Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry.

ABSTRACT: Diagnosis of human bladder cancer in untreated tissue sections is achieved by using imaging data from desorption electrospray ionization mass spectrometry (DESI-MS) combined with multivariate statistical analysis. We use the distinctive DESI-MS glycerophospholipid (GP) mass spectral profiles to visually characterize and formally classify twenty pairs (40 tissue samples) of human cancerous and adjacent normal bladder tissue samples. The individual ion images derived from the acquired profiles correlate with standard histological hematoxylin and eosin (H&E)-stained serial sections. The profiles allow us to classify the disease status of the tissue samples with high accuracy as judged by reference histological data. To achieve this, the data from the twenty pairs were divided into a training

SUBMITTER: Dill AL 

PROVIDER: S-EPMC3050580 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature