本研究旨在利用临床变量和超声放射组学特征构建机器学习模型来预测胰腺肿瘤的良恶性。 纳入了 2020 年 1 月至 6 月期间在广西医科大学第一附属医院住院的 242 名胰腺肿瘤患者2023年被纳入这项回顾性研究。患者被随机分为训练组（n=169）和测试组（n=73）。我们收集了患者的 28 个临床特征。同时，从患者肿瘤的超声图像中提取了 306 个放射组学特征。最初，使用逻辑回归算法构建临床模型。随后，使用 SVM、随机森林、XGBoost 和 KNN 算法构建放射组学模型。最后，我们将临床特征与应用放射组学模型计算出的新特征RAD概率相结合，构建融合模型，并基于融合模型开发列线图。融合模型的性能优于临床模型和放射组学模型。在训练队列中，融合模型在 5 倍交叉验证期间实现了 0.978（95% CI：0.96-0.99）的 AUC，在测试队列中实现了 0.925（95% CI：0.86-0.98）的 AUC。校准曲线和决策曲线分析表明，融合模型构建的列线图具有较高的准确性和临床实用性。包含临床和超声放射组学特征的融合模型在预测胰腺肿瘤的良恶性方面表现出优异的性能。Copyright © 2024 Yao,姚、黄、秦、陈。

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.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.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.The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.Copyright © 2024 Yao, Yao, Huang, Qin and Chen.