Precise diagnosis and risk stratification of prostate cancer by comprehensive serum metabolic fingerprints: a prediction model study.

ABSTRACT:

Objectives

Prostate cancer (PCa) is one of the most common malignancies in men worldwide and has caused increasing clinical morbidity and mortality, making timely diagnosis and accurate staging crucial. The authors introduced a novel approach based on mass spectrometry for precise diagnosis and stratification of PCa to facilitate clinical decision-making.

Methods

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry analysis of trace blood samples was combined with machine learning algorithms to construct diagnostic and stratification models. A total of 367 subjects, comprising 181 with PCa and 186 with non-PCa were enrolled. Additional 60 subjects, comprising 30 with PCa and 30 with non-PCa were enrolled as an external cohort for validatio

SUBMITTER: Fei X 

PROVIDER: S-EPMC10942223 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature