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The urinary-metabolite-based lung cancer index (uLCI): an interpretable machine-learning risk model for early-stage
Mohammed A Khan1, Ewy A Mathé2, Sharon R Pine3
1Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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-5β-cholestane-3α,7α,12α,24R,25S-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 ρ=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.