由于相似的临床表现和影像学征象，原发性肠淋巴瘤（PIL）和克罗恩病（CD）的鉴别诊断在临床实践中是一个挑战。为了探讨放射组学结合机器学习方法区分PIL和CD的能力。我们收集了对比增强计算机断层扫描 (CECT) 和来自中心 1 120 名患者的临床数据。CECT 扫描的单相图像总共提取了 944 个特征。使用最后的绝对收缩和选择算子模型，筛选最佳的预测放射学特征和临床适应症。中心 2 收集了 54 名患者的数据作为外部验证集，以验证模型的稳健性。采用受试者工作特征曲线下面积、准确度、灵敏度和特异度进行评价。共建立了5个机器学习模型来区分PIL和CD。从测试组的结果来看，大多数模型都表现良好，曲线下面积（AUC）（> 0.850）和高精度（> 0.900）。临床和放射组学相结合的模型（AUC = 1.000，准确度 = 1.000）是所有模型中最好的模型。基于机器学习，构建了结合临床数据和放射学特征的模型，可以有效区分 PIL 和 CD。©作者（ s) 2024。百事登出版集团有限公司出版。保留所有权利。

Due to similar clinical manifestations and imaging signs, differential diagnosis of primary intestinal lymphoma (PIL) and Crohn's disease (CD) is a challenge in clinical practice.To investigate the ability of radiomics combined with machine learning methods to differentiate PIL from CD.We collected contrast-enhanced computed tomography (CECT) and clinical data from 120 patients form center 1. A total of 944 features were extracted single-phase images of CECT scans. Using the last absolute shrinkage and selection operator model, the best predictive radiographic features and clinical indications were screened. Data from 54 patients were collected at center 2 as an external validation set to verify the robustness of the model. The area under the receiver operating characteristic curve, accuracy, sensitivity and specificity were used for evaluation.A total of five machine learning models were built to distinguish PIL from CD. Based on the results from the test group, most models performed well with a large area under the curve (AUC) (> 0.850) and high accuracy (> 0.900). The combined clinical and radiomics model (AUC = 1.000, accuracy = 1.000) was the best model among all models.Based on machine learning, a model combining clinical data with radiologic features was constructed that can effectively differentiate PIL from CD.©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.