Development and validation of an interpretable machine learning model for prediction of occult lymph node metastasis in clinical stage T1 lung adenocarcinoma.

ABSTRACT:

Background

Accurate comprehensive prediction of occult lymph node metastasis (OLNM) is crucial for optimizing treatment strategy in early-stage lung adenocarcinoma (LUAD). This study aimed to develop and validate a machine learning (ML) model integrating multimodal data for the individualized prediction of OLNM.

Methods

A retrospective cohort of 12,679 patients with clinical T1N0 LUAD (≤3 cm) was identified from a single institution. After propensity score matching (PSM) to address class imbalance, a balanced cohort of 614 patients (307 with OLNM, 307 without) was used for model development. Univariable and multivariable logistic regression identified independent predictors. Eight ML models were trained on 80% of the data using these predictors and evaluated on a held-out 20

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12775695 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature