Xu Jun, Li Junjun, Wang Tengfei, Luo Xin, Zhu Zhangxiang, Wang Yimou, Wang Yong, Zhang Zhenglin, Song Ruipeng, Yang Li-Zhuang, Wang Hongzhi, Wong Stephen T C, Li Hai
Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.
University of Science and Technology of China, Hefei, 230026, People's Republic of China.
BMC Cancer. 2025 Apr 3;25(1):602. doi: 10.1186/s12885-025-13978-4.
BACKGROUND: Identifying effective predictive strategies to assess the response of immune checkpoint inhibitors (ICIs)-based combination therapy in advanced hepatocellular carcinoma (HCC) is crucial. This study presents a new longitudinal CT-based radiomics model to predict treatment response and prognosis in advanced HCC patients undergoing ICIs-based combination therapy. METHODS: Longitudinal CT images were collected before and during the treatment for HCC patients across three institutions from January 2019 to April 2022. A total of 1316 radiomic features were extracted from arterial and portal venous phase abdominal CT images for each patient. A model called Longitudinal Whole-liver CT-based Radiomics (LWCTR) was developed to categorize patients into responders or non-responders using radiomic features and clinical information through support vector machine (SVM) classifiers. The area under the curve (AUC) was used as the performance metric and subsequently applied for risk stratification and prognostic assessment. The Shapley Additive explanations (SHAP) method was used to calculate the Shapley value, which explains the contribution of each feature in the SVM model to the prediction. RESULTS: This study included 395 eligible participants, with a median age of 57 years (IQR 51-66), comprising 344 males and 51 females. The LWCTR model performed well in predicting treatment response, achieving an AUC of 0.883 (95% confidence interval [CI] 0.881-0.888) in the training cohort, 0.876 (0.858-0.895) in the internal validation cohort, and 0.875 (0.860-0.887) in the external test cohort. The Rad-Nomo model, integrating the LWCTR model's prediction score (Rad-score) with the modified Response Evaluation Criteria in Solid Tumors (mRECIST), demonstrated strong prognostic performance. It achieved time-dependent AUC values of 0.902, 0.823, and 0.850 at 1, 2, and 3 years in the internal validation cohort and 0.893, 0.848, and 0.762 at the same intervals in the external test cohort. CONCLUSION: The proposed LWCTR model performs well in predicting treatment response and prognosis in patients with HCC receiving ICIs-based combination therapy, potentially contributing to personalized and timely treatment decisions.
背景：确定有效的预测策略以评估基于免疫检查点抑制剂（ICI）的联合疗法在晚期肝细胞癌（HCC）中的反应至关重要。本研究提出了一种新的基于CT的纵向放射组学模型，用于预测接受基于ICI联合疗法的晚期HCC患者的治疗反应和预后。 方法：收集了2019年1月至2022年4月期间来自三个机构的HCC患者治疗前和治疗期间的纵向CT图像。为每位患者从腹部CT动脉期和门静脉期图像中提取了总共1316个放射组学特征。开发了一种名为基于全肝CT的纵向放射组学（LWCTR）的模型，通过支持向量机（SVM）分类器使用放射组学特征和临床信息将患者分为反应者或无反应者。曲线下面积（AUC）用作性能指标，随后用于风险分层和预后评估。使用Shapley加性解释（SHAP）方法计算Shapley值，该值解释了SVM模型中每个特征对预测的贡献。 结果：本研究纳入了395名符合条件的参与者，中位年龄为57岁（IQR 51 - 66），其中男性344名，女性51名。LWCTR模型在预测治疗反应方面表现良好，在训练队列中的AUC为0.883（95%置信区间[CI] 0.881 - 0.888），内部验证队列中为0.876（0.858 - 0.895），外部测试队列中为0.875（0.860 - 0.887）。将LWCTR模型的预测评分（Rad评分）与改良实体瘤反应评估标准（mRECIST）相结合的Rad-Nomo模型显示出强大的预后性能。在内部内部内部验证队列中，其在1年、2年和3年时的时间依赖性AUC值分别为0.902、0.823和0.850，在外部测试队列中相同时间间隔时分别为0.893、0.848和0.762。 结论：所提出的LWCTR模型在预测接受基于ICI联合疗法的HCC患者的治疗反应和预后方面表现良好，可能有助于做出个性化和及时的治疗决策。
J Natl Cancer Inst. 2024-10-1
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