BACKGROUND This study aimed to evaluate the significance of MRI-based radiomics model derived from high-resolution T2-weighted images (T2WIs) in predicting tumor pathological features of rectal cancer. METHODS A total of 152 patients with rectal cancer who underwent surgery without any neoadjuvant therapy between March 2017 and September 2018 were included retrospectively. The patients were scanned using a 3-T magnetic resonance imaging, and high-resolution T2WIs were obtained. Lesions were delineated, and 1029 radiomics features were extracted. Least absolute shrinkage and selection operator was used to select features, and multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) were trained using fivefold cross-validation to build a prediction model. The diagnostic performance of the prediction models was assessed using the receiver operating characteristic curves. RESULTS A total of 1029 features were extracted, and 15, 11, and 11 features were selected to predict the degree of differentiation, T stage, and N stage, respectively. The best performance of the radiomics model for the degree of differentiation, T stage, and N stage was obtained by SVM [area under the curve (AUC), 0.862; 95% confidence interval (CI), 0.750-0.967; sensitivity, 83.3%; specificity, 85.0%], MLP (AUC, 0.809; 95% CI, 0.690-0.905; sensitivity, 76.2%; specificity, 74.1%), and RF (AUC, 0.746; 95% CI, 0.622-0.872; sensitivity, 79.3%; specificity, 72.2%). CONCLUSION This study demonstrated that the high-resolution T2WI-based radiomics model could serve as pretreatment biomarkers in predicting pathological features of rectal cancer.

中文翻译：

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基于MRI的直肠癌放射物：术前评估病理特征。

背景技术本研究旨在评估从高分辨率T2加权图像（T2WI）衍生的基于MRI的放射学模型在预测直肠癌的肿瘤病理特征中的意义。方法回顾性分析2017年3月至2018年9月间未经手术治疗的152例直肠癌患者的临床资料。使用3-T磁共振成像对患者进行了扫描，并获得了高分辨率的T2WI。划定病变部位，并提取1029个放射学特征。使用最小绝对收缩和选择算子来选择特征，以及多层感知器（MLP），逻辑回归（LR），支持向量机（SVM），决策树（DT），随机森林（RF），和K近邻（KNN）使用五重交叉验证进行训练，以建立预测模型。使用接收器工作特性曲线评估了预测模型的诊断性能。结果共提取了1029个特征，分别选择了15个，11个和11个特征来预测分化程度，T期和N期。通过SVM [曲线下面积（AUC）为0.862；在曲线下面积（AUC），可获得最佳的放射学模型的分化程度，T阶段和N阶段。95％置信区间（CI），0.750-0.967；灵敏度为83.3％；特异性（85.0％），MLP（AUC，0.809; 95％CI，0.690-0.905;敏感性，76.2％;特异性，74.1％）和RF（AUC，0.746; 95％CI，0.622-0.872;敏感性，79.3％ ；特异性为72.2％）。