机构：[1]School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.[2]Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China.[3]Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.四川大学华西医院[4]Department of Medical Laboratory Science, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China.[5]Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu 610041, China.四川大学华西医院[6]Department of Radiology, Sanya People's Hospital, Sanya 572000, China.
Background: The transcription factor SOX9 plays a critical role in various diseases, including hepatocellular carcinoma (HCC), and has been implicated in resistance to sorafenib treatment. Accurate assessment of SOX9 expression is important for guiding personalized therapy in HCC patients; however, a reliable non-invasive method for evaluating SOX9 status remains lacking. This study aims to develop a deep learning (DL) model capable of preoperatively and non-invasively predicting SOX9 expression from CT images in HCC patients. Methods: We retrospectively analyzed a dataset comprising 4011 CT images from 101 HCC patients who underwent surgical resection followed by sorafenib therapy at West China Hospital, Sichuan University. A deep reinforcement learning (DRL) approach was proposed to enhance prediction accuracy by identifying and focusing on image regions highly correlated with SOX9 expression, thereby reducing the impact of background noise. Results: Our DRL-based model achieved an area under the curve (AUC) of 91.00% (95% confidence interval: 88.64-93.15%), outperforming conventional DL methods by over 10%. Furthermore, survival analysis revealed that patients with SOX9-positive tumors had significantly shorter recurrence-free survival (RFS) and overall survival (OS) compared to SOX9-negative patients, highlighting the prognostic value of SOX9 status. Conclusions: This study demonstrates that a DRL-enhanced DL model can accurately and non-invasively predict SOX9 expression in HCC patients using preoperative CT images. These findings support the clinical utility of imaging-based SOX9 assessment in informing treatment strategies and prognostic evaluation for patients with advanced HCC.
基金：
This work was supported by the National Key Research and Development Program under
Grant 2022YFB3104600, the Municipal Government of Quzhou (Grant 2023D007, Grant 2023D014,
Grant 2023D033, Grant 2023D034, Grant 2023D035, Grant 2024D058), and the Guiding project of
Quzhou Science and Technology Bureau (2022K50, 2023K013 and 2023K016).
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2025]版：
大类|3 区医学
小类|3 区医学：内科
最新[2025]版：
大类|3 区医学
小类|3 区医学：内科
第一作者：
第一作者机构：[1]School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.[2]Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China.
通讯作者：
通讯机构：[2]Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China.[3]Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.[4]Department of Medical Laboratory Science, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China.[6]Department of Radiology, Sanya People's Hospital, Sanya 572000, China.
推荐引用方式(GB/T 7714)：
Liu Minghui,Wei Yi,Xie Tianshu,et al.Deep Reinforcement Learning for CT-Based Non-Invasive Prediction of SOX9 Expression in Hepatocellular Carcinoma[J].Diagnostics (Basel, Switzerland).2025,15(10):doi:10.3390/diagnostics15101255.
APA：
Liu Minghui,Wei Yi,Xie Tianshu,Yang Meiyi,Cheng Xuan...&Liu Ming.(2025).Deep Reinforcement Learning for CT-Based Non-Invasive Prediction of SOX9 Expression in Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland),15,(10)
MLA：
Liu Minghui,et al."Deep Reinforcement Learning for CT-Based Non-Invasive Prediction of SOX9 Expression in Hepatocellular Carcinoma".Diagnostics (Basel, Switzerland) 15..10(2025)
