Screening for gastric cancer using exhaled breath samples.

ABSTRACT

BACKGROUND: The aim was to derive a breath-based classifier for gastric cancer using a nanomaterial-based sensor array, and to validate it in a large screening population. METHODS: A new training algorithm for the diagnosis of gastric cancer was derived from previous breath samples from patients with gastric cancer and healthy controls in a clinical setting, and validated in a blinded manner in a screening population. RESULTS: The training algorithm was derived using breath samples from 99 patients with gastric cancer and 342 healthy controls, and validated in a population of 726 people. The calculated training set algorithm had 82 per cent sensitivity, 78 per cent specificity and 79 per cent accuracy. The algorithm correctly classified all three patients with gastric cancer and 570 of the 723 cancer-free controls in the screening population, yielding 100 per cent sensitivity, 79 per cent specificity and 79 per cent accuracy. Further analyses of lifestyle and confounding factors were not associated with the classifier. CONCLUSION: This first validation of a nanomaterial sensor array-based algorithm for gastric cancer detection from breath samples in a large screening population supports the potential of this technology for the early detection of gastric cancer.