Objectives: 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).
Methods: 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).
Results: 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.
Conclusions: The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with PDAC.
Clinical relevance statement: 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.
Key points: 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.
Keywords: Ki-67; Machine learning; Nomogram; Pancreatic ductal adenocarcinoma; Radiomics.
© 2023. The Author(s), under exclusive licence to European Society of Radiology.