0000000000595523
AUTHOR
A'laa Gharra
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 …
Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules.
We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined.…