Feng Ye, Xu Yinuo, Wang Jian, Cao Zhenyu, Liu Bojun, Du Zeliu, Zhou Lingling, Hua Haokai, Wang Wenjie, Mei Jie, Lai Linqiang, Tu Jianfei
Zhejiang Province Key Laboratory of Imaging and Interventional Medicine, Wenzhou Medical University Affiliated Fifth Hospital, Zhejiang, China (Y.F., Y.X., B.L., Z.D., L.Z., H.H., W.W., J.M., J.T.).
Department of Radiology, Tongde Hospital of Zhejiang Province Affiliated to Zhejiang Chinese Medical University (Tongde Hospital of Zhejiang Province), Zhejiang, China (J.W., Z.C.).
Acad Radiol. 2025 Aug 9. doi: 10.1016/j.acra.2025.07.039.
Bronchial artery chemoembolization (BACE) is a new treatment method for lung cancer. This study aimed to investigate the ability of dual-energy computed tomography (DECT) to predict early recurrence (ER) after BACE among patients with non-small cell lung cancer (NSCLC) who failed first-line therapy.
Clinical and imaging data from NSCLC patients undergoing BACE at Wenzhou Medical University Affiliated Fifth *** Hospital (10/2023-06/2024) were retrospectively analyzed. Logistic regression (LR) machine learning models were developed using 5 arterial-phase (AP) virtual monoenergetic images (VMIs; 40, 70, 100, 120, and 150 keV), while deep learning models utilized ResNet50/101/152 architectures with iodine maps. A combined model integrating optimal Rad-score, DL-score, and clinical features was established. Model performance was assessed via area under the receiver operating characteristic curve analysis (AUC), with SHapley Additive exPlanations (SHAP) framework applied for interpretability.
A total of 196 patients were enrolled in this study (training cohort: n=158; testing cohort: n=38). The 100 keV machine learning model demonstrated superior performance (AUC=0.751) compared to other VMIs. The deep learning model based on the ResNet101 method (AUC=0.791) performed better than other approaches. The hybrid model combining Rad-score-100keV-A, Rad-score-100keV-V, DL-score-ResNet101-A, DL-score-ResNet101-V, and clinical features exhibited the best performance (AUC=0.798) among all models.
DECT holds promise for predicting ER after BACE among NSCLC patients who have failed first-line therapy, offering valuable guidance for clinical treatment planning.
支气管动脉化疗栓塞术（BACE）是一种肺癌的新治疗方法。本研究旨在探讨双能计算机断层扫描（DECT）预测一线治疗失败的非小细胞肺癌（NSCLC）患者接受BACE治疗后早期复发（ER）的能力。
回顾性分析温州医科大学附属第五***医院（2023年10月 - 2024年6月）接受BACE治疗的NSCLC患者的临床和影像数据。使用5幅动脉期（AP）虚拟单能量图像（VMIs；40、70、100、120和150 keV）建立逻辑回归（LR）机器学习模型，而深度学习模型采用带有碘图的ResNet50/101/152架构。建立了一个整合最佳Rad评分、DL评分和临床特征的联合模型。通过受试者操作特征曲线分析（AUC）评估模型性能，并应用SHapley加性解释（SHAP）框架进行可解释性分析。
本研究共纳入196例患者（训练队列：n = 158；测试队列：n = 38）。100 keV机器学习模型表现出优于其他VMIs的性能（AUC = 0.751）。基于ResNet101方法的深度学习模型（AUC = 0.791）比其他方法表现更好。结合Rad评分 - 100keV - A、Rad评分 - 100keV - V、DL评分 - ResNet101 - A、DL评分 - ResNet101 - V和临床特征的混合模型在所有模型中表现最佳（AUC = 0.798）。
DECT有望预测一线治疗失败的NSCLC患者接受BACE治疗后的ER，为临床治疗规划提供有价值的指导。
