Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

Abstract

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. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.

Keywords: breath; carbon nanotube; diagnosis; disease; nanoparticle; noninvasive; sensor; volatile organic compound.

Figure 1

Schematic representation of the concept and design of the study. It involved collection of breath samples from 1404 subjects in 14 departments in nine clinical centers in five different countries (Israel, France, USA, Latvia, and China). The population included 591 healthy controls and 813 patients diagnosed with one of 17 different diseases: lung cancer, colorectal cancer, head and neck cancer, ovarian cancer, bladder cancer, prostate cancer, kidney cancer, gastric cancer, Crohn’s disease, ulcerative colitis, irritable bowel syndrome, idiopathic Parkinson’s, atypical Parkinsonism, multiple sclerosis, pulmonary arterial hypertension, pre-eclampsia, and chronic kidney disease. One breath sample obtained from each subject was analyzed with the artificially intelligent nanoarray for disease diagnosis and classification, and a second was analyzed with GC-MS for exploring its chemical composition.

Figure 2

Heat map of 59 stable sensing features, extracted from 20 different nanomaterial-based sensors on the artificially intelligent nanoarray. Each raw datum in the heat map represents the mean responses for each of the 17 diseases tested in this way. Some sensing features (SFs) were more sensitive than others to differences in the breath VOCs. No individual sensing feature was sufficiently informative to discriminate among all the diseases, but the overall response patterns had discriminative potential (columns in the heat map). For details regarding each of the measured sensing features, see SI, Table S13.

Figure 3

Graphical presentation of the accuracy of the binary DFA classifiers. Each box represents the accuracy achieved in a blind validation of each pair of subject groups. The left heat map gives the results of comparisons between groups of patients, whereas the graph on the right gives the results of the same classifiers applied to the corresponding control groups. The average accuracy was 86% for all disease classifiers (left graph) and 58% for the corresponding control groups (right graph). The letter “C” beside each disease named in the right figure means the “control” group relates to that specific disease.

Figure 4

Clustering analysis of the responses of the sensors. Each cluster represents a similar response profile, suggesting considerable resemblance between samples (subjects) in a specific cluster. It is clear that the clustering is not based on any of the potential confounding factors, but there are strong resemblances between subgroups with common pathophysiologies.

Figure 5

GC-MS analysis of the breath samples. The area under the peak (abundance) measured in the different diseases of three representative VOCs: (a) nonanal, (b) undecane, and (c) isononane. The whisker boxes present first quartile, third quartile, median (line), and average (square); the bars represent the 10% and 90% points, whereas the dots represent minimal and maximal readings.

Figure 6

Heat map of the GC-MS analysis of patients’ breath samples. The average of each of the 13 VOCs is given on the color scale. Hatched boxes are cases in which the VOC was found in <70% of the samples of a specific group of patients. The VOCs are VOC-01, 2-ethylhexanol; VOC-02, 3-methylhexane; VOC-03, 5-ethyl-3-methyloctane; VOC-04, acetone; VOC-05, ethanol; VOC-06, ethyl acetate; VOC-07, ethylbenzene; VOC-08, isononane; VOC-09, isoprene; VOC-10, nonanal; VOC-11, styrene; VOC-12, toluene; and VOC-13, undecane. At the individual VOC level, it becomes almost impossible to distinguish between different diseases (rows in the heat map). However, the overall VOC composition (columns in the color map) is quite distinctive.