对比增强的CT成像特征与临床因素相结合，以预测肝细胞癌跨性化学栓塞的功效和预后
Contrast-Enhanced CT Imaging Features Combined with Clinical Factors to Predict the Efficacy and Prognosis for Transarterial Chemoembolization of Hepatocellular Carcinoma
影响因子:3.90000
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
摘要
肝细胞癌（HCC）患者对跨性化学栓塞（TACE）的治疗反应的准确预测对于精确治疗至关重要。这项研究旨在开发一种综合模型（DLRC），该模型结合了对比增强的计算机断层扫描（CECT）图像和临床因素，以预测HCC患者对TACE的反应。这本回顾性研究总共包括399例中级HCC患者。根据动脉相结合图像，相关分析以及最低绝对的收缩和选择（Lasso）回归分析，将深度学习和放射线标记用于特征选择。使用多元逻辑回归开发了结合深度学习放射线标志和临床因素的DLRC模型。接收器操作特征曲线（AUC），校准曲线和决策曲线分析（DCA）下的面积用于评估模型的性能。绘制了基于DLRC的Kaplan-Meier生存曲线，以评估随访队列中的总体存活率（n = 261）。使用19个定量放射线特征，10个深度学习特征和3个临床因素开发了DLRC模型。 DLRC模型的AUC为0.937（95％置信区间[CI]，0.912-0.962）和0.909和0.909（95％CI，0.850-0.968），分别在培训和验证群体中分别超过了两个签名模型，该模型具有两个签名或单个签名或单个签名（P <0.05）。分层分析表明，在亚组之间，DLRC在统计学上没有差异（p> 0.05），并且DCA证实了更大的净临床益处。此外，多变量COX回归表明，DLRC模型输出是整体生存的独立风险因素（危险比：1.20，95％CI：1.03-1.40; p = 0.019）。DLRC模型在预测对TACE的反应时表现出了出色的准确性，并且可以通过As a af As a As a As Potement Portent Workes进行预测。
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.