Li Miao, Zhang Feng, Lu Shen-Xin, Shan Yan, Xu Peng-Ju, Zhou Ying-Ting, Zhu Ying-E, Ren Zheng-Gang, Yang Bi-Wei, Yin Xin
Liver Cancer Institute, Zhongshan Hospital, Fudan University, 136 Yi Xue Yuan Road, Shanghai 200032, China.
National Clinical Research Center for Interventional Medicine, 136 Yi Xue Yuan Road, Shanghai 200032, China.
J Cancer. 2022 Jul 4;13(9):2863-2871. doi: 10.7150/jca.73678. eCollection 2022.
High technical complexity limits the wide use of transradial approach (TRA) chemoembolization in the management of liver cancer. We sought to construct a thoracoabdominal aorta CTA-based nomogram model to identify ideal candidates for TRA chemoembolization in patients with liver cancer. Patients who had received thoracoabdominal aorta CTA before TRA chemoembolization from 2018 to 2020 were retrospectively enrolled and randomly divided into a training set and a validation set. The clinical characteristics and CTA features were collected to build a clinical model. Univariate and multivariate analyses were used to identify significant clinical-radiological variables. A CTA-based nomogram model was constructed by using multivariate logistic regression analysis. The predictive performance, as well as discrimination efficacy of the model, was evaluated by ROC analysis and calibration plot. Vascular variation (=0.028), Myla classification (=0.030), length from left subclavian artery to the left subclavian artery (=0.017), and angle between common hepatic artery and abdominal aorta (=0.017) were identified as important factors associated with the technical complexity of TRA chemoembolization, indicated by fluoroscopy time of the total procedure. The CTA-based nomogram model was established by these abovementioned variables, which demonstrated good predictive ability in both the training cohort (AUC=0.929) and validation cohort (AUC= 0.769), with a high C-index of 0.928 and 0.827 respectively. Moreover, satisfactory calibrations were confirmed by the Hosmer-Lemeshow test with values of 0.618 and 0.299 in the training cohort and validation cohort. Our study constructs a novel CTA-based nomogram, which can serve as a useful tool to identify ideal candidates for TRA chemoembolization in patients with liver cancer.
高技术复杂性限制了经桡动脉途径(TRA)化疗栓塞术在肝癌治疗中的广泛应用。我们试图构建一种基于胸腹主动脉CTA的列线图模型,以识别肝癌患者中TRA化疗栓塞术的理想候选者。回顾性纳入2018年至2020年在TRA化疗栓塞术前接受过胸腹主动脉CTA检查的患者,并将其随机分为训练集和验证集。收集临床特征和CTA特征以建立临床模型。采用单因素和多因素分析来确定显著的临床放射学变量。通过多因素逻辑回归分析构建基于CTA的列线图模型。通过ROC分析和校准图评估模型的预测性能和判别效能。血管变异(=0.028)、Myles分类(=0.030)、从左锁骨下动脉到左锁骨下动脉的长度(=0.017)以及肝总动脉与腹主动脉之间的夹角(=0.017)被确定为与TRA化疗栓塞术技术复杂性相关的重要因素,以整个操作过程的透视时间为指标。基于上述变量建立了基于CTA的列线图模型,该模型在训练队列(AUC=0.929)和验证队列(AUC=0.769)中均显示出良好的预测能力,C指数分别为0.928和0.827。此外,Hosmer-Lemeshow检验在训练队列和验证队列中的值分别为0.618和0.299,证实了校准效果良好。我们的研究构建了一种新型的基于CTA的列线图,可作为识别肝癌患者TRA化疗栓塞术理想候选者的有用工具。