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

Chunyuan Cen; Chunyou Wang; Siqi Wang; Kan Wen; Liying Liu; Xin Li; Linxia Wu; Mengting Huang; Ling Ma; Huan Liu; Heshui Wu; Ping Han

doi:10.3389/fonc.2023.1218128

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

Front Oncol. 2023 Sep 4:13:1218128. doi: 10.3389/fonc.2023.1218128. eCollection 2023.

Authors

Chunyuan Cen^{1

2}, Chunyou Wang³, Siqi Wang^{1

2}, Kan Wen⁴, Liying Liu⁴, Xin Li^{1

2}, Linxia Wu^{1

2}, Mengting Huang^{1

2}, Ling Ma⁵, Huan Liu⁶, Heshui Wu³, Ping Han^{1

2}

Affiliations

¹ Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
² Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China.
³ Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
⁴ Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
⁵ He Kang Corporate Management (SH) Co. Ltd, Shanghai, China.
⁶ Advanced Application Team, GE Healthcare, Shanghai, China.

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.

Keywords: histological grade; overall survival; pancreatic ductal adenocarcinoma; radiomics; x-ray computed tomography.

Grants and funding

This study was financially supported by the National Natural Science Foundation of China (81873895 to PH).