Pilot Study: Detection of Gastric Cancer From Exhaled Air Analyzed With an Electronic Nose in Chinese Patients

Abstract

The aim of this pilot study is to investigate the ability of an electronic nose (e-nose) to distinguish malignant gastric histology from healthy controls in exhaled breath. In a period of 3 weeks, all preoperative gastric carcinoma (GC) patients (n = 16) in the Beijing Oncology Hospital were asked to participate in the study. The control group (n = 28) consisted of family members screened by endoscopy and healthy volunteers. The e-nose consists of 3 sensors with which volatile organic compounds in the exhaled air react. Real-time analysis takes place within the e-nose, and binary data are exported and interpreted by an artificial neuronal network. This is a self-learning computational system. The inclusion rate of the study was 100%. Baseline characteristics differed significantly only for age: the average age of the patient group was 57 years and that of the healthy control group 37 years (P value = .000). Weight loss was the only significant different symptom (P value = .040). A total of 16 patients and 28 controls were included; 13 proved to be true positive and 20 proved to be true negative. The receiver operating characteristic curve showed a sensitivity of 81% and a specificity of 71%, with an accuracy of 75%. These results give a positive predictive value of 62% and a negative predictive value of 87%. This pilot study shows that the e-nose has the capability of diagnosing GC based on exhaled air, with promising predictive values for a screening purpose.

Electronic Nose as a Novel Method for Diagnosing Cancer: A Systematic Review

“…In terms of E-Nose tools, GC was commonly studied with nanosensors, appearing in three out of the five studies published to date, using both self-made and commercial devices. Among the latter, Schuermans et al [ 62 ] applied the E-Nose technology to this field, specifically using the Aeonose to classify breath samples from 16 patients with GC and 28 controls. The ROC curve derived from the analysis and obtained with the Aethena tool, revealed 81% sensitivity and 71% specificity for the abovementioned discrimination, highlighting as main limitations the small sample size and the specificities of the Chinese population on which the investigation has been carried out.…”

Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

“…In other works [27][28][29], prolonged breath was exhaled directly into the chamber, which included sensors. In [28], inhalation was performed through a charcoal filter and exhalation was performed into the sampling chamber. Afterwards, the system did not collect data for two minutes, thereby minimizing the influence of the environment.…”

“…Before each sample analysis, the chamber was purged using the sensors, with dry air for 100 s, then air was pumped into the chamber from the bag for 40 s and analyzed for 30 s. The samples were finally removed from the chamber within 140 s. In a study by Herman-Saffar et al, [27] patients breathed through a mask connected to an electronic nose for 40 s. Schmekel et al [25] injected samples within 40 s, and the average value of the last two measurements was used for calculations. In another study [28], patients breathed through an "electronic nose" device for five minutes (two minutes for preparation and three minutes for data acquisition). The composition of the air was measured every 20 s using two 32-step sinusoidal temperature modulations on the sensor surface.…”

“…The recognition accuracy was 77%, which is comparable to studies of markers in the blood (67-83%) and primary biopsies (75-80%). The same device was used in many other works [28,29,34,37,40]. The Cyranose 320 "electronic nose" is a more sophisticated, compact, and mobile solution, consisting of an array of 32 semiconductor sensors capable of analyzing many gases, not limited to the medical field.…”

Applying the electronic nose for pre-operative SARS-CoV-2 screening

“…Since the diagnosis can be made within only sixteen minutes, the test can be considered a point-of-care test. Extensive research with the Aeonose has already been done in oncology (22)(23)(24)(25) and pre-malignant disorders such as Barrett esophagus (26), but also into infectious diseases such as tuberculosis and in differentiating viral from bacterial respiratory infections in acute COPD exacerbations (27,28).…”