European Radiology: Application of deep learning-based ultrasound nomogram model in preoperative prediction of pancreatic neuroendocrine tumor aggressiveness

Pancreatic neuroendocrine tumors (PNENs) originate from neuroendocrine cells and are the second most common pancreatic solid tumor.

The rapid development of artificial intelligence in medical image analysis has created a new field of deep learning (DL.

Recently, published in European Radiology, combining CEUS image-based DL with clinical factors to develop and validate a combined nomogram model for personalized prediction of preoperative tumor aggressiveness in patients with PNENs for accurate preoperative risk profilin.

This retrospective study performed CEUS in consecutive patients with histologically confirmed PNENs between January 2010 and October 2020. Patients were randomly assigned to training and test group.

A total of 104 patients were evaluated, including 80 in the training set (mean age ± SD, 47 years ± 12; 56 males) and 24 in the test set (50 years ± 12; 14 males.

This study demonstrates that the joint model developed in this study integrating DL prediction probability and clinical characteristics can effectively predict the preoperative invasiveness of PNEN.

Original source:

Jingzhi Huang, Xiaohua Xie, Hong Wu, et a.