基于ct的栖息地模型预测经动脉化疗栓塞联合分子靶向药物和免疫检查点抑制剂治疗肝癌的肿瘤反应和生存

IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Xiao Shen, Jin-Xing Zhang, Hai-Tao Yan, Jin Liu, Sheng Liu, Hai-Bin Shi, Qing-Quan Zu
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引用次数: 0

摘要

基本原理和目的:开发并验证基于ct的栖息地模型,用于预测不可切除肝细胞癌(uHCC)患者接受经动脉化疗栓塞,联合分子靶向药物和免疫检查点抑制剂(TACE-MTAs-ICIs)的肿瘤反应和总生存期(OS)。材料和方法:回顾性纳入2019年6月至2024年8月期间接受TACE-MTAs-ICIs治疗的200例患者。从增强CT图像中提取体素水平的放射学特征,并使用K-means聚类识别肿瘤栖息地。从栖息地亚区和整个肿瘤体积中提取放射组学特征(常规放射组学)。建立支持向量机(SVM)模型预测肿瘤反应,采用SHapley加性解释(SHAP)分析可解释性。同时,构建Cox比例风险模型预测OS。将独立的临床危险因素与放射学特征相结合,建立联合模型。使用多个统计指标对模型性能进行评估和比较。结果:在试验队列中,habitat模型在肿瘤反应预测(AUC: 0.881)和OS分层(C-index: 0.703; 1年AUC: 0.788; 2年AUC: 0.806)方面表现较好,优于传统的放射组学模型。值得注意的是,结合栖息地特征和临床变量的综合模型进一步提高了预测精度,反应预测的AUC为0.884,OS区分能力更强(C-index: 0.749; 1年AUC: 0.831; 2年AUC: 0.841)。结论:提出的基于ct的栖息地模型能够更准确和可解释地评估HCC的治疗反应和OS,提供有价值的反映肿瘤内异质性的非侵入性生物标志物。这种方法有望改善个体化治疗计划和临床结果。数据可得性声明:支持本研究结果的数据可根据通讯作者的合理要求获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CT-Based Habitat Model for Predicting Tumor Response and Survival in Hepatocellular Carcinoma Treated with Transarterial Chemoembolization Combining Molecular Targeted Agents and Immune Checkpoint Inhibitors.

Rationale and objectives: To develop and validate a CT-based habitat model for predicting tumor response and overall survival (OS) in patients with unresectable hepatocellular carcinoma (uHCC) undergoing transarterial chemoembolization, combined with molecular-targeted agents, and immune checkpoint inhibitors (TACE-MTAs-ICIs).

Materials and methods: A total of 200 patients treated with TACE-MTAs-ICIs between June 2019 and August 2024 were retrospectively included. Voxel-level radiomic features were extracted from contrast-enhanced CT images, and tumor habitats were identified using K-means clustering. Radiomic features were extracted from both habitat subregions and the entire tumor volume (conventional radiomics). A support vector machine (SVM) model was developed to predict tumor response, with SHapley Additive exPlanations (SHAP) analysis applied for interpretability. In parallel, a Cox proportional hazards model was constructed to predict OS. Independent clinical risk factors were incorporated with radiomic features to build a combined model. Model performance was evaluated and compared using multiple statistical metrics.

Results: In the test cohort, the habitat model achieved strong performance for tumor response prediction (AUC: 0.881) and OS stratification (C-index: 0.703; 1-year AUC: 0.788; 2-year AUC: 0.806), outperforming the conventional radiomics model. Notably, the integrated model combining habitat features and clinical variables further improved predictive accuracy, yielding an AUC of 0.884 for response prediction and superior OS discrimination (C-index: 0.749; 1-year AUC: 0.831; 2-year AUC: 0.841).

Conclusion: The proposed CT-based habitat model enables a more accurate and interpretable assessment of treatment response and OS in HCC, offering valuable noninvasive biomarkers that reflect intra-tumor heterogeneity. This approach holds promise for improving individualized treatment planning and clinical outcomes.

Data availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.

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来源期刊
Academic Radiology
Academic Radiology 医学-核医学
CiteScore
7.60
自引率
10.40%
发文量
432
审稿时长
18 days
期刊介绍: Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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