术前CT图像影像组学联合深度学习预测肝细胞癌经动脉化疗栓塞术后疗效的价值

The value of radiomics combined with deep learning based on preoperative CT images in predicting the curative effect of transarterial chemoembolization for hepatocellular carcinoma

目的探讨术前CT图像影像组学联合深度学习方法预测肝细胞癌(HCC)首次经动脉化疗栓塞术(TACE)疗效的价值。方法该研究为回顾性队列研究。回顾性收集2015年1月至2021年1月于哈尔滨医科大学附属第二医院行TACE治疗的HCC患者影像及临床信息。共纳入265例患者, 于初次TACE后1~2个月, 根据改良的实体瘤疗效评估标准(mRECIST)评估病灶术后改变, 分为有反应组(175例)和无反应组(90例)。采用随机数表法按8∶2的比例分为训练集(212例, 有反应组140例、无反应组72例)和测试集(53例, 有反应组35例、无反应组18例)。采用单因素和多因素logistic回归筛选临床变量, 构建临床模型。从术前CT图像中提取影像组学特征, 经降维构建影像组学模型。采用深度学习方法, 建立3种残差神经网络(ResNet)模型(ResNet18、ResNet50和ResNet101), 并对其效能进行比较和集成, 取最佳模型为深度学习模型。应用logistic回归将3个模型两两联合, 建立联合模型。采用受试者操作特征曲线在测试集中评价模型区分TACE后有反应与无反应的效能。结果在测试集中, 临床模型、影像组学模型区分TACE后有反应与无反应的曲线下面积(AUC)为0.730(95%CI 0.569~0.891)、0.775(95%CI 0.642~0.907), ResNet18、ResNet50和ResNet101的AUC分别为0.719、0.748、0.533, 将ResNet18、ResNet50集成获得深度学习模型, AUC为0.806(95%CI 0.665~0.946)。两两融合后, 深度学习-影像组学联合模型效能最高, AUC为0.843(95%CI 0.730~0.956), 优于深度学习-临床模型(AUC为0.838, 95%CI 0.719~0.957)和影像组学-临床模型(AUC为0.786, 95%CI 0.648~0.898)。结论联合影像组学和深度学习的联合模型可以在术前预测HCC患者行TACE的疗效, 具有较高的效能。

Objective To explore the value of radiomics and deep learning in predicting the efficacy of initial transarterial chemoembolization(TACE)for hepatocellular carcinoma(HCC).Methods This was a cohort study.The imaging and clinical information of HCC patients treated with TACE in the Second Affiliated Hospital of Harbin Medical University from January 2015 to January 2021 were collected retrospectively.A total of 265 patients were divided into response group(175 cases)and non-response group(90 cases)according to the modified solid tumor efficacy evaluation criteria(mRECIST)1 to 2 months after initial TACE.According to the proportion of 8∶2,the patients were randomly divided into training group(212 cases,140 responders and 72 non-responders)and test set(53 cases,35 responders and 18 non-responders).Univariate and multivariate logistic regression was used to screen clinical variables and construct a clinical model.The radiomics features were extracted from the preoperative CT images,and radiomics model was constructed after feature dimensionality reduction.Using the deep learning method,three residual network(ResNet)models(ResNet18,ResNet50 and ResNet101)were established,and their effectiveness was compared and integrated to build a deep learning model with best performance.Univariate and multivariate logistic regression was used to combine pairwise three models to establish the combined model.The receiver operating characteristic curve was used to evaluate the performance of the model to distinguish between TACE response and non-response groups.Results In the test set,the area under the curve(AUC)of the clinical model and the radiomics model in the differentiation between response and non-response after TACE were 0.730(95%CI 0.569-0.891)and 0.775(95%CI 0.642-0.907).The AUC of ResNet18,ResNet50 and ResNet101 were 0.719,0.748 and 0.533,respectively.The AUC for deep learning model obtained by integrating ResNet18 and ResNet50 was 0.806(95%CI 0.665-0.946).After pairwise fusion,the combined deep learning-radiomics model showed the highest performance,with an AUC of 0.843(95%CI 0.730-0.956),which was better than those of the deep learning-clinical model(AUC of 0.838,95%CI 0.719-0.957)and the radiomics-clinical model(AUC of 0.786,95%CI 0.648-0.898).Conclusions The combined model of radiomics and deep learning has high performance in predicting the curative effect of TACE in patients with HCC before operation.