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

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

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 pred...

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Bibliographic Details
Main Authors: Tong Ling, Zhichao Zuo, Mingwei Huang, Jie Ma, Liucheng Wu
Format: Article
Language:English
Published: BMC 2025-05-01
Series:BMC Cancer
Subjects:
Machine learning
Lymphatic metastasis
Stomach neoplasms
Neoadjuvant therapy
Prognosis
Online Access:https://doi.org/10.1186/s12885-025-14259-w
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https://doi.org/10.1186/s12885-025-14259-w

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