Clinical-radiomics nomogram using contrast-enhanced CT to predict histological grade and survival in pancreatic ductal adenocarcinoma.

ABSTRACT

Objectives: Tumor grading is important for prognosis of pancreatic ductal adenocarcinoma (PDAC). In this study, we developed preoperative clinical-radiomics nomograms using features from contrast-enhanced CT (CECT), to discriminate high-grade and low-grade PDAC and predict overall survival (OS). Methods: In this single-center, retrospective study conducted from February 2014 to April 2021, consecutive PDAC patients who underwent CECT and had pathologically identified grading were randomized to training (n=200) and test (n=84) cohorts for development of model to predict histological grade based on radiomics scores from CECT (HGrad). Another 42 patients were used as external validation cohort of HGrad. A nomogram (HGnom) was constructed using radiomics score, CA12-5 and smoking to predict histological grade. A second nomogram (Pnom) was constructed using radiomics score, CA12-5, TNM, adjuvant treatment, resection margin and microvascular invasion to predict OS in radical resection patients (217 of 284). Results: Among 326 patients, 122 were high-grade (120 poorly differentiated and 2 undifferentiated). The HGrad yielded AUCs of 0.75 (95% CI 0.64, 0.85) and 0.76 (95% CI 0.60, 0.91) in test and validation cohorts. The HGnom achieved AUCs of 0.77 (95% CI 0.66, 0.87), and the predicted grades calibrated well with actual grades (P=.13). OS was different between the grades predicted by radiomics scores (P=.01). The integrated AUC of the Pnom for predicting OS was 0.80 (95% CI 0.75, 0.88). Conclusion: Compared with the HGrad using features from CECT, the HGnom demonstrated higher performance for predicting histological grade. The Pnom helped identify patients with high survival outcome in pancreatic ductal adenocarcinoma.