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

ABSTRACT:

Background

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

Materials and methods

This retrospective study employed a three-dimensional nnU-Net-based model trained in plain MRI from patients who underwent resection for pancreatic neoplasms. A radiomics model was developed for diagnosing pancreatic neoplasms based on automatic segmentation. The segmentation performance of the deep learning model was quantitatively evaluated using dice similarity coefficient (DSC). The performance of the radiomics model was assessed through receiver operating characteristic analysis.

Results

The study included 165 and 89 patients in the training and te

SUBMITTER: Shi YJ 

PROVIDER: S-EPMC12523135 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature