Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics.

ABSTRACT: The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our

SUBMITTER: Schult TA 

PROVIDER: S-EPMC8713787 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature