# 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  
Chronic and acute lung diseases such as asthma, COPD, idiopathic pulmonary fibrosis (IPF), LC, mesothelioma, and sarcoidosis have been connected with similar metabolic alternations [202]. Especially asthma and COPD are also characterized by similar symptoms [37] with COPD being commonly underdiagnosed or diagnosed at late stages [203]. Concerning LC, no symptoms are expressed in early stages [204] while disease manifestation is limited to non-specific symptoms [204] including cough, short breath, chest pain, and weight loss [37]. Disease phenotyping, on the other hand, is mandatory in some cases. Asthma subtypes such as eosinophilic, neutrophilic, mixed granulocyte, and paucigranulocytic asthma [205] are characterized by similar symptoms while different treatment is required [206]. Similarly, immunosuppressive, antifibrotic, or a combination of medications may be needed for fibrotic interstitial lung diseases (ILDs), depending on the respective phenotype (inflammatory, more fibrotic, or combination) [207]. Thus, reliable phenotyping is needed for appropriate medication to be administered [33; 206; 207]. LC is subdivided into different categories with different clinical characteristics as well. Small cell LC (SCLC), with 20-25% percentage of occurrence [208], is characterized by increased metabolic and proliferation rates compared to other cancer cells [209], while non-small cell LC (NSCLC) accounts for 70-75% of LC cases and is subdivided into the smoking-related [37; 210] squamous cell carcinoma (SCC) [208] and the non-squamous cell carcinomas [37] including adenocarcinoma (ADC) (minor smoking correlation) and large-cell carcinoma (LCC) [208]. Consequently, the accurate discrimination of different lung diseases and subtypes of a lung disease, especially using breath analysis of exhaled VOCs, is of particular importance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Electronic Nose** & **Technology Used** & **Ref.** \\ \hline
**Cyranose 320** & 32 carbon black-polymer composite chemeresistors & [117; 118] \\ \hline
**Common Invent e-Nose** & MOS sensors & [90; 117] \\ \hline
**SpiroNose** & 5 sensor arrays, each composed of 4 MOS sensors & [91; 117] \\ \hline
**AeoNose** & Micro hotplate MOS & [92; 117] \\ \hline
**DiagNose** & 12 doped MOS & [93; 118] \\ \hline
**BIONOTE e-Nose** & 8 QCM sensors using anthocyanin-coated gold electrodes & [117; 136] \\ \hline
**Tor Vergata e-Nose** & QCM covered with metalloporphyrins & [117] \\ \hline
**Owlstone Lonestar e-Nose** & Field asymmetric ion mobility spectrometry & [117] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Branded e-Noses and type of technology used in each case. The use of GC-MS has rendered lung disease differentiation, phenotyping and staging feasible in many cases. To be more specific, LC discrimination from patients with other lung diseases has been investigated in several studies. In an attempt to discriminate between NSCLC, COPD, and HC patients, by taking smoking habits into account, 4 VOCs were identified (in varying concentrations) for NSCLC and COPD [211]. In another study, [PERSON] et al. attempted to discriminate LC from COPD, asthma, pneumonia, pulmonary embolism and benign lung tumor patients; however, the selected 10 VOCs could not discriminate accurately between the two groups, implying their potential confounding role during LC-biomarkers determination [18]. [PERSON] et al. have also attempted to discriminate LC from other respiratory diseases, using 19 distinctive VOCs, based on the underlying disease mechanisms (targeted method); only the discrimination of LC patients from HC, using ethylbenzene, toluene, styrene, 2- and 1-propanol was achieved [200]. However, in a more recent study of the same group, the discrimination of LC patients from patients suffering from sarcoidosis, hypersensitivity pneumonitis, interstitial lung diseases or pulmonary infections was achieved with an increased accuracy of 75.3%; the 29 VOCs were selected following a hypothesis-generating non-targeted strategy [212].