Differential diagnosis between low-risk and high-risk thymoma: Comparison of diagnostic performance of radiologists with and without deep learning model.

ABSTRACT:

Background

There are few CT-based deep learning (DL) studies on thymoma according to the World Health Organization classification.

Purpose

To develop a CT-based DL model to distinguish between low-risk and high-risk thymoma and to compare the diagnostic performance of radiologists with and without the DL model.

Material and methods

159 patients with 160 thymomas were included. A fine-tuning VGG16 network model with Adam optimizer was used, followed by k-fold cross validation. The dataset consisted of three axial slices, including the maximum tumor size from the CT volume data. The data were augmented 50 times by rotation, zoom, shear, and horizontal/vertical flip. Three independent networks for the CT dataset were considered, and the result was determined by voting. T

SUBMITTER: Yoshida Y 

PROVIDER: S-EPMC11457241 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature