Deep learning models for preoperative T-stage assessment in rectal cancer using MRI: exploring the impact of rectal filling.

ABSTRACT:

Background

The objective of this study was twofold: firstly, to develop a convolutional neural network (CNN) for automatic segmentation of rectal cancer (RC) lesions, and secondly, to construct classification models to differentiate between different T-stages of RC. Additionally, it was attempted to investigate the potential benefits of rectal filling in improving the performance of deep learning (DL) models.

Methods

A retrospective study was conducted, including 317 consecutive patients with RC who underwent MRI scans. The datasets were randomly divided into a training set (n = 265) and a test set (n = 52). Initially, an automatic segmentation model based on T2-weighted imaging (T2WI) was constructed using nn-UNet. The performance of the model was evaluated us

SUBMITTER: Tian C 

PROVIDER: S-EPMC10722089 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature