Table_2_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.

研究目的：本研究的目的是探究基于碘材料分解（MD）图像的放射组学分析在双能量计算机断层扫描（DECT）成像中对于术前预测结直肠癌（CRC）患者微卫星不稳定性（MSI）状态的价值。研究方法：本研究纳入了102名经术后病理证实的CRC患者，其MSI状态通过免疫组化染色进行确认。所有患者均接受了术前DECT成像扫描，扫描设备为Revolution CT或Discovery CT 750HD扫描仪，并在静脉相重建碘材料MD图像。收集并分析了患者的临床、CT报告和放射组学特征。使用Revolution CT扫描仪的数据建立放射组学模型以预测MSI状态（70%的样本随机选择作为训练集，其余样本用于验证）；而使用Discovery CT 750HD扫描仪的数据来测试放射组学模型。选择具有用户间和用户内稳定性的稳定放射组学特征进行后续分析。通过Student's t检验或Mann-Whitney U检验、Spearman等级相关检验、最小-最大标准化、独热编码以及最小绝对收缩和选择算子选择方法进行特征维度降低。建立了多参数逻辑回归模型以预测MSI状态。通过受试者工作特征（ROC）曲线分析评估模型的判别性能；通过校准曲线伴随Hosmer-Lemeshow检验测试校准性能；通过决策曲线分析评估临床实用性。研究结果：最终选取了九个排名靠前的特征来构建放射组学模型。在训练集中，ROC曲线下面积（AUC）为0.961（准确率：0.875；灵敏度：1.000；特异性：0.812）；在验证集中，AUC为0.918（准确率：0.875；灵敏度：0.875；特异性：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；特异性：0.727）。通过所提出的逻辑回归模型生成了基于临床因素和放射组学评分的诺谟图。通过校准分析和决策曲线分析分别观察到良好的校准和临床实用性。研究结论：基于DECT成像的碘材料MD图像的放射组学分析在预测CRC患者MSI状态方面具有巨大的潜力。