Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography

Abstract

Zhi Dong,1,* Yingyu Lin,1,* Fangzeng Lin,2,* Xuyi Luo,3 Zhi Lin,1 Yinhong Zhang,1 Lujie Li,1 Zi-Ping Li,1 Shi-Ting Feng,1 Huasong Cai,1 Zhenpeng Peng1 1Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People’s Republic of China; 2Department of Interventional Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People’s Republic of China; 3Department of Emergency, Guangzhou First People’s Hospital, Guangzhou, Guangdong, 510180, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huasong Cai; Zhenpeng PengDepartment of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58th, The Second Zhongshan Road, Guangzhou, Guangdong, People’s Republic of ChinaTel [phone], extension 8471Fax [phone]Email [email protected]; [email protected]: 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.Patients and Methods: 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.Results: 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.Conclusion: 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.Keywords: hepatocellular carcinoma, transarterial chemoembolization, machine learning, prediction model, treatment response

Keywords

• hepatocellular carcinoma
• transarterial chemoembolization
• machine learning
• prediction model
• treatment response