Evaluation of dual-energy CT derived radiomics signatures in predicting outcomes in patients with advanced gastric cancer after neoadjuvant chemotherapy
Evaluation of dual-energy CT derived radiomics signatures in predicting outcomes in patients with advanced gastric cancer after neoadjuvant chemotherapy
复制标题
双能 CT 衍生的放射组学特征对晚期胃癌患者新辅助化疗后预后预测的评估
DOI：
10.1016/j.ejso.2021.07.014
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发表时间：
2022-02-24
期刊：
影响因子：
3.8
通讯作者：
Zhang, Huan
中科院分区：
文献类型：
--
作者：
Chen, Yong;Yuan, Fei;Zhang, Huan
背景资料：为探讨基于双能CT（DECT）的放射组学对进展期胃癌（AGC）患者新辅助化疗（NAC）后无病生存期（DFS）和总生存期（OS）的预测价值，方法：2014年1月至2018年12月，共入组156例AGC患者，按2：1的比例随机分为训练队列和试验队列。在8个图像系列上描绘原发性肿瘤的感兴趣体积。为每个存活臂生成来自前NAC和delta放射组学的四个特征集。随机存活森林用于生成最佳放射组学签名（RS）。模型评价的统计指标包括Harrell一致性指数（C指数）和整个随访期间的平均累积/动态AUC。建立了一个临床模型和一个组合的Rad-clinical模型进行比较。结果：前IU（来自NAC前的碘摄取图像）RS在测试队列中的DFS和OS表现最好（C指数，0.784和0.698;平均累积/动态AUC，0.80和0.77）。当与临床模型相比时，放射组学模型具有显著更高的C指数来预测测试队列中的DFS（0.784对0.635，p < 0.001），但没有发现OS的统计学差异（0.698对0.680，p = 0.473）。联合放射临床模型显示，在测试队列中的性能得到改善，C指数为0.810和0.710 DFS和OS，foreign.Conclusion：DECT衍生的放射组学作为一个有前途的非侵入性生物标志物，以预测NAC后AGC患者的生存，提供了一个机会，转化为适当的治疗。(C)2021年爱思唯尔有限公司，BASO类似于癌症外科协会和欧洲外科肿瘤学会。All rights reserved.
Background: To investigate the prognostic value of dual-energy CT (DECT) based radiomics to predict disease-free survival (DFS) and overall survival (OS) for patients with advanced gastric cancer (AGC) after neoadjuvant chemotherapy (NAC).Methods: From January 2014 to December 2018, a total of 156 AGC patients were enrolled and randomly allocated into a training cohort and a testing cohort at a ratio of 2:1. Volume of interest of primary tumor was delineated on eight image series. Four feature sets derived from pre-NAC and delta radiomics were generated for each survival arm. Random survival forest was used for generating the optimal radiomics signature (RS). Statistical metrics for model evaluation included Harrell's concordance index (C-index) and the average cumulative/dynamic AUC throughout follow-up. A clinical model and a combined Rad-clinical model were built for comparison.Results: The pre-IU (derived from iodine uptake images before NAC) RS performed best for DFS and OS in the testing cohort (C-indices, 0.784 and 0.698; the average cumulative/dynamic AUCs, 0.80 and 0.77). When compared with the clinical model, the radiomics model had significantly higher C-index to predict DFS in the testing cohort (0.784 vs. 0.635, p < 0.001), but no statistical difference was found for OS (0.698 vs. 0.680, p = 0.473). The combined Rad-clinical models showed improved performance in the testing cohort, with C-indices of 0.810 and 0.710 for DFS and OS, respectively.Conclusion: DECT-derived radiomics serves as a promising non-invasive biomarker to predict survival for AGC patients after NAC, providing an opportunity for transforming proper treatment. (C) 2021 Elsevier Ltd, BASO similar to The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.