# 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} \hline \hline
**Sensor** & **Diseases/Phenotypes/Stages** & **Subjects** & **Classifier** & **Results** & **Ref.** \\ \hline \multicolumn{5}{c}{**Differential diagnosis**} \\ \hline NSCLC vs. COPD (GOLD stage-III) & 10 NSCLC, 10 COPD & PCA, CDA & 8\% acc. & [223] \\ \hline IC vs. non-cancer (COPD, asthma, pneumonia) & 165 LC, 91 non-cancer & SVM & 87.3\% acc. & [224] \\ \hline LC vs. COPD vs. LC/COPD vs. HC & 631 LC, 15 COPD, 79 both, 78 HC & 77.45 acc., 1007s-accurate LC/COPD classification & \\ \hline LC vs. non-cancer (COPD, asthma, pneumonia) & 252 LC, 223 non-cancer & LRA & Sens: 95.8\% (5), 96.2\% (NS) & [223] \\ \hline \multirow{5}{*}{**Cyanage 320**} & Asthma vs. COPD & 20 asthma, 30 COPD & PCA, CDA & Within/between day probability, reproducibility of \\  & & & e-Notes & & \\ \cline{2-5}  & Fixed and classic asthma vs. COPD & 21 fixed asthma, 40 COPD & PCA, CDA & 8\% acc.8\% users, 90\% spec. & [230] \\ \cline{2-5}  & (GOLD stages II-III) & 39 classic asthma, 40 COPD & 8\% acc.9\% users, 90\% spec. & \\ \cline{2-5}  & IPF vs. COPD & 32 IPF, 33 COPD & PCA, CDA & 80\% cross-validated acc, Wider patient cohorts and \\  & & & & & Inclusion of more comorbidities needed \\ \hline \multirow{5}{*}{COPD vs. LC vs. LC} & \multirow{2}{*}{50 COPD, 30 LC, 70 BC} & \multirow{2}{*}{PCA, CDA, CAP} & \multirow{2}{*}{LC vs. COPD 90\%, 47\%, LC vs. BC 90\%, 30\%, NC vs. COPD} & [231] \\  & & & & 10\% COPD vs. LC vs. BC, 91\% & \\ \cline{2-5}  & Bronchial vs. Laryngeal SCC (advanced) & 10 bronchial, 10 laryngeal & JMP Pro & 10\% misclassification, 100% users, 80\% spec. & [232] \\ \cline{2-5}  & AD vs. PD vs. HC & 18 AD, 16 PD, 19 HC & LDA & 76.2\% users, 48.5\% spec., \(p<0.0001\) & [233] \\ \hline \multirow{5}{*}{**Aeses/None**} & ILDs (COPD vs. COPD, ILDs) & 28 COP, 23 COPD & AUC 0.77, 75\% users, 71\% spec. & \\ \cline{2-5}  & ILDs (COPD, ILD, ILDs) & 25 CTD-ID, 23 COPD & & & \\ \cline{2-5}  & sarcoidosis,ulla, nulls., abs., NSSIP, & 174 BL, 23 COPD & & & \\ \cline{2-5}  & & & & & \\ \cline{2-5}  & Asthma vs. CF & 20 asthma (moderate-severe), 13 SC & ANN & AUC 0.90\% users, 77\% spec. & [233] \\ \cline{2-5}  & HNSCC vs. LC & 52 HNSCC, 32 LC & ANN & Acc.,sens., 95\%, 95\%, 85\% (true-validation) & [234] \\ \cline{2-5}  & & & & Acc.,sens., spec. 81\%, 79\%, 81\%-HNSCC vs. colon & \\ \cline{2-5}  & Cancer types & 100 HNSCC, 40 bladder, 28 colon cancer & ANN & 8\%, 80\%, 86\% HNSCC vs. bladder cancer, & [237] \\ \cline{2-5}  & & & & 8\%, 80\%, 86\% (Cohen vs. bladder cancer & \\ \cline{2-5}  & & & & & \\ \cline{2-5}  & & & & & \\ \cline{2-5}  & & & & & \\ \cline{2-5}  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Sensing devices used for differential diagnosis, staging, and phenotyping of different categories of diseases. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Sensor** & **Diseases/Phenotypes/Siggers** & **Subjects** & **Classifier** & **Results** & **Ref. ** \\ \hline \hline  & LC vs. COPD vs. asthma vs. HC & 31 LC, 31 COPD, 37 asthma, 45 HC & PCA & Cross-validation values 78-88\%, repeatability \(\uparrow\) & [91] \\ \cline{2-7}  & LC vs. COPD vs. asthma vs. HC