PDF(Pubmed)
Abstract：
UNASSIGNED: This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors.
UNASSIGNED: 242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients\' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model.
UNASSIGNED: The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility.
UNASSIGNED: The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.
UNASSIGNED: 242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients\' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model.
UNASSIGNED: The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility.
UNASSIGNED: The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.
摘要：
■本研究旨在使用临床变量和超声影像组学特征来构建机器学习模型，以预测胰腺肿瘤的良性或恶性性质。
■2020年1月至2023年6月在广西医科大学第一附属医院住院的242例胰腺肿瘤患者纳入本回顾性研究。将患者随机分为训练队列（n=169）和测试队列（n=73）。我们收集了28例患者的临床特征。同时,从患者肿瘤的超声图像中提取了306个影像组学特征。最初，使用逻辑回归算法构建临床模型.随后，使用SVM建立影像组学模型，随机森林,XGBoost,和KNN算法。最后,我们将临床特征与应用影像组学模型计算的新特征RADprob相结合，构建融合模型，并基于融合模型开发了列线图。
■融合模型的性能超过了临床和影像组学模型。在训练组中，融合模型在5倍交叉验证中的AUC为0.978(95%CI:0.96~0.99),在试验队列中的AUC为0.925(95%CI:0.86~0.98).校准曲线和决策曲线分析表明，由融合模型构建的列线图具有较高的准确性和临床实用性。
■包含临床和超声影像组学特征的融合模型在预测胰腺肿瘤的良性或恶性性质方面表现出出色的性能。
■2020年1月至2023年6月在广西医科大学第一附属医院住院的242例胰腺肿瘤患者纳入本回顾性研究。将患者随机分为训练队列（n=169）和测试队列（n=73）。我们收集了28例患者的临床特征。同时,从患者肿瘤的超声图像中提取了306个影像组学特征。最初，使用逻辑回归算法构建临床模型.随后，使用SVM建立影像组学模型，随机森林,XGBoost,和KNN算法。最后,我们将临床特征与应用影像组学模型计算的新特征RADprob相结合，构建融合模型，并基于融合模型开发了列线图。
■融合模型的性能超过了临床和影像组学模型。在训练组中，融合模型在5倍交叉验证中的AUC为0.978(95%CI:0.96~0.99),在试验队列中的AUC为0.925(95%CI:0.86~0.98).校准曲线和决策曲线分析表明，由融合模型构建的列线图具有较高的准确性和临床实用性。
■包含临床和超声影像组学特征的融合模型在预测胰腺肿瘤的良性或恶性性质方面表现出出色的性能。
