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Abstract：
This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn\'s disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on the validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learning (DL) radiomics features were extracted from the arterial and venous phases images, respectively. The interobserver consistency analysis, Spearman\'s correlation, univariate analysis, and the least absolute shrinkage and selection operator (LASSO) regression were used to select features. Based on extracted multi-phase radiomics features, six logistic regression models were finally constructed. The diagnostic performances of different models were compared using ROC analysis and Delong test. The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality with AUCs of 0.885, 0.877, and 0.800 in SMOTE dataset, validation dataset one, and validation dataset two, respectively. Moreover, the deep learning radiomics model outperformed the handcrafted radiomics model in same phase images. In validation dataset one, the Delong test results indicated that there was a significant difference in the AUC of the arterial models (p = 0.037), while not in venous and arterial-venous combined models (p = 0.398 and p = 0.265) as comparing deep learning radiomics models and handcrafted radiomics models. In our study, the arterial-venous combined model based on deep learning radiomics analysis exhibited good performance in differentiating between CD and ITB.
摘要：
本研究旨在开发和评估基于CT的深度学习影像组学模型，以区分克罗恩病（CD）和肠结核（ITB）。将郑州大学第一附属医院330例经病理证实为CD或ITB的患者分为验证数据集1（CD：167；ITB：57）和验证数据集2（CD：78；ITB：28）。基于验证数据集1，采用合成少数过采样技术（SMOTE）创建平衡数据集作为特征选择和模型构建的训练数据。从动脉和静脉阶段图像中提取了手工制作和深度学习（DL）的影像组学特征，分别。观察者间一致性分析，斯皮尔曼的相关性，单变量分析，并使用最小绝对收缩和选择算子(LASSO)回归来选择特征。基于提取的多相影像组学特征，最后构建了六个logistic回归模型。使用ROC分析和Delong检验比较不同模型的诊断性能。用于区分CD和ITB的动静脉联合深度学习影像组学模型显示出很高的预测质量，在SMOTE数据集中的AUC为0.885、0.877和0.800。验证数据集1，和验证数据集2，分别。此外，深度学习影像组学模型在相同相位图像中优于手工制作的影像组学模型。在验证数据集一，Delong检验结果表明，动脉模型的AUC存在显着差异（p=0.037），而不是在静脉和动静脉联合模型(p=0.398和p=0.265)中，比较深度学习影像组学模型和手工制作的影像组学模型。在我们的研究中,基于深度学习影像组学分析的动静脉联合模型在区分CD和ITB方面表现良好.
