Analyses Of Ovarian Cancer Patients Breath With Nano-sensor Technology

Objective To investigate the feasibility of odor prints for discriminating the ovarian cancer patients and normal human with nanomaterial-based sensors, and to evaluate the feasibility for diagnosing the early stage of ovarian cancer.Methods Breath samples were collected by3L Tedlar(?) gas bag from48ovarian cancer (OC) patients (including40cases epithelial cancer origin;2cases of germ cells origin; and6cases of borderline ovarian tumor.25cases were early stage and22cases were advanced stage.1case is unknown), and48healthy controls, meanwhile the ambient air were collected for reference. ORBOTM420Tena(?) TA tubes were opened by tube cutter, then the3L Tedlar(?) gas bag and the glass rotameter DK800were collected by Teflon tube, and breath samples were concentrated by ORBOTM420Tenax(?) TA tubes, then ORBOTM420Tenax(?) TA tubes were covered. A Keithley data logger device (model2701DMM) is used to sequentially acquire resistance readings from the sensor array, during the entire experiment. The whole system is controlled by a custom made Lab View program and spans at least two cycles. The analysis of the signals from the sensor array (for the electronic NA-NOSE analysis) is performed using Discriminant Factor Analysis (DFA) pattern recognition.Results The blind DFA models showed:An excellent discrimination between the ovarian cancer patients and the healthy controls (89%accuracy); early stage versus healthy controls showed75%accuracy. Conclusion The primary results could lead for a new non-invasive promising screening method for diagnoses early of OC in early stages and thus lowering the mortality of OC.