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

Valerie N. E. Schuermans, Ziyu Li, Audrey C. H. M. Jongen, Zhouqiao Wu, Jinyao Shi, Jiafu Ji, Nicole D. Bouvy*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)429-434
Number of pages6
JournalSurgical Innovation
Volume25
Issue number5
DOIs
Publication statusPublished - Oct 2018

Keywords

  • gastric surgery
  • surgical oncology
  • evidence-based medicine
  • surgery
  • VOLATILE ORGANIC-COMPOUNDS
  • COLORIMETRIC SENSOR ARRAY
  • LUNG-CANCER
  • SILICON NANOWIRE
  • LONG-TERM
  • BREATH
  • DIAGNOSIS
  • IDENTIFICATION
  • TECHNOLOGY
  • PREDICTION

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