A deep learning model for personalized intra-arterial therapy planning in unresectable hepatocellular carcinoma: a multicenter retrospective study

Xiaoqi Lin; Ran Wei; Ziming Xu; Shuiqing Zhuo; Jiaqi Dou; Haozhong Sun; Rui Li; Runyu Yang; Qian Lu; Chao An; Huijun Chen

doi:10.1016/j.eclinm.2024.102808

A deep learning model for personalized intra-arterial therapy planning in unresectable hepatocellular carcinoma: a multicenter retrospective study

EClinicalMedicine. 2024 Sep 5:75:102808. doi: 10.1016/j.eclinm.2024.102808. eCollection 2024 Sep.

Authors

Xiaoqi Lin¹, Ran Wei², Ziming Xu¹, Shuiqing Zhuo³, Jiaqi Dou¹, Haozhong Sun¹, Rui Li¹, Runyu Yang¹, Qian Lu⁴, Chao An⁵, Huijun Chen¹

Affiliations

¹ School of Biomedical Engineering, Center for Biomedical Imaging Research, Tsinghua University, Beijing, 100019, China.
² Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510080, China.
³ Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, China.
⁴ Tsinghua Changgung Hospital, School of Clinical Medicine, Institute for Precision Medicine, Tsinghua University, Beijing, 100190, China.
⁵ Department of Minimal Invasive Intervention, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, China.

Abstract

Background: Unresectable Hepatocellular Carcinoma (uHCC) poses a substantial global health challenge, demanding innovative prognostic and therapeutic planning tools for improved patient management. The predominant treatment strategies include Transarterial chemoembolization (TACE) and hepatic arterial infusion chemotherapy (HAIC).

Methods: Between January 2014 and November 2021, a total of 1725 uHCC patients [mean age, 52.8 ± 11.5 years; 1529 males] received preoperative CECT scans and were eligible for TACE or HAIC. Patients were assigned to one of the four cohorts according to their treatment, four transformer models (SELECTION) were trained and validated on each cohort; AUC was used to determine the prognostic performance of the trained models. Patients were stratified into high and low-risk groups based on the survival scores computed by SELECTION. The proposed AI-based treatment decision model (ATOM) utilizes survival scores to further inform final therapeutic recommendation.

Findings: In this study, the training and validation sets included 1448 patients, with an additional 277 patients allocated to the external validation sets. The SELECTION model outperformed both clinical models and the ResNet approach in terms of AUC. Specifically, SELECTION-TACE and SELECTION-HAIC achieved AUCs of 0.761 (95% CI, 0.693-0.820) and 0.805 (95% CI, 0.707-0.881) respectively, in predicting ORR in their external validation cohorts. In predicting OS, SELECTION-TC and SELECTION-HC demonstrated AUCs of 0.736 (95% CI, 0.608-0.841) and 0.748 (95% CI, 0.599-0.865) respectively, in their external validation sets. SELECTION-derived survival scores effectively stratified patients into high and low-risk groups, showing significant differences in survival probabilities (P < 0.05 across all four cohorts). Additionally, the concordance between ATOM and clinician recommendations was associated with significantly higher response/survival rates in cases of agreement, particularly within the TACE, HAIC, and TC cohorts in the external validation sets (P < 0.05).

Interpretation: ATOM was proposed based on SELECTION-derived survival scores, emerges as a promising tool to inform the selection among different intra-arterial interventional therapy techniques.

Funding: This study received funding from the Beijing Municipal Natural Science Foundation, China (Z190024); the Key Program of the National Natural Science Foundation of China, China (81930119); The Science and Technology Planning Program of Beijing Municipal Science & Technology Commission and Administrative Commission of Zhongguancun Science Park, China (Z231100004823012); Tsinghua University Initiative Scientific Research Program of Precision Medicine, China (10001020108); and Institute for Intelligent Healthcare, Tsinghua University, China (041531001).

Keywords: Artificial intelligence; Decision support; Deep-learning; Hepatocellular carcinoma.