AUTHOR=Xu Jiake , Yang Jie , Feng Ye , Zhang Jie , Zhang Yuqiao , Chang Sha , Jin Jingqiang , Du Xia 

TITLE=MRI Feature-Based Nomogram Model for Discrimination Between Non-Hypervascular Pancreatic Neuroendocrine Tumors and Pancreatic Ductal Adenocarcinomas

JOURNAL=Frontiers in Oncology

VOLUME=12

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.856306

DOI=10.3389/fonc.2022.856306

ISSN=2234-943X

ABSTRACT=<p>This study aimed to investigate whether magnetic resonance imaging (MRI) features could differentiate non-hypervascular pancreatic neuroendocrine tumors (PNETs) from pancreatic ductal adenocarcinomas (PDACs). In this study, 131 patients with surgically and pathologically proven non-hypervascular PNETs (<italic>n</italic> = 44) or PDACs (<italic>n</italic> = 87) were enrolled. Two radiologists independently analyzed MRI imaging findings and clinical features. Relevant features in differentiating non-hypervascular PNETs from PDACs were identified <italic>via</italic> univariate and multivariate logistic regression models. The MRI feature-based nomogram was constructed based on multivariable logistic analysis and the reliability of the constructed nomogram was further validated. The results showed that tumor margin (<italic>P</italic> = 0.012; OR: 6.622; 95% CI: 1.510, 29.028), MPD dilation (<italic>P</italic> = 0.047; OR: 4.309; 95% CI: 1.019, 18.227), and signal in the portal phase (<italic>P</italic> &lt; 0.001; OR: 53.486; 95% CI: 10.690, 267.618) were independent discriminative MRI features between non-hypervascular PNETs and PDACs. The discriminative performance of the developed nomogram was optimized compared with single imaging features. The calibration curve, C-index, and DCA validated the superior practicality and usefulness of the MRI-based nomogram. In conclusion, the radiologically discriminative model integrating various MRI features could be preoperatively and easily utilized to differentiate non-hypervascular PNETs from PDACs.</p>