开发和验证基于对比增强计算机断层扫描 (CECT) 的放射组学列线图，用于术前评估胰腺导管腺癌 (PDAC) 中的 Ki-67 增殖状态。 在这项两中心回顾性研究中，共有 181 名患者（95包括接受 CECT 检查的 PDAC 患者（训练队列中 42 名，测试队列中 42 名，外部验证队列中 44 名）。从门静脉期图像中提取放射组学特征。放射组学特征是通过使用两种特征选择方法（救济和递归特征消除）和四种分类器（支持向量机、朴素贝叶斯、线性判别分析（LDA）和逻辑回归（LR））构建的。使用多变量 LR 建立临床模型和放射组学-临床列线图。使用受试者工作特征曲线下面积 (AUC) 和决策曲线分析 (DCA) 评估模型的预测性能。使用 12 个特征的缓解选择器和 LDA 分类器构建了最佳放射组学特征，AUC 分别为 0.948、0.927 和 0.824分别在训练、测试和外部验证队列中。结合了最佳放射组学特征、CT 报告的淋巴结状态和 CA19-9 的放射组学临床列线图显示出更好的预测性能，在训练、测试和外部验证队列中 AUC 分别为 0.976、0.955 和 0.882。校准曲线和 DCA 证明了列线图的拟合优度并提高了其在临床实践中的益处。放射组学-临床列线图是预测 PDAC 患者 Ki-67 表达状态的有效且非侵入性的计算机辅助工具。放射组学-临床列线图是预测胰腺导管腺癌患者Ki-67表达状态的有效且无创的计算机辅助工具。放射组学分析有助于预测胰腺导管腺癌患者Ki-67表达状态（PDAC）。放射组学-临床列线图与放射组学特征、临床数据和 CT 放射学特征相结合，可以显着提高 Ki-67 表达状态的鉴别诊断。放射组学-临床列线图显示了令人满意的校准和区分 PDAC 中 Ki-67 高低表达状态的净效益。© 2023。作者获得欧洲放射学会的独家许可。

To develop and validate a contrast-enhanced computed tomography (CECT)-based radiomics nomogram for the preoperative evaluation of Ki-67 proliferation status in pancreatic ductal adenocarcinoma (PDAC).In this two-center retrospective study, a total of 181 patients (95 in the training cohort; 42 in the testing cohort, and 44 in the external validation cohort) with PDAC who underwent CECT examination were included. Radiomic features were extracted from portal venous phase images. The radiomics signatures were built by using two feature-selecting methods (relief and recursive feature elimination) and four classifiers (support vector machine, naive Bayes, linear discriminant analysis (LDA), and logistic regression (LR)). Multivariate LR was used to build a clinical model and radiomics-clinical nomogram. The predictive performances of the models were evaluated using area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA).The relief selector and LDA classifier using twelve features built the optimal radiomics signature, with AUCs of 0.948, 0.927, and 0.824 in the training, testing, and external validation cohorts, respectively. The radiomics-clinical nomogram incorporating the optimal radiomics signature, CT-reported lymph node status, and CA19-9 showed better predictive performance with AUCs of 0.976, 0.955, and 0.882 in the training, testing, and external validation cohorts, respectively. The calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice of the nomogram.The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with PDAC.The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with pancreatic ductal adenocarcinoma.The radiomics analysis could be helpful to predict Ki-67 expression status in patients with pancreatic ductal adenocarcinoma (PDAC). The radiomics-clinical nomogram integrated with the radiomics signature, clinical data, and CT radiological features could significantly improve the differential diagnosis of Ki-67 expression status. The radiomics-clinical nomogram showed satisfactory calibration and net benefit for discriminating high and low Ki-67 expression status in PDAC.© 2023. The Author(s), under exclusive licence to European Society of Radiology.