Multi-frequency MR elastography-based tomoelastography: an additional new imaging tool for postoperative pancreatic fistula risk stratification

Background: Postoperative pancreatic fistula (POPF) is a prevalent and severe complication of pancreaticoenteric anastomosis; however, its accurate preoperative prediction is challenging. Purpose: To investigate the utility of pancreatic stiffness and fluidity derived from tomoelastography and stratify the risk of POPF. Materials and methods: This prospective study included participants who underwent preoperative tomoelastography and pancreaticoenteric anastomosis between November 2021 and July 2024. Participants were divided into training and test sets in a ratio of 2:1. Stiffness and fluidity were quantified using maps of shear-wave speed ( c ) and phase angle (φ). A nomogram was constructed using independent predictive factors of POPF, which were determined using logistic regression analysis of the training set. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA) of both sets. Results: The POPF rate was 20.19% (21/104) and 24.52% (13/53) in the training and test sets, respectively. A moderate correlation was observed between c and fibrosis ( r = 0.66; P < 0.001) and between fat fraction and lipomatosis ( r = 0.55; P < 0.001) in the total set. Pancreatic c (odds ratio, [OR]: 0.27; P < 0.001), φ (OR: 0.17; P < 0.001), main pancreatic duct (MPD) (OR: 0.49; P = 0.002), and fat fraction (OR: 1.05; P = 0.028) in the resection margin were independent predictive factors for POPF in training set. The AUCs of the nomogram were higher than those of the conventional MRI model (fat fraction and MPD) in both the training (0.941 vs. 0.812, P = 0.002) and test sets (0.900 vs. 0.808, P = 0.046). The nomogram had a good calibration. DCA curves showed that the nomogram had better clinical applicability than the conventional MRI model. Conclusion: A nomogram constructed with pancreatic mechanical properties (stiffness and fluidity) quantified using tomoelastography can improve the predictive performance of conventional MRI for POPF risk stratification.