Demographic distribution matching between real‐world and virtual phantom population

DISTINCT provides a statistically rigorous and scalable approach for covariate alignment between real and virtual imaging cohorts based on demographic factors of variability. Although demonstrated for lung cancer screening with low-dose CT, the framework is broadly applicable to other imaging modalities and diseases, and across wide ranges of factors of variability. By enabling fair and representative performance assessments, DISTINCT advances the integration of VITs into imaging research and protocol optimization workflows.