DataSheet_1_Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer.docx

ObjectiveTo establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning.MethodsThe clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves.ResultsThe radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801.ConclusionThe radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.

本研究旨在构建并验证一种基于原发性肿瘤特征的放射组学评分系统，以此通过机器学习预测直肠癌术前病理学外周静脉侵犯（EMVI）。研究方法包括对2012年4月至2018年5月间281例原发性直肠癌患者的临床和影像数据进行回顾性分析。所有患者被分别划分为训练集（n = 198）和测试集（n = 83）。从每位患者的增强计算机断层扫描（CT）、T2加权成像（T2WI）和钆增强T1加权成像（CE-TIWI）中提取了原发性肿瘤的放射组学特征。通过对每种模态图像进行降维后，采用受试者工作特征（ROC）曲线分析生成每个模态图像中的一个最佳放射组学特征。基于训练集构建了三种模型，包括临床模型（结合最佳放射组学特征的临床特征）、磁共振成像模型（结合最佳放射组学特征与mrEMVI状态）和综合模型（结合最佳放射组学特征、临床特征及mrEMVI状态）。最终，通过ROC曲线和决策曲线分析曲线验证了评分系统的临床效能。结果发现，基于T2WI构建的放射组学特征表现出最佳性能，其AUC值为0.717，敏感性为0.742，特异性为0.621。放射组学评分系统具有较高的预测效率，其中AUC为0.863，敏感性为0.774，特异性为0.801。结论表明，放射组学评分系统在预测EMVI方面具有最高效率，这有助于患者选择最佳治疗方案，并可能加强个性化治疗方法，以进一步优化治疗效果。