Lin Zijing, Wang Ting, Li Haiming, Xiao Meiling, Ma Xiaoliang, Gu Yajia, Qiang Jinwei
Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Quant Imaging Med Surg. 2023 Jan 1;13(1):108-120. doi: 10.21037/qims-22-255. Epub 2022 Oct 19.
Microsatellite instability (MSI) status is an important indicator for screening patients with endometrial cancer (EC) who have potential Lynch syndrome (LS) and may benefit from immunotherapy. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics nomogram for the prediction of MSI status in EC.
A total of 296 patients with histopathologically diagnosed EC were enrolled, and their MSI status was determined using immunohistochemical (IHC) analysis. Patients were randomly divided into the training cohort (n=236) and the validation cohort (n=60) at a ratio of 8:2. To predict the MSI status in EC, the tumor radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images, which in turn were selected using one-way analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) algorithm to build the radiomics signature (radiomics score; radscore) model. Five clinicopathologic characteristics were used to construct a clinicopathologic model. Finally, the nomogram model combining radscore and clinicopathologic characteristics was constructed. The performance of the three models was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA).
Totals of 21 radiomics features and five clinicopathologic characteristics were selected to develop the radscore and clinicopathological models. The radscore and clinicopathologic models achieved an area under the curve (AUC) of 0.752 and 0.600, respectively, in the training cohort; and of 0.723 and 0.615, respectively, in the validation cohort. The radiomics nomogram model showed improved discrimination efficiency compared with the radscore and clinicopathologic models, with an AUC of 0.773 and 0.740 in the training and validation cohorts, respectively. The calibration curve analysis and DCA showed favorable calibration and clinical utility of the nomogram model.
The nomogram incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for the prediction of MSI status in EC.
微卫星不稳定性（MSI）状态是筛查子宫内膜癌（EC）患者的重要指标，这些患者可能患有潜在的林奇综合征（LS）并可能从免疫治疗中获益。本研究旨在开发一种基于磁共振成像（MRI）的影像组学列线图，用于预测EC中的MSI状态。
共纳入296例经组织病理学诊断为EC的患者，并使用免疫组织化学（IHC）分析确定其MSI状态。患者以8:2的比例随机分为训练队列（n = 236）和验证队列（n = 60）。为了预测EC中的MSI状态，从T2加权图像和对比增强T1加权图像中提取肿瘤影像组学特征，然后使用单因素方差分析（ANOVA）和最小绝对收缩和选择算子（LASSO）算法进行选择，以建立影像组学特征（影像组学评分；radscore）模型。使用五个临床病理特征构建临床病理模型。最后，构建结合radscore和临床病理特征的列线图模型。使用受试者工作特征（ROC）、校准和决策曲线分析（DCA）评估这三种模型的性能。
共选择21个影像组学特征和五个临床病理特征来开发radscore和临床病理模型。radscore和临床病理模型在训练队列中的曲线下面积（AUC）分别为0.752和0.600；在验证队列中分别为0.723和0.615。与radscore和临床病理模型相比，影像组学列线图模型显示出更高的鉴别效率，在训练和验证队列中的AUC分别为0.773和0.740。校准曲线分析和DCA显示列线图模型具有良好的校准和临床实用性。
结合基于MRI的影像组学特征和临床病理特征的列线图可能是预测EC中MSI状态的潜在工具。
