Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy.

Journal: BMC cancer
PMID: 40329193

Abstract

BACKGROUND: The early prediction of lymph node positivity (LN+) after neoadjuvant chemotherapy (NAC) is crucial for optimizing individualized treatment strategies. This study aimed to integrate radiomic features and clinical biomarkers through machine learning (ML) approaches to enhance prediction accuracy by focusing on patients with locally advanced gastric cancer (LAGC).

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.
  • Mingwei Huang
    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.
  • Liucheng Wu
    Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Guangxi, China.

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