Label-free machine learning detects a multi-cancer signal in a noninvasive urine and plasma metabolite panel: a case control study

Background

Urine and plasma metabolites are attractive noninvasive cancer biomarkers, but most panels are developed and normalized using case-control labels and evaluated only at study prevalence. We assessed how well a four-metabolite panel separates cancer cases from controls across eight cancer types, whether this separation is detectable without outcome labels, and how performance translates to realistic prevalence.