Chao An,1,* Lei Li,2,* Yang Luo,3,* Mengxuan Zuo,1 Wendao Liu,4 Chengzhi Li,5 Peihong Wu1 

1Department of Minimal Invasive Intervention, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China; 2Department of Interventional Radiology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences (Qingdao Central Hospital), Qingdao, Shandong, 266042, People’s Republic of China; 3School of Life Science and Technology, Xidian University, Xi’an, People’s Republic of China; 4Department of Interventional Therapy, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, People’s Republic of China; 5Department of Interventional Radiology and Vascular Surgery, The First Affiliated Hospital of Jinan University, Jinan, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Peihong Wu, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People’s Republic of China, Tel/Fax +86-20-87343272, Email wuph@sysucc.org.cn

Background: Hepatocellular carcinoma (HCC) is a major global health burden, with most patients presenting at advanced stages, limiting treatment options to intra-arterial therapy (IAT) such as transarterial chemoembolization (TACE) and hepatic arterial infusion chemotherapy (HAIC). However, optimizing IAT selection for large HCC remains challenging due to tumor heterogeneity and varying patient responses.
Aim: To develop and validate a deep learning (DL) model for guidance of decision-making between TACE and HAIC for unresectable HCC.
Methods: We conducted a retrospective, multi-center study involving 900 patients with large HCC treated with IATs. The DEep Learning for Interaction and Covariate Analysis in Intra-arterial Therapy SElection (DELICAITE) model integrates deep convolutional neural networks (DCNN) with covariate interaction analysis. The model was trained on dual-modal clinical and imaging data to predict treatment response and was validated using prospective and independent external validation cohorts.
Results: The DELICAITE model demonstrated superior discriminative ability and accuracy in predicting progressive disease (PD) in both internal and external test sets, with AUCs of 0.756, 0.664, and 0.701, respectively. Patients classified by the model into the “Maintain” group showed significantly longer overall survival (OS) compared to the “Alter” group (11.3 months vs 8.1 months, P < 0.001). The model’s performance was further supported by its ability to stratify patients into subgroups most likely to benefit from TACE or HAIC.
Conclusion: The DELICAITE model provides a precise and innovative approach to refine IAT schemes for large HCC, offering clinicians a reliable tool to select the most suitable treatment option and potentially improve patient survival outcomes.

Keywords: hepatocellular carcinoma, intra-arterial therapies, deep learning, progressive disease, overall survival