# 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
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensor** & **Disease/Phenotypes/Steps** & **Subjects** & **Classifier** & **Results** & **Ref.** \\ \hline
**Molecularly** & **Cox vs. LC** & 40 Coa, 149 LC, 56 asthma/COPD** & **DFA, ANN** & 92\% acc, 93\% LC, and 89\% Goal cement classification & [43] \\ \cline{3-6}
**modified SiNNF FET** & **LC vs. asthma and COPD** & & & 89\% acc, 92\% ex, 80\% spec. & \\ \cline{3-6}
**Gas vs. gastric ulcer patients** & **49 Coa, 30 gastric ulcer** & **Back-propagation Neural network** & & 99\% acc, 94\% conf. ambient, benign, normal & [241] \\ \cline{3-6}
**MOS gas sensor** & **MOS gas sensor** & & & & Acc., series, 92\% acc, 93\%, 94\% cross validation//tictictic & \\
**array** & **OC vs. benign and HC** & 86 OC, 51 benign, 114 HC & **PCA, k-NN** & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline
**BIONOTE** & **CID vs. NC-CLD** & 65 CLD, 39 NC-CLD** & **PLS-DA, radar plot** & & & \\ \cline{3-6}
**Commercial (MO)** & **OC vs. diabetes vs. HC high creatinine,** & 84 CKD, 24 diabetes, 54 HC high & Radar plots, PCA, SVM, & PCA+64\% of total variance expressed in PC1-3 & [243] \\
**gas sensors** & **HC low creatinine** & **HC low creatinine** & **coatting, 54 HC low creatinine** & **PI-spressivity, ICA** & & \\ \cline{3-6}
**Disease histology/phenotypes** & & & & & \\ \hline
**Tee Vergate +None** & **SCC vs. ADC** & 10 SCC, 10 ADC & **PLS-DA** & & 75\% correct classification & [132] \\ \cline{3-6}
**24 colcarts** & **SCC vs. ADC** & 22 SCC, 50 ADC & & & 86.4\% acc. & \\ \cline{3-6}
**SCLC vs. NSCLC** & 9 SCLC, 83 NCSC & & & & & \\ \cline{3-6}
**UV-irradiated** & **LC vs. HC** & & & & & \\ \cline{3-6}
**splitting, Aa, P, P,** & & & & & \\ \cline{3-6}
**AMPN, N-F-doped** & **SC** vs. NSCLC, SCC vs. ADC** & & & & \\ \cline{3-6}
**WOSWMs** & **SC** & & & & & \\ \cline{3-6}
**Molecularly capped** & & & & & & \\ \cline{3-6}
**AuNP and** & **LC vs. without _EGFR_ mutation & 19 with _EGFR_, 34 without _EGFR_ & & & \\ \cline{3-6}
**SWCNT based** & **Chemissions** & & & & & \\ \cline{3-6}
**Cynnose 320, Tor** & & & & & & \\ \cline{3-6}
**Vergate, Common** & & & & & & \\ \cline{3-6}
**In-event, Outsion** & & & & & & \\ \cline{3-6}
**Lancetar** & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cont. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensor** & **Diseases/Phenotypes/Stages** & **Subjects** & **Classifier** & **Results** & **Ref. ** \\ \hline \multirow{6}{*}{**Cyanage 320**} & Asthma inflammatory phenotypes & 24 EOS, 10 NEUTR, 18 PAUC. & PCA & Acc.,sens.,spec., 77%, 0.07%, 97%, 0.08%,  vs NEUTR, 7.7%, \\  & Uncontrolled asthma-like symptoms & Training set. 65 cluster 1, 22 cluster 2, 34 cluster 3 & one-way ANOVA, Kruskal-Wallis & Significant differences concerning chest tightness during exercise, dyspnea and gender & [246] \\ \cline{2-6}  & HC and controlled vs. parity controlled & 10 HC, 9 controlled, 7 partly, 12 & PCA, radar plot & Cross-validated AUC 0.08, 70%,,sens., 84% spec. & [119] \\  & and uncontrolled asthma & Uncontrolled & & Acc.,sens.,spec., &  
Concerning LC histology and staging with sensing devices, promising studies have been reported in the literature. The discrimination of NSCLC subtypes ADC and SCC has been permitted using Tor Vergata e-Nose with an accuracy of 75%, by applying endoscopic breath sampling [132] as well as by using a colorimetric sensor-array of 24 elements developed by [PERSON] et al. ultimately achieving an accuracy of 86.4% [127]. SCLC and NSCLC differentiation and LC staging (I/II vs. III/IV) were also examined by [PERSON] et al. though with moderate accuracies [127]. A 6-sensor-array based on UV-irradiated (394 nm) pristine or metal-doped WO\({}_{3}\)NWs (Table 4) differentiated effectively not only ADC from SCC, but also between SCLC and NSCLC with 77.5% and 84.5% accuracy values, respectively (Figure 15) [246].