Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer.

ABSTRACT:

Background

This study aimed to develop and validate machine learning models for preoperative identification of metastasis to station 4 mediastinal lymph nodes (MLNM) in non-small cell lung cancer (NSCLC) patients at pathological N0-N2 (pN0-pN2) stage, thereby enhancing the precision of clinical decision-making.

Methods

We included a total of 356 NSCLC patients at pN0-pN2 stage, divided into training (n = 207), internal test (n = 90), and independent test (n = 59) sets. Station 4 mediastinal lymph nodes (LNs) regions of interest (ROIs) were semi-automatically segmented on venous-phase computed tomography (CT) images for radiomics feature extraction. Using least absolute shrinkage and selection operator (LASSO) regression to select features with non-zero coefficients. Four mac

SUBMITTER: Kang Y 

PROVIDER: S-EPMC12285129 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature