Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients.

ABSTRACT:

Purpose

Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients.

Methods

This multi-center study retrospectively included 322 patients diagnosed with gastric cancer from January 2013 to June 2023 at two hospitals. Handcrafted radiomics technique and the EfficientNet V2 neural network were applied to arterial, portal venous, and delayed phase CT images to extract two-dimensional handcrafted and deep learning features. A nomogram model was built by integrating the handcrafted signature, the deep learning signature, with clinical features. Discriminative ability was assessed using the receiver operating ch

SUBMITTER: Zhang J 

PROVIDER: S-EPMC11519172 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature