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Clinical Validation Results of an Innovative Non-Invasive Device for Colorectal Cancer Preventive Screening through Fecal Exhalation Analysis

Screening is recommended to reduce both incidence and mortality of colorectal cancer. Currently, many countries employ fecal occult blood test (FOBT). In Emilia-Romagna (Italy), since 2005, FOBT immunochemical version (FIT) is performed every two years on people aged between 50 and 69 years. A colon...

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Detalles Bibliográficos
Autores principales: Zonta, Giulia, Malagù, Cesare, Gherardi, Sandro, Giberti, Alessio, Pezzoli, Alessandro, De Togni, Aldo, Palmonari, Caterina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7352827/
https://www.ncbi.nlm.nih.gov/pubmed/32512911
http://dx.doi.org/10.3390/cancers12061471
Descripción
Sumario:Screening is recommended to reduce both incidence and mortality of colorectal cancer. Currently, many countries employ fecal occult blood test (FOBT). In Emilia-Romagna (Italy), since 2005, FOBT immunochemical version (FIT) is performed every two years on people aged between 50 and 69 years. A colonoscopy is then carried out on those who are FIT positive. However, FIT shows approximately 65% false positives (non-tumoral bleedings), leading to many negative colonoscopies. The use of an economic and easy-to-use method to check FOBT-positives will improve screening effectiveness, reducing costs to the national health service. This work illustrates the results of a three-year clinical validation protocol (started in 2016) of a patented device composed of a core of nanostructured gas sensors. This device was designed to identify CRC presence by fecal volatile compounds, with a non-invasive, in vitro and low-cost analysis. Feces are, in fact, affected by tumor-volatile biomarkers, produced by cellular peroxidation and metabolic alterations. The protocol consisted in the analysis of fecal samples of FIT-positive subjects, using colonoscopy as a gold standard. A total of 398 samples were analyzed with machine learning techniques, leading to a sensitivity and specificity of 84.1% and 82.4%, respectively, and a positive predictive value of 72% (25–35% for FIT).