Recommendations for Validating Omics Prediction Models: Insights from a Lung Cancer RNA Biomarker Study.

ABSTRACT:

Background

External validation of predictive models in medical research is crucial to ensure their generalizability and applicability across diverse populations. However, validation often reveals discrepancies in model performance due to cohort differences, sample collection and storage, overfitting, and inconsistencies in data handling. This study investigates the challenges encountered during external validation of predictive models for early lung cancer detection using small RNA biomarkers, tying these challenges to specific validation outcomes and deriving recommendations.

Methods

Predictive models based on the XGBoost algorithm, developed from serum samples in the JanusRNA cohort, were externally validated in two independent Norwegian cohorts: HUNT and Norwegian Women a

SUBMITTER: Pestarino L 

PROVIDER: S-EPMC12491951 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature