Predicting neoadjuvant chemotherapy response in locally advanced gastric cancer using a machine learning model combining radiomics and clinical biomarkers.

Journal: Digital health
Published Date: May 8, 2025

Abstract

RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is a promising therapeutic strategy for managing locally advanced gastric cancer (LAGC), aiming to reduce tumor burden, enhance resection rates, and improve clinical outcomes. Due to variability in patient responses, the objective of this study was to enhance the prediction of NAC tumor regression grade (TRG) in patients with LAGC by integrating radiomic features with clinical biomarkers through machine learning (ML) approaches.

Authors

  • Tong Ling
    Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Guangxi, China.
  • Zhichao Zuo
    School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan, China.
  • Liucheng Wu
    Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Guangxi, China.
  • Jie Ma
    Respiratory Department, Beijing Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China.
  • Tingan Wang
    Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Guangxi, China.
  • Mingwei Huang
    Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Guangxi, China.

Keywords

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