Min Ji Hye, Chen Pin-Jung, Qureshi Touseef Ahmad, Javed Sehrish, Xie Yibin, Azab Linda, Wang Lixia, Kim Hyun-Seok, Li Debiao, Yang Ju Dong
Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea.
Diagnostics (Basel). 2025 Aug 20;15(16):2090. doi: 10.3390/diagnostics15162090.
Predicting treatment response to immunotherapy in hepatocellular carcinoma (HCC) is essential to improve clinical outcomes with personalized treatment strategies. This study aims to develop an AI-driven prediction model using radiomic analysis from the liver and viable HCCs on pretreatment CT to differentiate responders from non-responders. HCC patients who received immunotherapy between 2016 and 2023 with pretreatment CT scans were included. Radiomic features were extracted from the whole liver and the viable HCCs on the portal venous phase CT prior to immunotherapy. Multiple machine learning models were trained for binary classification to predict treatment response, initially using liver features (Model 1), and subsequently including both liver and tumor features (Model 2). Model performance was evaluated using three-fold cross-validation. Among 55 HCC patients (median age, 69; 76.4% male) who received immunotherapy, 21 (38.2%) were responders and 34 (61.8%) non-responders by mRECIST criteria. Over 5000 radiomic features were extracted from pretreatment CT scans of the liver and viable tumors, of which approximately 100 were predictive of treatment response. Model 1 (liver) achieved an average accuracy of 77%, sensitivity of 76%, and specificity of 78%. Model 2 (liver and tumor) demonstrated improved performance, with accuracy, sensitivity, and specificity of 86%, 70%, and 94%, respectively, supporting the value of combined liver-tumor radiomics in treatment response prediction. This pilot study developed an AI-based model using CT-derived radiomic features to predict immunotherapy response in HCC patients. The approach may offer a non-invasive strategy to support personalized treatment planning using pretreatment CT scans.
预测肝细胞癌（HCC）对免疫疗法的治疗反应对于通过个性化治疗策略改善临床结果至关重要。本研究旨在利用治疗前CT上肝脏和存活HCC的放射组学分析开发一种人工智能驱动的预测模型，以区分反应者和无反应者。纳入了2016年至2023年间接受免疫疗法且有治疗前CT扫描的HCC患者。在免疫疗法前的门静脉期CT上从全肝和存活的HCC中提取放射组学特征。训练多个机器学习模型进行二元分类以预测治疗反应，最初使用肝脏特征（模型1），随后包括肝脏和肿瘤特征（模型2）。使用三倍交叉验证评估模型性能。在55例接受免疫疗法的HCC患者（中位年龄69岁；76.4%为男性）中，根据mRECIST标准，21例（38.2%）为反应者，34例（61.8%）为无反应者。从肝脏和存活肿瘤的治疗前CT扫描中提取了5000多个放射组学特征，其中约100个可预测治疗反应。模型1（肝脏）的平均准确率为77%，敏感性为76%，特异性为78%。模型2（肝脏和肿瘤）表现出更好的性能，准确率、敏感性和特异性分别为86%、70%和94%，支持联合肝脏-肿瘤放射组学在治疗反应预测中的价值。这项初步研究开发了一种基于人工智能的模型，使用CT衍生的放射组学特征来预测HCC患者的免疫疗法反应。该方法可能提供一种非侵入性策略，以支持使用治疗前CT扫描进行个性化治疗规划。
