Department of Radiology, Changhai Hospital, No. 168 Changhai Road, Shanghai, China.
Huiying Medical Technology Co., Ltd., B2, Dongsheng Science and Technology Park, HaiDian District, Beijing, China.
Biomed Res Int. 2021 Jul 10;2021:5566885. doi: 10.1155/2021/5566885. eCollection 2021.
The manual delineation of the lesion is mainly used as a conventional segmentation method, but it is subjective and has poor stability and repeatability. The purpose of this study is to validate the effect of a radiomics model based on MRI derived from two delineation methods in the preoperative T staging of patients with rectal cancer (RC). A total of 454 consecutive patients with pathologically confirmed RC who underwent preoperative MRI between January 2018 and December 2019 were retrospectively analyzed. RC patients were grouped according to whether the muscularis propria was penetrated. Two radiologists segmented lesions, respectively, by minimum delineation (Method 1) and maximum delineation (Method 2), after which radiomics features were extracted. Inter- and intraclass correlation coefficient (ICC) of all features was evaluated. After feature reduction, the support vector machine (SVM) was trained to build a prediction model. The diagnostic performances of models were determined by receiver operating characteristic (ROC) curves. Then, the areas under the curve (AUCs) were compared by the DeLong test. Decision curve analysis (DCA) was performed to evaluate clinical benefit. Finally, 317 patients were assessed, including 152 cases in the training set and 165 cases in the validation set. Moreover, 1288/1409 (91.4%) features of Method 1 and 1273/1409 (90.3%) features of Method 2 had good robustness ( < 0.05). The AUCs of Model 1 and Model 2 were 0.808 and 0.903 in the validation set, respectively ( = 0.035). DCA showed that the maximum delineation yielded more net benefit. MRI-based radiomics models derived from two segmentation methods demonstrated good performance in the preoperative T staging of RC. The minimum delineation had better stability in feature selection, while the maximum delineation method was more clinically beneficial.
手动勾画病变主要作为一种传统的分割方法，但它具有主观性，并且稳定性和可重复性差。本研究旨在验证基于 MRI 的两种勾画方法的放射组学模型在直肠癌（RC）患者术前 T 分期中的效果。回顾性分析了 2018 年 1 月至 2019 年 12 月期间接受术前 MRI 检查且病理证实为 RC 的 454 例连续患者。RC 患者根据是否穿透肌层进行分组。两名放射科医生分别采用最小勾画（方法 1）和最大勾画（方法 2）对病灶进行勾画，然后提取放射组学特征。评估所有特征的组内和组间相关系数（ICC）。经过特征降维后，使用支持向量机（SVM）构建预测模型。通过受试者工作特征（ROC）曲线确定模型的诊断性能。然后通过 DeLong 检验比较曲线下面积（AUC）。采用决策曲线分析（DCA）评估临床获益。最后，评估了 317 例患者，其中 152 例在训练集中，165 例在验证集中。此外，方法 1 的 1288/1409（91.4%）特征和方法 2 的 1273/1409（90.3%）特征具有良好的稳健性（<0.05）。验证集中模型 1 和模型 2 的 AUC 分别为 0.808 和 0.903（=0.035）。DCA 表明，最大勾画产生了更多的净获益。基于 MRI 的放射组学模型源自两种分割方法，在 RC 术前 T 分期中表现出良好的性能。最小勾画在特征选择中具有更好的稳定性，而最大勾画方法更具临床益处。
