Feasibility of a CT-based lymph node radiomics nomogram in detecting lymph node metastasis in PDAC patients - Li, Qian; Song, Zuhua; Zhang, Dan; Li, Xiaojiao; Liu, Qian; Yu, Jiayi; Li, Zongwen; Zhang, Jiayan; Ren, Xiaofang; Wen, Youjia; Tang, Zhuoyue (Frontiers in Oncology, 2022)
Objectives: To investigate the potential value of contrast enhanced computed tomography (CECT)-based radiological-radiomics nomogram combining lymph node (LN) radiomics signature and LN radiological features for preoperatively detecting LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods: In this retrospective study, 196 LNs from 61 PDAC patients were enrolled and divided into training (137 LNs) and validation cohort (59 LNs). Radiomics features were extracted from portal venous phase images of LNs, the least absolute shrinkage and selection operator (LASSO) regression algorithm with 10-fold cross-validation was used to select the optimal features to establish the radiomics score (Rad-score). The radiological-radiomics nomogram was developed by using significant predictors of LN metastasis via multivariate logistic regression (LR) analysis in the training cohort and validated in the validation cohort independently. Its diagnostic performance was assessed by the receiver operating characteristic curve (ROC), decision curve analysis (DCA) and calibration curve. Results: The radiological-radiomics nomogram, which incorporated LN Rad-score and three LN radiological features, performed better than Rad-score model and radiological model in preoperative diagnosis of LN metastasis in PADC patients. The area under the ROC curve (AUC) of the nomogram was 0.937 in the training cohort and 0.851 in the validation cohort. The calibration curve and DCA revealed that the radiological-radiomics nomogram showed satisfactory consistency and the most net benefit for preoperative diagnosis of LN metastasis. Conclusions: The CT-based LN radiological-radiomics nomogram may serve as a valid and convenient computer-aided tool for personalized risk assessment of LN metastasis and help first-line clinicians make clinical decisions for PADC patients.