PDF(Pubmed)
Abstract：
UNASSIGNED: The purpose of this study was to develop and validate an CT-based radiomics nomogram for the preoperative differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma.
UNASSIGNED: 96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study (32 and 64 cases respectively). All cases have been confirmed by imaging, clinical follow-up and/or pathology. The imaging data were considered as: 70% training cohort and 30% test cohort. Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images. Independent-sample T tests and LASSO regression were used for feature selection. The training cohort was classified using a variety of machine learning-based classifiers, and 5-fold cross-validation has been performed. The classification performance was evaluated using the test cohort. Multivariate logistic regression analysis was then used to develop a radiomics nomogram model, containing the CT findings and Rad-Score. Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model. Different patients have been selected to test and evaluate the model prediction process. Finally, receiver operating characteristic curves and decision curves were plotted, and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability.
UNASSIGNED: A total of 158 radiomics features were extracted from each image. 7 features were selected to construct the radiomics model, then a variety of classifiers were used for classification and multinomial logistic regression (MLR) was selected to be the optimal classifier. Combining CT findings with radiomics model, a prediction model based on CT findings and radiomics was finally obtained. The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts, respectively. The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually.
UNASSIGNED: The CT image-based radiomics nomogram model can accurately distinguish between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma patients and provide additional clinical benefits.
UNASSIGNED: 96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study (32 and 64 cases respectively). All cases have been confirmed by imaging, clinical follow-up and/or pathology. The imaging data were considered as: 70% training cohort and 30% test cohort. Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images. Independent-sample T tests and LASSO regression were used for feature selection. The training cohort was classified using a variety of machine learning-based classifiers, and 5-fold cross-validation has been performed. The classification performance was evaluated using the test cohort. Multivariate logistic regression analysis was then used to develop a radiomics nomogram model, containing the CT findings and Rad-Score. Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model. Different patients have been selected to test and evaluate the model prediction process. Finally, receiver operating characteristic curves and decision curves were plotted, and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability.
UNASSIGNED: A total of 158 radiomics features were extracted from each image. 7 features were selected to construct the radiomics model, then a variety of classifiers were used for classification and multinomial logistic regression (MLR) was selected to be the optimal classifier. Combining CT findings with radiomics model, a prediction model based on CT findings and radiomics was finally obtained. The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts, respectively. The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually.
UNASSIGNED: The CT image-based radiomics nomogram model can accurately distinguish between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma patients and provide additional clinical benefits.
摘要：
UNASSIGNED：本研究的目的是开发和验证基于CT的影像组学列线图，用于局灶性自身免疫性胰腺炎与胰腺导管腺癌的术前鉴别。
UNASSIGNED：96例局灶性自身免疫性胰腺炎和胰腺导管腺癌患者已纳入研究（分别为32例和64例）。所有病例均已通过影像学证实,临床随访和/或病理。成像数据被认为是:70%训练队列和30%测试队列。两名放射科医生手动描绘了胰腺病变，并进行了图像分割以从CT图像中提取影像组学特征。独立样本T检验和LASSO回归用于特征选择。训练队列使用各种基于机器学习的分类器进行分类，并进行了5倍交叉验证。使用测试队列评估分类性能。然后使用多变量逻辑回归分析来开发放射组学列线图模型，包含CT检查结果和Rad评分。已绘制了校准曲线，显示了影像组学列线图模型的预测概率和实际概率之间的一致性。选择了不同的患者来测试和评估模型预测过程。最后,绘制了接收机工作特性曲线和决策曲线，并将影像组学列线图模型与单一模型进行比较，以直观评估其诊断能力。
UNASSIGNED：从每张图像中总共提取了158个影像组学特征。选择了7个特征来构建影像组学模型，然后使用多种分类器进行分类，并选择多项逻辑回归（MLR）作为最佳分类器。将CT检查结果与影像组学模型相结合，最终获得了基于CT检查结果和影像组学的预测模型.列线图模型在训练和测试队列中显示出良好的敏感性和特异性，AUC分别为0.87和0.83。分别。曲线下面积和决策曲线分析表明，放射组学列线图模型可以提供比单一模型更好的诊断性能，并分别比CT发现模型和放射组学签名模型实现更大的临床净收益。
UNASSIGNED：基于CT图像的影像组学列线图模型可以准确区分局灶性自身免疫性胰腺炎和胰腺导管腺癌患者，并提供额外的临床益处。
UNASSIGNED：96例局灶性自身免疫性胰腺炎和胰腺导管腺癌患者已纳入研究（分别为32例和64例）。所有病例均已通过影像学证实,临床随访和/或病理。成像数据被认为是:70%训练队列和30%测试队列。两名放射科医生手动描绘了胰腺病变，并进行了图像分割以从CT图像中提取影像组学特征。独立样本T检验和LASSO回归用于特征选择。训练队列使用各种基于机器学习的分类器进行分类，并进行了5倍交叉验证。使用测试队列评估分类性能。然后使用多变量逻辑回归分析来开发放射组学列线图模型，包含CT检查结果和Rad评分。已绘制了校准曲线，显示了影像组学列线图模型的预测概率和实际概率之间的一致性。选择了不同的患者来测试和评估模型预测过程。最后,绘制了接收机工作特性曲线和决策曲线，并将影像组学列线图模型与单一模型进行比较，以直观评估其诊断能力。
UNASSIGNED：从每张图像中总共提取了158个影像组学特征。选择了7个特征来构建影像组学模型，然后使用多种分类器进行分类，并选择多项逻辑回归（MLR）作为最佳分类器。将CT检查结果与影像组学模型相结合，最终获得了基于CT检查结果和影像组学的预测模型.列线图模型在训练和测试队列中显示出良好的敏感性和特异性，AUC分别为0.87和0.83。分别。曲线下面积和决策曲线分析表明，放射组学列线图模型可以提供比单一模型更好的诊断性能，并分别比CT发现模型和放射组学签名模型实现更大的临床净收益。
UNASSIGNED：基于CT图像的影像组学列线图模型可以准确区分局灶性自身免疫性胰腺炎和胰腺导管腺癌患者，并提供额外的临床益处。
