Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning.

ABSTRACT:

Background

Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases.

Methods

Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-

SUBMITTER: Lemieux ME 

PROVIDER: S-EPMC9862555 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature