Detection of Ovarian Cancer through Exhaled Breath by Electronic Nose: A Prospective Study

Abstract

Background: Diagnostic methods for the early identification of ovarian cancer (OC) represent an unmet clinical need, as no reliable diagnostic tools are available. Here, we tested the feasibility of electronic nose (e-nose), composed of ten metal oxide semiconductor (MOS) sensors, as a diagnostic tool for OC detection. Methods: Women with suspected ovarian masses and healthy subjects had volatile organic compounds analysis of the exhaled breath using e-nose. Results: E-nose analysis was performed on breath samples collected from 251 women divided into three groups: 86 OC cases, 51 benign masses, and 114 controls. Data collected were analyzed by Principal Component Analysis (PCA) and K-Nearest Neighbors’ algorithm (K-NN). A first 1-K-NN (cases vs. controls) model has been developed to discriminate between OC cases and controls; the model performance tested in the prediction gave 98% of sensitivity and 95% of specificity, when the strict class prediction was applied; a second 1-K-NN (cases vs. controls + benign) model was built by grouping the non-cancer groups (controls + benign), thus considering two classes, cases and controls + benign; the model performance in the prediction was of 89% for sensitivity and 86% for specificity when the strict class prediction was applied. Conclusions: Our preliminary results suggested the potential role of e-nose for the detection of OC. Further studies aiming to test the potential adoption of e-nose in the early diagnosis of OC are needed.

Breath Analysis: A Promising Tool for Disease Diagnosis—The Role of Sensors

“…Remarkably, a molecularly modified Si NW FET, developed by Haick’s group, has permitted Gca staging with an accuracy of 87% as well as Gca differentiation from LC with an accuracy of 92% [ 43 ]. More recently, discrimination of ovarian cancer (OC) from women with benign tumors and HC was achieved by Raspagliesi et al with great classification performance, both in the case of strict and most probable prediction, using again MOS sensors [ 242 ]. Notably in class prediction application, 4/23 early-stage OC patients were misclassified as benign/HC along with 2/14 OC patients with tumor size < 3 cm in cross validation phase, while in prediction phase only 1/9 early-stage patients were misdiagnosed [ 242 ].…”

Real-time and on-line monitoring of ethanol fermentation process by viable cell sensor and electronic nose

“…Electronic nose is widely used in many elds by detecting the content of certain volatile components. Raspagliesi et al (2020) used electronic nose to analyze volatile organic compounds in the breath of women with suspected ovarian mass and healthy people, and then a diagnostic model was established with a prediction performance of 89% sensitivity and 86% speci city. Bhattacharyya et al (2007) used electronic nose to monitor the emission rule of volatile components in black tea fermentation process and determined the optimal fermentation time, thus saving manpower and tedious off-line chemical detection.…”

Immune-Checkpoint Inhibitors in Platinum-Resistant Ovarian Cancer

“…Ninety percent of patients with early-stage disease are alive at 10 years compared with only around 15% with advanced disease, despite optimal treatment [ 3 ]. Over 50% of patients present with advanced disease due to a lack of effective screening measures and the absence of specific symptoms which leads to diagnostic delay [ 4 ]. Most OC arises from the epithelial tissue (90%), and most of them seems originating from the fallopian tube [ 5 ].…”