Background/Objectives: To develop a decision framework integrating computed tomography (CT) radiomics and clinical factors to guide the selection of transarterial chemoembolization (TACE) technique for optimizing treatment response in non-resectable hepatocellular carcinoma (HCC). Methods: A retrospective analysis was performed on 151 patients [33 conventional TACE (cTACE), 69 drug-eluting bead TACE (DEB-TACE), 49 degradable starch microsphere TACE (DSM-TACE)] who underwent TACE for HCC at a single tertiary center. Pre-TACE contrast-enhanced CT images were used to extract radiomic features of the TACE-treated liver tumor volume. Patient clinical and laboratory data were combined with radiomics-derived predictors in an elastic net regularized logistic regression model to identify independent factors associated with early response at 4–6 weeks post-TACE. Predicted response probabilities under each TACE technique were compared with the actual techniques performed. Results: Elastic net modeling identified three independent predictors of response: radiomic feature “Contrast” (OR = 5.80), BCLC stage B (OR = 0.92), and viral hepatitis etiology (OR = 0.74). Interaction models indicated that the relative benefit of each TACE technique depended on the identified patient-specific predictors. Model-based recommendations differed from the actual treatment selected in 66.2% of cases, suggesting potential for improved patient–technique matching. Conclusions: Integrating CT radiomics with clinical variables may help identify the optimal TACE technique for individual HCC patients. This approach holds promise for a more personalized therapy selection and improved response rates beyond standard clinical decision-making.
背景/目的：构建一个整合计算机断层扫描（CT）影像组学与临床因素的决策框架，以指导不可切除肝细胞癌（HCC）患者经动脉化疗栓塞术（TACE）技术的选择，从而优化治疗反应。方法：回顾性分析151例在单一三级医疗中心接受TACE治疗的HCC患者[其中33例接受传统TACE（cTACE），69例接受药物洗脱微球TACE（DEB-TACE），49例接受可降解淀粉微球TACE（DSM-TACE）]。研究基于TACE治疗前增强CT图像提取TACE靶区肝肿瘤体积的影像组学特征。将患者临床及实验室数据与影像组学预测因子共同纳入弹性网络正则化逻辑回归模型，以识别与TACE术后4-6周早期治疗反应相关的独立因素。比较各TACE技术下的预测反应概率与实际采用的治疗技术。结果：弹性网络模型识别出三个独立的治疗反应预测因子：影像组学特征“对比度”（OR=5.80）、BCLC B期（OR=0.92）和病毒性肝炎病因（OR=0.74）。交互模型显示各TACE技术的相对获益取决于所识别的患者特异性预测因子。基于模型的治疗建议与临床实际选择在66.2%的病例中存在差异，提示患者与技术匹配存在优化空间。结论：整合CT影像组学与临床变量有助于为个体化HCC患者确定最佳TACE技术。该方法有望实现超越常规临床决策的个性化治疗方案选择，并提高治疗反应率。
肿瘤(癌症)患者之家