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Updated: Jul 25, 2026

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

Deep learning automatic segmentation and radiomics model for diagnosing pancreatic solid neoplasms in MRI.

Yan-Jie Shi¹, Han Zhang¹, Lin-Lin Wang¹

¹Key Laboratory of Department of Radiology, Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.

|October 14, 2025

Summary

This study developed a deep learning tool for MRI segmentation of pancreatic neoplasms, achieving good accuracy. A radiomics model effectively differentiated pancreatic ductal adenocarcinomas from other neoplasms.

Keywords:

Deep learning Magnetic resonance imaging Pancreatic neoplasms Radiomics Segmentation

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Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Pancreatic solid neoplasms present diagnostic challenges in MRI.
Accurate segmentation is crucial for diagnosis and treatment planning.

Purpose of the Study:

To develop and validate a deep learning tool for automatic segmentation of pancreatic solid neoplasms on MRI.
To establish a radiomics model for diagnosing these neoplasms.

Main Methods:

A 3D nnU-Net deep learning model was trained on plain MRI scans for automatic segmentation.
A radiomics model was developed using segmentations for neoplasm diagnosis.
Performance was evaluated using Dice Similarity Coefficient (DSC) and receiver operating characteristic (ROC) analysis.

Main Results:

The deep learning model achieved high segmentation performance (mean DSC 0.82-0.91 training, 0.64-0.70 testing).
Segmentation was reasonably efficient for lesions < 2 cm (DSC 0.51-0.92).
The radiomics model showed high accuracy in differentiating pancreatic ductal adenocarcinomas (PDACs) (AUC 0.968 training, 0.790 testing).

Conclusions:

Deep learning automatic segmentation is accurate for pancreatic neoplasms on MRI.
The radiomics model effectively differentiates PDACs from other pancreatic neoplasms.