinoma.

发表日期:2023 Feb 17
作者: Zhongqi Sun, Zhongxing Shi, Yanjie Xin, Sheng Zhao, Hao Jiang, Jinping Li, Jiaping Li, Huijie Jiang
来源: ACADEMIC RADIOLOGY

摘要:

准确预测肝细胞癌(HCC)患者经肝动脉化学栓塞治疗(TACE)的治疗反应,对于精准治疗至关重要。本研究旨在开发一个综合模型(DLRC),结合增强计算机断层扫描(CECT)图像和临床因素,预测中期HCC患者对TACE的反应。共有399例中期HCC患者被纳入这项回顾性研究。基于动脉期CECT图像建立了深度学习和放射学标志特征,采用相关分析和最小绝对收缩和选择(LASSO)回归分析进行特征筛选。采用多元 logistic 回归建立结合深度学习放射学标志和临床因素的 DLRC 模型,并使用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)来评估模型的性能。采用 DLRC 模型绘制 Kaplan-Meier 生存曲线,以评估随访队列(n = 261)的总生存率。DLRC 模型采用了19个定量放射学特征、10个深度学习特征和3个临床因素。DLRC模型的AUC分别为0.937(95%置信区间[CI],0.912-0.962)和0.909(95% CI,0.850-0.968),在训练组和验证组均表现出优异表现,优于建立两个标志或单个标志的模型(p<0.05)。分层分析显示DLRC在亚组间没有统计学差异(p>0.05),而DCA证实了更大的净临床益处。此外,多元 cox 回归分析表明,DLRC 模型输出是总生存率的独立危险因素(风险比:1.20,95%CI:1.03-1.40; p=0.019)。DLRC 模型在预测 TACE 反应方面表现出显著的准确性,可以作为精准治疗的有力工具。版权所有©2022年大学放射学家协会。由Elsevier Inc.出版。保留所有权利。
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.Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.