Machine learning assisted breathomic approach for early-stage thoracic cancer detection.

ABSTRACT:

Objective

This study explores the feasibility of using breathomic biomarkers analyzed by machine learning as a non-invasive diagnostic tool to differentiate between benign and malignant thoracic lesions, aiming to enhance early detection of thoracic cancers and inform clinical decision-making.

Methods

This study enrolled 132 participants with confirmed diagnosis of lung cancer, esophageal cancer, thymoma, and benign diseases. Exhaled breath samples were analyzed by thermal desorption-gas chromatography-mass spectrometry. A logistic regression algorithm was employed to construct a classification model for benign and malignant thoracic lesions. This model was trained on a subset of 80 cases and subsequently validated in a separate set comprising 52 samples.

Results

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SUBMITTER: Chen Z 

PROVIDER: S-EPMC12483886 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature