# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 4 Differential Diagnosis and Disease Phenotyping and Staging in Breath Analysis
### The Case for Lung Diseases
In another study aiming at the discrimination of LC patients from HC while taking into account the existence of metabolic comorbidities, Tor Vergata e-Nose exhibited far higher sensitivity for stage I LC in comparison to the rest of stages, either in the presence or absence of metabolic diseases (Table 4) [133]. LC staging was recently attempted by [PERSON] et al. along with COPD discrimination as mentioned above, with stage III LC being effectively discriminated from stage IV with an accuracy higher than 80%, using KPCA-XGBoost [227]. [PERSON]'s team has achieved LC staging with an accuracy of 81% and with low sensitivity, using a molecularly modified Si NW FET (Table 4) [43]. As in the case of breath analysis with analytical methods, precise diagnosis of lung diseases other than LC via sensing devices is an extensive field of research. The effective discrimination of COPD and asthma has been reported in the literature by [PERSON] at al. using Cyranose 320 and taking into consideration smoking habits, leading to high cross-validated accuracy values (Table 4) [229,230]. More recently, asthma and CF discrimination was also reported for pediatric population using AeoNose and with high accuracy values, excluding the confounding factors of diet, exercise, comorbidities and inhaled drugs [235]. Concerning ILDs, [PERSON] et al. used the AeoNose in an attempt to differentiate between ILDs subgroups (Table 4), with moderate accuracy, as well as between ILDs cryptogenic organizing pneumonia and connective-tissue diseases-associated ILD from COPD patients with good sensitivity and specificity [234]. COPD and IPF differentiation has been recently investigated by [PERSON] et al., with a high accuracy of 80%, verified by external validation using Cyranose 320 [231]. In contrast to [PERSON] et al., [PERSON]'s group achieved to reliably discriminate patients suffering from different ILDs by using SpiroNose as well as greater cohorts of ILD-patients (Table 4) [207]; the group demonstrated the applicability of e-Noses  
Figure 14: (**a**) Presentation of the cross-validation percentages of the differentiation of asthma, COPD, LC patients, and HC, using SpiroNose; (**b**) PCA plot of breathprints collected from asthmatic patients at the Academic Medical Center (AMC), Amsterdam and Medical Spectrum Twente (MST), Enschede, for which no significant differentiation is observed (\(p\) = 0.892). Adapted with permission from Ref. [91]. Copyright © 2015 IOP Publishing Ltd.  
in ILDs differential diagnosis and specifically in IPF discrimination from non-IPF patients with high accuracies (91%) [207]. Disease phenotyping using sensing devices seems to be also feasible. [PERSON] et al. achieved differentiation between the three inflammatory phenotypes of asthma with high accuracy values (Table 4), with the participants' phenotypes being characterized by differential leukocyte counts in induced sputum [247] while asthma-control assessment has been also reported. [PERSON] et al. used 4 different e-Noses in order to discriminate between stable and unstable periods, comparing baseline (control) vs. loss of control and loss of control vs. recovery breath samples, with the Owlstone Lonestar being the most prominent concerning the discrimination of unstable periods (Table 4) [90]. More recently, [PERSON] et al. demonstrated the ability of Cyranose 320 to discriminate the uncontrolled asthma-like symptoms, using 3 different groups of asthmatic or suspicious of asthma participants divided by unsupervised hierarchical clustering [248]. The division of participants was based on asthma, lung function, symptoms of the last month, age, and food/drink intake 2 h before breath sampling [248].