Development of deep learning model for diagnosing muscle-invasive bladder cancer on MRI with vision transformer.

ABSTRACT:

Rationale and objectives

To develop and validate a deep learning (DL) model to automatically diagnose muscle-invasive bladder cancer (MIBC) on MRI with Vision Transformer (ViT).

Materials and methods

This multicenter retrospective study included patients with BC who reported to two institutions between January 2016 and June 2020 (training dataset) and a third institution between May 2017 and May 2022 (test dataset). The diagnostic model for MIBC and the segmentation model for BC on MRI were developed using the training dataset with 5-fold cross-validation. ViT- and convolutional neural network (CNN)-based diagnostic models were developed and compared for diagnostic performance using the area under the curve (AUC). The performance of the diagnostic model with manual and auto-

SUBMITTER: Kurata Y 

PROVIDER: S-EPMC11381713 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature