Table_1_Radiomics Analysis of Iodine-Based Material Decomposition Images With Dual-Energy Computed Tomography Imaging for Preoperatively Predicting Microsatellite Instability Status in Colorectal Cancer.DOCX

Purpose: The aim of this study was to investigate the value of radiomics analysis of iodine-based material decomposition (MD) images with dual-energy computed tomography (DECT) imaging for preoperatively predicting microsatellite instability (MSI) status in colorectal cancer (CRC).
Methods: This study included 102 CRC patients proved by postoperative pathology, and their MSI status was confirmed by immunohistochemistry staining. All patients underwent preoperative DECT imaging scanned on either a Revolution CT or Discovery CT 750HD scanner, and the iodine-based MD images in the venous phase were reconstructed. The clinical, CT-reported, and radiomics features were obtained and analyzed. Data from the Revolution CT scanner were used to establish a radiomics model to predict MSI status (70% samples were randomly selected as the training set, and the remaining samples were used to validate); and data from the Discovery CT 750HD scanner were used to test the radiomics model. The stable radiomics features with both inter-user and intra-user stability were selected for the next analysis. The feature dimension reduction was performed by using Student's t-test or Mann–Whitney U-test, Spearman's rank correlation test, min–max standardization, one-hot encoding, and least absolute shrinkage and selection operator selection method. The multiparameter logistic regression model was established to predict MSI status. The model performances were evaluated: The discrimination performance was accessed by receiver operating characteristic (ROC) curve analysis; the calibration performance was tested by calibration curve accompanied by Hosmer–Lemeshow test; the clinical utility was assessed by decision curve analysis.
Results: Nine top-ranked features were finally selected to construct the radiomics model. In the training set, the area under the ROC curve (AUC) was 0.961 (accuracy: 0.875; sensitivity: 1.000; specificity: 0.812); in the validation set, the AUC was 0.918 (accuracy: 0.875; sensitivity: 0.875; specificity: 0.857). In the testing set, the diagnostic performance was slightly lower with AUC of 0.875 (accuracy: 0.788; sensitivity: 0.909; specificity: 0.727). A nomogram based on clinical factors and radiomics score was generated via the proposed logistic regression model. Good calibration and clinical utility were observed using the calibration and decision curve analyses, respectively.
Conclusion: Radiomics analysis of iodine-based MD images with DECT imaging holds great potential to predict MSI status in CRC patients.

本研究旨在探讨双能计算机断层扫描（dual-energy computed tomography, DECT）获取的碘基物质分解（material decomposition, MD）图像的放射组学分析，在结直肠癌（colorectal cancer, CRC）患者术前预测微卫星不稳定（microsatellite instability, MSI）状态中的应用价值。
本研究纳入102例经术后病理证实的CRC患者，其MSI状态经免疫组化染色确认。所有患者术前均接受DECT扫描，扫描设备为Revolution CT或Discovery CT 750HD，并重建获得静脉期碘基MD图像。提取并分析临床特征、CT报告特征及放射组学特征。以Revolution CT设备的数据构建预测MSI状态的放射组学模型：将70%的样本随机划分为训练集，剩余样本作为验证集；以Discovery CT 750HD设备的数据对该放射组学模型进行测试。筛选同时具备操作者间与操作者内稳定性的稳定放射组学特征用于后续分析。通过t检验或曼-惠特尼U检验、斯皮尔曼秩相关检验、最小-最大标准化、独热编码以及最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）选择法进行特征降维。构建多参数逻辑回归模型以预测MSI状态。对模型性能进行评估：采用受试者工作特征（receiver operating characteristic, ROC）曲线分析评估判别性能；采用校准曲线结合霍斯默-莱梅肖检验评估校准性能；采用决策曲线分析评估临床实用性。
最终筛选出9个排名靠前的特征用于构建放射组学模型。在训练集中，受试者工作特征曲线下面积（area under the ROC curve, AUC）为0.961（准确率：0.875；灵敏度：1.000；特异度：0.812）；在验证集中，AUC为0.918（准确率：0.875；灵敏度：0.875；特异度：0.857）。在测试集中，诊断性能略有下降，AUC为0.875（准确率：0.788；灵敏度：0.909；特异度：0.727）。基于临床因素与放射组学评分，通过所提出的逻辑回归模型生成了列线图。校准曲线与决策曲线分析分别显示模型具备良好的校准性能与临床实用性。
基于DECT的碘基MD图像放射组学分析在预测CRC患者MSI状态中具有巨大应用潜力。