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.

Abstract:Two of the biggest challenges in medicine today are the need to detect diseases in a non-invasive manner, and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularlymodified Silicon Nanowire Field Effect Transistors (SiNW FETs) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma and Chronic Obstructive Pulmonary Disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlated their sensitivity and selectivity towards volatile organic compounds (VOCs) linked with diseased states. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples showed that the optimized SiNW FETs can detectand discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct positive way, which can reassure patients and prevent numerous negative investigations. 3Physicians are always challenged by the need to give the correct diagnosis as early in the onset of a disease is possible, whether the disease-related symptoms are absent or not evident.1 Symptoms are not always characteristic of one particular disease; overlap of symptoms is common in, for example, lung diseases. 2 Patients with different respiratory diseases, such as malignant or benign tumors, or substantially less severe diseases, may have similar symptoms, e.g. cough, chest pain, difficulty to breathe, etc. These symptoms may be characteristic of lung cancer (LC), pneumonia, asthma, and chronic obstructive pulmonary disease (COPD). 1,2Therefore, it is of particular clinical importance to find a diagnostic tool capable of distinguishing between these diseases. A diagnostic tool that involves no needle, surgery and/or active materials and/or radioactive exposure would have a benefit.A highly promising approach that could meet the aforementioned need is based on the detection and classification of the disease breathprint, viz. the chemical profiles of highly-and semi-VOCs in exhaled breath linked with disease. [3][4][5][6][7][8][9][10][11][12][13][14][15] The rationale behind this approach relies on the fact that VOCs generated by cellular metabolic pathways during a specific disease circulate in the blood stream and diffuse into exhaled breath, which is easily sampled. 4,16,17 In certain instances, analysis of breathprints offers several potential advantages, such as: (a) breath samples are non-invasive and easy to obtain; (b) breath contains less complicated mixtures than either serum or urine; and (c) breath testing has the potential for direct and real-time diagnosis and monitoring. 3,18-21Several mass-spectrometry and spectroscopy studies have shown that the breathprint of a specific disease differs from that of healthy control...

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Purpose In this prospective National Cancer Institute–funded American College of Radiology Imaging Network/Radiation Therapy Oncology Group cooperative group trial, we hypothesized that standardized uptake value (SUV) on post-treatment [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) correlates with survival in stage III non–small-cell lung cancer (NSCLC). Patients and Methods Patients received conventional concurrent platinum-based chemoradiotherapy without surgery; postradiotherapy consolidation chemotherapy was allowed. Post-treatment FDG-PET was performed at approximately 14 weeks after radiotherapy. SUVs were analyzed both as peak SUV (SUVpeak) and maximum SUV (SUVmax; both institutional and central review readings), with institutional SUVpeak as the primary end point. Relationships between the continuous and categorical (cutoff) SUVs and survival were analyzed using Cox proportional hazards multivariate models. Results Of 250 enrolled patients (226 were evaluable for pretreatment SUV), 173 patients were evaluable for post-treatment SUV analyses. The 2-year survival rate for the entire population was 42.5%. Pretreatment SUVpeak and SUVmax (mean, 10.3 and 13.1, respectively) were not associated with survival. Mean post-treatment SUVpeak and SUVmax were 3.2 and 4.0, respectively. Post-treatment SUVpeak was associated with survival in a continuous variable model (hazard ratio, 1.087; 95% CI, 1.014 to 1.166; P = .020). When analyzed as a prespecified binary value (≤ v > 3.5), there was no association with survival. However, in exploratory analyses, significant results for survival were found using an SUVpeak cutoff of 5.0 (P = .041) or 7.0 (P < .001). All results were similar when SUVmax was used in univariate and multivariate models in place of SUVpeak. Conclusion Higher post-treatment tumor SUV (SUVpeak or SUVmax) is associated with worse survival in stage III NSCLC, although a clear cutoff value for routine clinical use as a prognostic factor is uncertain at this time.