Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.

ABSTRACT:

Objective

The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to be susceptible to inter-observer variability. Subsequently, a prediction model of gastric cancer (GC) serosal invasion was constructed in conjunction with radiomics and deep learning features, and a nomogram was generated to explore the clinical guiding significance.

Methods

This study enrolled 311 patients from two centers with pathologically confirmed of GC. we employed a deep learning model, U-Mamba, to obtain fully automatic segmentation of the spleen CT images. Subsequently, radiomics features and deep learning features were

SUBMITTER: Shang H 

PROVIDER: S-EPMC11790706 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature