Feasibility of a CT-based lymph node radiomics nomogram in detecting lymph node metastasis in PDAC patients

Qian Li; Zuhua Song; Dan Zhang; Xiaojiao Li; Qian Liu; Jiayi Yu; Zongwen Li; Jiayan Zhang; Xiaofang Ren; Youjia Wen; Zhuoyue Tang

doi:10.3389/fonc.2022.992906

Feasibility of a CT-based lymph node radiomics nomogram in detecting lymph node metastasis in PDAC patients

Front Oncol. 2022 Oct 5:12:992906. doi: 10.3389/fonc.2022.992906. eCollection 2022.

Authors

Qian Li^{1

2

3

4}, Zuhua Song⁴, Dan Zhang⁴, Xiaojiao Li⁴, Qian Liu⁴, Jiayi Yu⁴, Zongwen Li⁴, Jiayan Zhang⁴, Xiaofang Ren⁴, Youjia Wen⁴, Zhuoyue Tang^{1

2

3

4}

Affiliations

¹ Department of Radiology, Chongqing Medical University, Chongqing, China.
² Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China.
³ Chongqing School, University of Chinese Academy of Sciences, Chongqing, China.
⁴ Department of Radiology, Chongqing General Hospital, Chongqing, China.

Abstract

Objectives: To investigate the potential value of a contrast enhanced computed tomography (CECT)-based radiological-radiomics nomogram combining a lymph node (LN) radiomics signature and LNs' radiological features for preoperative detection of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC).

Materials and methods: In this retrospective study, 196 LNs in 61 PDAC patients were enrolled and divided into the training (137 LNs) and validation (59 LNs) cohorts. Radiomic 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 optimal features to determine the radiomics score (Rad-score). The radiological-radiomics nomogram was developed by using significant predictors of LN metastasis by multivariate logistic regression (LR) analysis in the training cohort and validated in the validation cohort independently. Its diagnostic performance was assessed by receiver operating characteristic curve (ROC), decision curve (DCA) and calibration curve analyses.

Results: The radiological model, including LN size, and margin and enhancement pattern (three significant predictors), exhibited areas under the curves (AUCs) of 0.831 and 0.756 in the training and validation cohorts, respectively. Nine radiomic features were used to construct a radiomics model, which showed AUCs of 0.879 and 0.804 in the training and validation cohorts, respectively. The radiological-radiomics nomogram, which incorporated the LN Rad-score and the three LNs' radiological features, performed better than the Rad-score and radiological models individually, with AUCs of 0.937 and 0.851 in the training and validation cohorts, respectively. Calibration curve analysis and DCA revealed that the radiological-radiomics nomogram showed satisfactory consistency and the highest 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 clinicians make appropriate clinical decisions for PADC patients.

Keywords: computed tomography; lymph node metastasis; nomogram; pancreatic ductal adenocarcinoma; radiomics.