The urinary-metabolite-based lung cancer index (uLCI): an interpretable machine-learning risk model for early-stage disease

Background Five-year survival from lung cancer exceeds 60% at stage I-II but falls below 10% once metastasis occurs. Low-dose CT (LDCT) screening reduces mortality in heavy smokers but carries a false-positive rate of approximately 29% and is restricted to smoking-based eligibility, leaving most cases undetected. We aimed to develop and independently validate an interpretable machine-learning urinary metabolite risk index (uLCI) for non-invasive lung cancer detection. Methods Four urinary metabolites - creatine riboside (CR), N-acetylneuraminic acid (NANA), 27-nor-5-beta-cholestane-3-alpha,7-alpha,12-alpha,24,25-pentol (CP), and cortisol sulfate (CS) - and three clinical variables (age, race, smoking) were integrated by Lasso-regularised logistic regression into a uLCI score. The model was developed under 10-fold cross-validation in the NCI-Maryland (NCI-MD) cohort (n=845; 470 controls, 375 cases, stages I-IV) and applied without refitting to the independent Colorado Lung Cancer Cohort (n=488; 211 controls, 277 cases). Analyses were prespecified; reporting followed TRIPOD+AI. Findings uLCI achieved an area under the curve (AUC) of 0.906 (95% CI 0.887-0.926) in NCI-MD and 0.748 (0.701-0.793) in the independent Colorado cohort. Scores rose monotonically across stages in both cohorts (Spearman rho=0.69 and 0.45; both p<0.0001). Stage-specific discrimination was preserved from stage I to IV (NCI-MD 0.900-0.927; Colorado 0.722-0.843). Net reclassification improvement over clinical variables was 1.24 (1.14-1.36) and 0.74 (0.56-0.90). uLCI tertiles stratified post-resection survival in stage I-II disease (adjusted hazard ratio 2.03, 1.26-3.27). Interpretation uLCI is an independently validated, interpretable urinary risk index that detects lung cancer across all stages, with monotonic stage progression and post-resection prognostic value. Its false-positive rate compares favourably with published estimates for LDCT and cell-free-DNA assays, supporting prospective head-to-head evaluation as a non-invasive triage tool, including in screening-ineligible populations. Funding Intramural Research Program, Center for Cancer Research, National Cancer Institute, US National Institutes of Health.