Artificial Intelligence-Based Urine Volatomic Assay for Multi-Cancer Detection: A Multicentric Case-Control Discovery and Internal Validation Study.

Abstract

BACKGROUND: Multi-cancer detection assays may extend early-detection efforts beyond organ-specific screening programs and support emerging models of oncologic primary care, including age- and risk-stratified multi-cancer screening. We evaluated urine volatile organic compound profiles (volatome), an emerging biomarker source, for its association with cancer detection. OBJECTIVE: To determine whether urinary volatome profiles measured by gas chromatography-mass spectrometry (GC-MS) and interpreted with artificial intelligence (AI) distinguish participants with known cancer from cancer-free controls and support exploratory tissue-of-origin (TOO) and stage classification. METHODS: This multicenter retrospective case-control discovery study included urine from 491 treatment-naive participants with one of 10 cancers each and 200 cancer-free healthy controls. Three participating sites contributed de-identified specimens. Volatome profiles were measured by GC-MS. A proprietary ensemble tree-based AI pipeline underwent repeated stratified fivefold internal cross-validation. Preprocessing was fitted within each training partition. Each participant contributed one sample and was assigned only once within a given fold partition. The reported cancer-signal operating point targeted 98% specificity. TOO analysis identified the most likely cancer type and its presumed tissue of origin. RESULTS: The cancer-signal classifier produced an out-of-fold area under the receiver operating characteristic curve (AUC) of 0.990 (95% confidence interval [CI], 0.984-0.996). At the reported 98% specificity-optimized cutoff, sensitivity was 91.9% (451/491). The positive predictive value (PPV) was 99.1% with the cohort's 71% (491/691) cancer prevalence. Grouped TOO accuracy was 91.6% (450/491; 95% CI, 89.2%-94.1%). Among 374 cancers with a recorded stage, the early-versus-late detection classifier had an AUC of 0.866, and it correctly assigned 98.1% (252/257) of early-stage cancers to the early class while recovering 25.6% (30/117) of the late-stage cancers. Recursive removal with high feature importance values produced a small, gradual decline rather than an abrupt loss of performance across the evaluated classification tasks. CONCLUSIONS: Urinary VOC profiles contained a composite signal that discriminated known cancer cases from cancer-free controls under internal cross-validation with signals that were able to identify TOO with reasonable certainty. The feature-removal analysis was consistent with distributed predictive information rather than reliance on one dominant VOC or a small feature panel. Prospective blinded validation in the intended-use population remains necessary to establish population-screening accuracy, comparative superiority, or clinical utility.

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