Colorectal Cancer Diagnosis through Breath Test Using a Portable Breath Analyzer-Preliminary Data.
Arcangelo PicciarielloAgnese DeziLeonardo VincentiMarcello Giuseppe SpampinatoWenzhe ZangPamela RiahiJared ScottRuchi SharmaXudong FanDonato Francesco AltomarePublished in: Sensors (Basel, Switzerland) (2024)
Screening methods available for colorectal cancer (CRC) to date are burdened by poor reliability and low patient adherence and compliance. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a non-invasive potential diagnostic tool for distinguishing CRC patients from healthy controls (HC). The aim of this study was to evaluate the reliability of an innovative portable device containing a micro-gas chromatograph in enabling rapid, on-site CRC diagnosis through analysis of patients' exhaled breath. In this prospective trial, breath samples were collected in a tertiary referral center of colorectal surgery, and analysis of the chromatograms was performed by the Biomedical Engineering Department. The breath of patients with CRC and HC was collected into Tedlar bags through a Nafion filter and mouthpiece with a one-way valve. The breath samples were analyzed by an automated portable gas chromatography device. Relevant volatile biomarkers and discriminant chromatographic peaks were identified through machine learning, linear discriminant analysis and principal component analysis. A total of 68 subjects, 36 patients affected by histologically proven CRC with no evidence of metastases and 32 HC with negative colonoscopies, were enrolled. After testing a training set (18 CRC and 18 HC) and a testing set (18 CRC and 14 HC), an overall specificity of 87.5%, sensitivity of 94.4% and accuracy of 91.2% in identifying CRC patients was found based on three VOCs. Breath biopsy may represent a promising non-invasive method of discriminating CRC patients from HC.
Keyphrases
- end stage renal disease
- machine learning
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- clinical trial
- prognostic factors
- randomized controlled trial
- type diabetes
- patient reported outcomes
- coronary artery disease
- gas chromatography
- mass spectrometry
- adipose tissue
- weight loss
- deep learning
- left ventricular
- patient reported
- human health