Identification of profiles of volatile organic compounds in exhaled breath by means of an electronic nose as a proposal for a screening method for breast cancer

Lorena Díaz de León-Martínez 1, Maribel Rodríguez-Aguilar 1, Patricia Gorocica-Rosete 2, Carlos Alberto Domínguez Reyes 3, Verónica Martínez Bustos 3, Juan Alberto Tenorio-Torres 3, Omar Ornelas-Rebolledo 4, José Alfonso Cruz Ramos 5, Berenice Balderas-Segura 2, Rogelio Flores-Ramírez 6.

1 Center for Applied Research in Environment and Health, CIACYT, Medicine Faculty, Autonomous University of San Luis Potosí. Av. Venustiano Carranza 2405, CP 78210, San Luis Potosí, S.L.P. Mexico. 2 Biochemistry research department of National Institute Respiratory Diseases, Ismael Cosío Villegas, Mexico city, Mexico. 3 Fundación del Cáncer de Mama A.C., Mexico city, Mexico. 4 Labinnova Inc., Spring, Tx, USA. 5 Instituto Jalisciense de Cancerología, Guadalajara, Mexico. 6 Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí. 

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Aim. The objective of the present study was to identify volatile chemical prints on exhaled breath called breath-print from breast cancer (BC) patients and healthy women by means of electronic nose and to evaluate its potential use as a screening method.  

Method. A cross-sectional study of 443 exhaled breath samples from women, 262 women with BC diagnosed by biopsy and 181 healthy women as a control group was performed. The breath-print analysis was performed utilizing the Cyranose 320 electronic nose. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA) and Support Vector Machine (SVM) and the test's diagnostic power by means of ROC (Receiver Operating Characteristic) curves.  

Results. The results indicated that the breath-print of BC patients is different from that of healthy women and that they present variability of up to 98.8% with a correct classification of 98%. Sensitivity, specificity, negative predictive value and positive predictive value reach 100% according to the ROC curve. 

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