Dong Zhi, Lin Yingyu, Lin Fangzeng, Luo Xuyi, Lin Zhi, Zhang Yinhong, Li Lujie, Li Zi-Ping, Feng Shi-Ting, Cai Huasong, Peng Zhenpeng
Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China.
Department of Interventional Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China.
J Hepatocell Carcinoma. 2021 Nov 30;8:1473-1484. doi: 10.2147/JHC.S334674. eCollection 2021.
The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment.
Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled. Clinical data and imaging features based on contrast-enhanced CT were collected for the selection of characteristics. Treatment responses were evaluated based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) by postoperative CT examination within 2 months after the procedure. Python (version 3.70) was used to develop machine learning models. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select features with the impact on predicting treatment response after the first TACE procedure. Six machine learning algorithms were used to build predictive models, including XGBoost, decision tree, support vector machine, random forest, k-nearest neighbor, and fully convolutional networks, and their performances were compared using receiver operator characteristic (ROC) curves to determine the best performing model.
Following TACE, 31 patients (28.2%) were described as responsive to TACE, while 72 patients (71.8%) were nonresponsive to TACE. Portal vein tumor thrombosis type, albumin level, and distribution of tumors within the liver were selected for predictive model building. Among the models, the RF model showed the best performance, with area under the curve (AUC), accuracy, sensitivity, and specificity of 0.802, 0.784, 0.904, and 0.480, respectively.
Machine learning models can provide an accurate prediction of the early response of initial TACE treatment for HCC, which can help in individualizing clinical decision-making and modification of further treatment strategies for patients with unresectable HCC.
对肝细胞癌（HCC）患者进行初次传统经动脉化疗栓塞术（cTACE）后的治疗反应对其预后至关重要。本研究探索并验证了基于临床数据和对比增强计算机断层扫描（CT）图像结果的机器学习模型预测HCC患者初次cTACE治疗后早期反应的可行性。
本研究回顾性纳入了110例首次接受cTACE治疗的不可切除HCC患者。收集基于对比增强CT的临床数据和影像特征以选择特征变量。在术后2个月内通过术后CT检查，根据改良实体瘤疗效评价标准（mRECIST）评估治疗反应。使用Python（版本3.70）开发机器学习模型。应用最小绝对收缩和选择算子（LASSO）算法选择对预测首次TACE术后治疗反应有影响的特征。使用六种机器学习算法构建预测模型，包括XGBoost、决策树、支持向量机、随机森林、k近邻和全卷积网络，并使用受试者工作特征（ROC）曲线比较它们的性能以确定表现最佳的模型。
TACE术后，31例患者（28.2%）对TACE有反应，而72例患者（71.8%）对TACE无反应。选择门静脉癌栓类型、白蛋白水平和肝脏内肿瘤分布用于构建预测模型。在这些模型中，随机森林（RF）模型表现最佳，曲线下面积（AUC）、准确率、灵敏度和特异度分别为0.802、0.784、0.904和0.480。
机器学习模型可以准确预测HCC初次TACE治疗的早期反应，这有助于为不可切除HCC患者制定个体化的临床决策和调整进一步的治疗策略。
