肠直肠腺癌外周静脉侵犯（EMVI）是其预后的重要因素，但EMVI的准确术前评估仍然困难。为了通过放射组学技术预先评估EMVI，并使用不同的算法结合临床因素建立多种模型，以在手术前做出最准确的判断。研究共纳入212例2012年9月至2019年7月之间的直肠腺癌患者，并将其分配到训练和验证数据集。从治疗前T2加权图像中提取放射组学特征。基于放射组学特征和临床因素分别构建了不同的预测模型（临床模型、逻辑回归[LR]、随机森林[RF]、支持向量机[SVM]、临床-LR模型、临床-RF模型和临床-SVM模型）。采用曲线下面积（AUC）和准确度评估不同模型的预测效果。还计算了敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。临床-LR模型表现出最佳的诊断效率，训练数据集的AUC为0.962（95％置信区间[CI] = 0.936-0.988），准确度为0.899，敏感度为0.867，特异度为0.913，PPV为0.813，NPV为0.940，验证数据集的AUC为0.865（95％ CI = 0.770-0.959），准确度为0.828，敏感度为0.818，特异度为0.833，PPV为0.720，NPV为0.897。基于放射组学的预测模型是EMVI检测的有价值工具，可以辅助临床实践中的决策制定。

Extramural venous invasion (EMVI) is an important prognostic factor of rectal adenocarcinoma. However, accurate preoperative assessment of EMVI remains difficult.To assess EMVI preoperatively through radiomics technology, and use different algorithms combined with clinical factors to establish a variety of models in order to make the most accurate judgments before surgery.A total of 212 patients with rectal adenocarcinoma between September 2012 and July 2019 were included and distributed to training and validation datasets. Radiomics features were extracted from pretreatment T2-weighted images. Different prediction models (clinical model, logistic regression [LR], random forest [RF], support vector machine [SVM], clinical-LR model, clinical-RF model, and clinical-SVM model) were constructed on the basis of radiomics features and clinical factors, respectively. The area under the curve (AUC) and accuracy were used to assess the predictive efficacy of different models. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated.The clinical-LR model exhibited the best diagnostic efficiency with an AUC of 0.962 (95% confidence interval [CI] = 0.936-0.988) and 0.865 (95% CI = 0.770-0.959), accuracy of 0.899 and 0.828, sensitivity of 0.867 and 0.818, specificity of 0.913 and 0.833, PPV of 0.813 and 0.720, and NPV of 0.940 and 0.897 for the training and validation datasets, respectively.The radiomics-based prediction model is a valuable tool in EMVI detection and can assist decision-making in clinical practice.