The value of radiomics combined with deep learning based on preoperative CT images in predicting the curative effect of transarterial chemoembolization for hepatocellular carcinoma
10.3760/cma.j.cn112149-20230612-00401
- VernacularTitle: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
- Author:
Dandan WANG
1
;
Haibo WANG
;
Zhongqi SUN
;
Huijie JIANG
Author Information
1. 哈尔滨医科大学附属第二医院CT诊断科，哈尔滨 150086
- Keywords:
Carcinoma, hepatocellular;
Tomography, X-ray computed;
Chemoembolization, therapeutic;
Radiomics;
Deep learning
- From:
Chinese Journal of Radiology
2024;58(2):209-215
- CountryChina
- Language:Chinese
-
Abstract:
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.
