Radiomics Features at Multiparametric MRI Predict Disease-Free Survival in Patients With Locally Advanced Rectal Cancer
Radiomics Features at Multiparametric MRI Predict Disease-Free Survival in Patients With Locally Advanced Rectal Cancer
复制标题
多参数 MRI 的放射组学特征可预测局部晚期直肠癌患者的无病生存期
DOI：
10.1016/j.acra.2021.11.024
复制
发表时间：
2022-08-01
影响因子：
4.8
通讯作者：
Yang, Xiaotang
中科院分区：
文献类型：
--
作者：
Cui, Yanfen;Wang, Guanghui;Yang, Xiaotang
目的：探讨基于术前多参数MRI的放射组学特征在预测局部进展期直肠癌（LARC）患者无病生存期（DFS）中的潜在价值。方法：我们确定了234例LARC患者进行术前MRI检查，包括T2加权、弥散峰度成像和对比增强T1加权。所有患者随机分为训练组（n = 164）和验证组（n = 70）。从上述序列中提取肿瘤的414个特征，然后主要基于特征稳定性和考克斯比例风险模型生成放射组学签名。构建了两个模型，整合术前和术后变量，以验证用于DFS estimation.Results的放射组学签名，由6个DFS相关特征组成，在训练和验证队列中与DFS显著相关（均p < 0.001）。在术前和术后模型中，放射组学特征和MR定义的壁外静脉侵犯（mrEMVI）被确定为DFS的独立预测因子。在两个队列中，两种基于放射学的模型表现出比相应的临床模型更好的预测性能（C指数>= 0.77，所有p < 0.05），具有正的净重新分类改善和较低的Akaike信息标准（AIC）。决策曲线分析也证实了它们的临床实用性。结论：结合术前和术后特征的放射组学模型可预测LARC患者的DFS，为LARC患者的个体化治疗提供有价值的指导。(C)2021大学放射科医师协会。爱思唯尔公司出版All rights reserved.
Objective: To investigate the potential value of radiomics features based on preoperative multiparameter MRI in predicting disease-free survival (DFS) in patients with local advanced rectal cancer (LARC).Methods: We identified 234 patients with LARC who underwent preoperative MRI, including T2-weighted, diffusion kurtosis imaging, and contrast enhanced T1-weighted. All patients were randomly divided into the training (n = 164) and validation (n = 70) cohorts. 414 features were extracted from the tumor from above sequences and the radiomics signature was then generated, mainly based on feature stability and Cox proportional hazards model. Two models, integrating pre- and postoperative variables, were constructed to validate the radiomics signatures for DFS estimation.Results: The radiomics signature, composed of six DFS-related features, was significantly associated with DFS in the training and validation cohorts (both p < 0.001). The radiomics signature and MR-defined extramural venous invasion (mrEMVI) were identified as the independent predictor of DFS both in the pre- and postoperative models. In both cohorts, the two radiomics-based models exhibited better prediction performance (C-index >= 0.77, all p < 0.05) than the corresponding clinical models, with positive net reclassification improvement and lower Akaike information criterion (AIC). Decision curve analysis also confirmed their clinical usefulness. The radiomics-based models could categorize LARC patients into high- and low-risk groups with distinct profiles of DFS (all p < 0.05).Conclusion: The proposed radiomics models with pre- and postoperative features have the potential to predict DFS, and may provide valuable guidance for the future individualized management in patients with LARC. (C) 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.