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Table 2 Literature overview eNose technology in lung disease

From: The smell of lung disease: a review of the current status of electronic nose technology

  Study participants Outcome measures Results    eNose Statistical breathprint analysis
Asthma
Dragonieri, 2007 [18] n = 20 asthma
• n = 10 mild
• n = 10 severe
n = 20 HC
• n = 10 old
• n = 10 young
Diagnostic accuracy Mild vs young HC
CVV 100%
Severe vs old HC
CVV 90%
Mild vs severe
CVV 65%
Cyranose 320 PCA; CDA
Fens 2009 [19] n = 20 asthma
n = 30 COPD
n = 20 non-smoking HC
n = 20 smoking HC
Diagnostic accuracy COPD vs asthma
CVA 96%
COPD vs smoking HC
CVA 66%
Non-smoking vs smoking HC
Not significant
Cyranose 320 PCA
Lazar 2010 [20] n = 10 asthma
• induction of bronchoconstriction with methacholine or saline
n = 10 controls
Disease course Bronchoconstriction causes no significant change in breathprint    Cyranose 320 PCA; mixed model analysis
Montuschi 2010 [21] n = 27 asthma
n = 24 HC
Diagnostic accuracy eNose only
Acc 87.5%
eNose + FeNO
Acc 95.8%
  Tor Vergata PCA; feed-forward neural network
Fens 2011 [26] Training: [19]
n = 20 asthma
n = 20 COPD
Validation:
n = 60 asthma
• n = 21 fixed obstruction
• n = 39 classic
n = 40 COPD
Diagnostic accuracy Validation: Classic asthma vs COPD
Sens 85%
Spec 90%
AUC 0.93 (0.84–1.00)
Acc 83%
Validation: Fixed asthma vs COPD
Sens 91%
Spec 90%
AUC 0.95 (0.87–1.00)
Acc 88%
Validation: Fixed vs classic asthma
No significant difference
Cyranose 320 PCA; CDA
Van der Schee 2013 [22] n = 25 asthma
n = 20 HC
Diagnostic accuracy Before OCS
Sens 80.0%
Spec 65.0%
AUC 0.766 ± 0.14
After OCS
Sens 84.0%
Spec 80%
AUC 0.862 ± 0.12
Before OCS (FeNO only)
AUC 0.738 ± 0.15
Cyranose 320 PCA; CDA
  n = 18 asthma
• maintenance ICS, stop ICS (4 weeks) and OCS (2 weeks)
Therapeutic effect OCS responsive vs not
Sens 90.9%
Spec 71.4%
AUC 0.883 (± 0.16)
    
  n = 25 asthma
• maintenance ICS, stop ICS (4 weeks) and OCS (2 weeks)
• n = 13 Loss of control (LOC)
Disease course LOC vs no LOC
Sens 90.9%
Spec 71.4%
AUC 0.814 ± 0.17
Correlation sputum eos—breathprint
R = 0.601
   
Plaza 2015 [30] n = 24 eosinophilic asthma
n = 10 neutrophilic asthma
n = 18 paucigranulocytic asthma
Diagnostic accuracy Neutro vs pauci
Sens 94%
Spec 80%
AUC 0.88
CVA 89%
EoS vs neutro
Sens 60%
Spec 79%
AUC 0.92
CVA 73%
EoS vs pauci
Sens 55%
Spec 87%
AUC 0.79
CVA 74%
Cyranose 320 PCA; CDA
Brinkman 2017 [32] n = 22 asthma, induced LOC
• maintenance ICS, stop ICS (8 weeks) and restart ICS
Disease course Baseline vs LOC
Acc 95%
LOC vs recovery
Acc 86%
Correlation sputum eos—breathprint
Not significant
Cyranose 320 PCA
Bannier 2019 [23] n = 20 asthma (age > 6 years)
n = 22 HC
Diagnostic accuracy Sens 74%
Spec 74%
AUC 0.79
   Aeonose ANN
Brinkman 2019 [31] n = 78 severe asthma
• n = 51 longitudinal follow-up
Clustering 3 clusters (baseline), acc 93%
Differences: chronic OCS use, percent serum eosinophil and neutrophil count
Follow-up (18 months)
n = 21 cluster stable
n = 30 migrated
Cyranose 320, Tor Vergata, Comon Invent PCA; Ward clustering; Non-hierarchical K-means clustering; PLS-DA; PAM; Topological data analysis
Cavaleiro Rufo 2019 [34] n = 64 suspected asthma (age 6–18 years)
• n = 45 asthma
• n = 29 persistent
• n = 16 intermittent
• n = 19 no asthma
Diagnostic accuracy Asthma vs no asthma
Sens 77.8%
Spec 84.2%
AUC 0.81 (0.69–0.93)
Acc 79.7%
Persistent vs no asthma
Sens 79.7%
Spec 68.6%
AUC 0.81 (0.70–0.92)
Acc 79.7%
Intermittent vs no asthma
Not significant
Cyranose 320 PCA; Hierarchical clustering
Dragonieri 2019 [24] Training:
n = 14 AAR
n = 14 rhinitis
n = 14 HC
Validation:
n = 7 AAR
n = 7 rhinitis
n = 7 HC
Diagnostic accuracy Training:
AAR vs HC
AUC 0.87 (0.70–0.97)
CVA 75.0%
Validation:
AAR vs HC
AUC 0.77 (0.62–0.93)
CVA 67.4%
Validation:
AAR vs rhinitis
AUC 0.92 (0.84–1.00)
CVA 83.1%
Cyranose 320 PCA; CDA
Abdel-Aziz 2020 [118] Training:
n = 486 atopic asthma (age > 4 years)
Validation:
n = 169 atopic asthma (age > 4 years)
Diagnostic accuracy Training:
AUC 0.837–0.990
Sens, spec and acc only visually available
Validation:
AUC 0.18–0.926
Sens, spec and acc only visually available
  Cyranose 320, Tor Vergata, Comon Invent, SpiroNose PLS-DA; adaptive least absolute shrinkage and selection operator; gradient boosting machine
Farraia 2020 [28] Training:
n = 121 asthma suspected (age > 6 years)
Validation:
n = 78 asthma suspected
(age > 6 years)
Clustering Training: 3 clusters (hierarchic), differences:
food/drink intake 2 h prior to sampling, percentage of asthma diagnosis in group, PEF%, age < 12 y
Validation: 3 clusters (hierarchic), differences: food/drink intake 2 h prior to sampling Cyranose 320 Unsupervised hierarchic clustering; Non-hierarchical K-means clustering; PAM
Tenero 2020 [25] n = 28 asthma (age 6–16 years)
• n = 9 controlled
• n = 7 partially controlled
• n = 12 uncontrolled
n = 10 HC
Diagnostic accuracy HC + controlled vs. partially + uncontrolled
Sens 79%
Spec 84%
AUC 0.85 (0.72–0.98)
   Cyranose 320 Penalized logistic regression
PCA
Chronic obstructive pulmonary disease (COPD)
Fens 2011 [45] n = 28 GOLD I + II
• airway inflammation (sputum eosinophil cationic protein and myeloperoxidase)
Disease course Correlation eosinophil cationic protein and breathprint
r = 0.37
Correlation myeloperoxidase and breathprint
Not significant
Airway inflammation vs no
Sens 50–73%
Spec 77–91%
AUC 0.66–0.86
Cyranose 320 PCA
Hattesohl 2011 [37] n = 23 COPD (pure exhaled breath, PEB)
n = 10 COPD (exhaled breath condensate, EBC)
n = 10 HC (EBC, PEB)
n = 10 AATd (EBC, PEB)
Diagnostic accuracy COPD vs HC
Sens 100%
Spec 100%
CVV PEB 67.6%
CVV EBC 80.5%
COPD vs AATd
Sens 100%
Spec 100%
CVV PEB 58.3%
CVV EBC 82.0%
HC vs AATd
Sens 100%
Spec 100%
CVV PEB 62.0%
CVV EBC 59.5%
Cyranose 320 LDA
  n = 11 AATd COPD (PEB)
• augmentation therapy
Therapeutic effect Before vs 6 d after therapy
Sens 100%
Spec 100%
CVV 53.3%
    
Fens 2013 [42] n = 157 COPD Clustering 4 clusters (acc 97.4%)
Differences: airflow limitation, health related QoL, sputum production, dyspnoea, smoking history, co-morbidity, radiologic density, gender
Cyranose 320 Hierarchical cluster analysis
Non-hierarchical K-means clustering
Sibila 2014 [41] n = 10 COPD bacterial colonised
n = 27 COPD non-colonised
n = 13 HC
Diagnostic accuracy Colonised vs non-colonised
Sens 82%
Spec 96%
AUC 0.922
CVA 89%
HC vs non-colonised
Sens 81%
Spec 86%
AUC 0.937
CVA 83%
HC vs colonised
Sens 80%
Spec 93%
AUC 0.986
CVA 87%
Cyranose 320 PCA; CDA
Cazzola 2015 [38] n = 27 COPD
• n = 8 AECOPD ≥ 2 per year
• n = 19 AECOPD < 2 per year
n  = 7 HC
Diagnostic accuracy COPD vs HC
Sens 96%
Spec 71%
CVA 91%
AECOPD ≥ 2 vs < 2 per y
Not significant
  Prototype (6 QMB sensors) PLS-DA
Shafiek 2015 [39] n = 50 COPD
• n = 17 sputum PPM growth
n = 93 AECOPD
• n = 42 sputum PPM growth
n = 30 HC
Diagnostic accuracy COPD vs HC
Sens 70–72%
Spec 70–73%
COPD vs AECOPD no PPM
Sens 89%
Spec 48%
(with PPM not significant)
AECOPD PPM vs AECOPD no PPM
Sens 88%
Spec 60%
Cyranose 320 LDA; SLR
  n = 61 AECOPD
• during and 2 months after recovery
Disease course During vs recovery
Sens 74%
Spec 67%
    
Van Geffen 2016 [46] n = 43 AECOPD
• n = 18 with viral infection
• n = 22 with bacterial infection
Diagnostic accuracy With vs without viral infection
Sens 83%
Spec 72%
AUC 0.74
With vs without bacterial infection
Sens 73%
Spec 76%
AUC 0.72
  Aeonose ANN
De Vries 2018 [43] Training:
n = 321 asthma/COPD
Validation:
n = 114 asthma/COPD
Clustering 5 clusters
Differences: ethnicity, systemic eosinophilia/ neutrophilia, FeNO, BMI, atopy, exacerbation rate
SpiroNose PCA; Unsupervised Hierarchical clustering
Finamore 2018 [49] n = 63 COPD
• n = 32 n6MWD worsened 1 year
• n = 31 n6MWD stable or improved 1 year
Disease course n6MWD change predicted by eNose
Sens 84%
Spec 88%
CVA 86%
n6MWD change predicted by eNose + GOLD
Sens 81%
Spec 78%
CVA 79%
  BIONOTE PLS-DA
Montuschi 2018 [50] n = 14 COPD
• maintenance ICS, stop ICS (4 weeks) and restart ICS
Therapeutic effect Maintenance vs restart ICS
Change in 15 of 32 Cyranose sensors; 3 of 8 Tor Vergata sensors
Maintenance vs restart ICS
Spirometry + breathprint prediction model
AUC 0.857
  Cyranose 320, Tor Vergata Multilevel PLS; KNN
Scarlata 2018 [44] n = 50 COPD
• standard inhalation therapy (12 weeks)
Therapeutic effect Baseline vs after 12 w
Significant decline in VOCs
   BIONOTE PLS-DA
  n = 50 COPD Clustering 3 clusters
Differences: BODE index, number of comorbidities, MEF75, KCO, pH/pCO2 arterial blood
  Unsupervised K-means clustering
Van Velzen 2019 [47] n = 16 AECOPD
• before, during and after recovery
Disease course Before vs during
Sens 79%
Spec 71%
CVA 75%
During vs after
Sens 79%
Spec 71%
CVA 75%
Before vs after
Sens 57%
Spec 64%
CVA 61%
Cyranose 320, Tor Vergata, Comon Invent PCA
Rodríguez-Aguilar 2020 [40] n = 116 COPD
• n = 88 smoking, n = 28 household air pollution associated
• n = 64 GOLD I-II, n = 52 GOLD III-IV
n = 178 HC
Diagnostic accuracy COPD vs HC
Sens 100%
Spec 97.8%
AUC 0.989
Acc 97.8% (CDA), 100% (SVM)
Smoking vs air pollution associated
Not significant
GOLD I–II vs GOLD III–IV
Not significant
Cyranose 320 PCA; CDA; SVM
Cystic fibrosis (CF)
Paff 2013 [52] n = 25 CF
n = 25 primary ciliary dyskinesia (PCD)
n = 23 HC
Diagnostic accuracy CF vs HC
Sens 84%
Spec 65%
AUC 0.76
CF vs PCD
Sens 84%
Spec 60%
AUC 0.77
Exacerbation CF
Sens 89%
Spec 56%
AUC 0.76
Cyranose 320 PCA
Joensen 2014 [53] n = 64 CF
• n = 14 pseudomonas infection
n = 21 PCD
n = 21 HC
Diagnostic accuracy CF vs HC
Sens 50%
Spec 95%
AUC 0.75
CF vs PCD
Not significant
Pseudomonas vs. non-infected CF
Sens 71.4%
Spec 63.3%
AUC 0.69 (0.52–0.86)
Cyranose 320 PCA
De Heer 2016 [54] n = 9 CF colonised A. fumigatus
n = 18 CF not colonised
Diagnostic accuracy Sens 78%
Spec 94%
AUC 0.80–0.89
CVA 88.9%
   Cyranose 320 PCA; CDA
Bannier 2019 [23] n = 13 CF (age > 6 years)
n = 22 HC
Diagnostic accuracy Sens 85%
Spec 77%
AUC 0.87
   Aeonose ANN
Interstitial lung disease (ILD)
Dragonieri 2013 [58] n = 31 sarcoidosis
• n = 11 untreated
• n = 20 treated
n = 25 HC
Diagnostic accuracy Untreated vs HC
AUC 0.825
CVA 83.3%
Untreated vs treated
CVA 74.2%
Treated vs HC
Not significant
Cyranose 320 PCA; CDA
Yang 2018 [59] Training: 80% of
n = 34 pneumo-coniosis
n = 64 HC
Validation: 20% of
n = 34 pneumo-coniosis
n = 64 HC
Diagnostic accuracy Training:
Sens 64.3–67.9%
Spec 88.0–92.0%
AUC 0.89–0.91
Acc 80.8–82.1%
Validation:
Sens 33.3–66.7%
Spec 71.4–78.6%
AUC 0.61–0.86
Acc 65.0–70.0%
  Cyranose 320 LDA; SVM
Krauss 2019 [60] n = 174 ILD
• n = 51 IPF
• n = 25 CTD-ILD
n = 33 HC
n = 23 COPD
Diagnostic accuracy IPF vs HC
Sens 88%
Spec 85%
AUC 0.95
CTD-ILD vs HC
Sens 84%
Spec 85%
AUC 0.90
IPF vs CTD-ILD
Sens 86%
Spec 64%
AUC 0.84
Aeonose ANN
Dragonieri 2020 [61] n = 32 IPF
n = 36 HC
n = 33 COPD
Diagnostic accuracy IPF vs HC
AUC 1.00 (1.00–1.00)
CVA 98.5%
IPF vs COPD
AUC 0.85 (0.75–0.95)
CVA 80.0%
IPF vs COPD + HC
AUC 0.84
CVA 96.1%
Cyranose 320 PCA; CDA; LDA
Moor 2020 [57] Training:
n = 215 ILD
• n = 57 IPF
• n = 158 non-IPF
n = 32 HC
Validation:
n = 107 ILD
• n = 28 IPF
• n = 79 non-IPF
n = 15 HC
Diagnostic accuracy Training + validation:
ILD vs HC
Sens 100%
Spec 100%
AUC 1.00
Acc 100%
Training:
IPF vs non-IPF ILD
Sens 92%
Spec 88%
AUC 0.91 (0.85–0.96)
Acc 91%
Validation:
IPF vs non-IPF ILD
Sens 95%
Spec 79%
AUC 0.87 (0.77–0.96)
Acc 91%
SpiroNose PLS-DA
Lung cancer (LC)
Machado 2005 [75] Training:
n = 14 LC
n = 20 HC
n = 27 other lung disease
Validation:
n = 14 LC
n = 30 HC
n = 32 other lung disease
Diagnostic accuracy Training: LC vs HC + other
CVA 71.6% (CDA)
Validation: LC vs HC + other
Sens 71.4%
Spec 91.9%
Acc 85% (SVM)
  Cyranose 320 SVM
PCA
CDA
Hubers 2014 [71] Training:
n = 20 LC
n = 31 HC
Validation:
n = 18 LC
n = 8 HC
Diagnostic accuracy Training:
Sens 80%
Spec 48%
Validation:
Sens 94%
Spec 13%
  Cyranose 320 PCA
Schmekel, 2014 [88] n = 22 LC
• n = 10 survival > 1 year
• n = 12 survival < 1 year
n = 10 HC
Disease course  < 1 y vs HC
R = 0.95–0.98
 < 1 y vs > 1 y
R = 0.86–0.97
Prediction model survival days
R = 0.99
Applied Sensor AB model 2010 PCA; PLS; ANN
McWilliams 2015 [68] n = 25 LC
n = 166 smoking HC
Diagnostic accuracy Sens 84–96%
Spec 63.3–81.3%
AUC 0.84
   Cyranose 320 Classification and regression tree; DFA
Gasparri 2016 [76] Training:
n = 51 LC
n = 54 HC
Validation:
n = 21 LC
n = 20 HC
Diagnostic accuracy Training + validation:
Sens 81%
Spec 91%
AUC 0.874
Training:
Sens 90%
Spec 100%
Validation:
Sens 81%
Spec 100%
Prototype (8 QMB sensors) PLS-DA
Rocco 2016 [16] n = 100 (former) smokers
• n = 23 LC
Diagnostic accuracy Detection LC
Sens 86%
Spec 95%
AUC 0.87
   BIONOTE PLS-Toolbox; PLS-DA
Van Hooren 2016 [81] n = 32 LC
n = 52 head-neck SCC
Diagnostic accuracy Sens 84–96%
Spec 85–88%
AUC 0.88–0.98
Acc 85–93%
   Aeonose ANN
Shlomi 2017 [67] n = 30 benign nodule
n = 89 LC
• n = 16 early stage LC
• n = 53 EGFR tested (n = 19 mutation)
Diagnostic accuracy Early stage LC vs benign
Sens 75%
Spec 93.3%
Acc 87.0
EGFR mutation vs wild type
Sens 79.0%
Spec 85.3%
Acc 83.0%
  Prototype (40 nanomaterial-sensors) DFA
Tirzite 2017 [83] n = 165 LC
n = 79 HC
n = 91 other lung disease
Diagnostic accuracy LC vs HC + other
Sens 87.3–88.9%
Spec 66.7–71.2%
CVV 72.8%
LC vs HC
Sens 97.8–98.8%
Spec 68.8–81.0%
CVV 69.7%
LC stages
Not significant
Cyranose 320 SVM
Huang 2018 [70] Training: 80% of
n = 56 LC
n = 188 HC
Validation: 20% of
n = 56 LC
n = 188 HC
External:
n = 12 LC
n = 29 HC
Diagnostic accuracy Validation:
LC vs HC
Sens 100, 92.3%
Spec 88.6, 92.9%
AUC 0.96, 0.95
Acc 90.2, 92.7%
External validation:
LC vs HC
Sens 75, 83.3%
Spec 96.6, 86.2%
AUC 0.91, 0.90
Acc 85.4, 85.4%
  Cyranose 320 LDA; SVM
Van de Goor 2018 [73] Training:
n = 52 LC
n = 93 HC
Validation:
n = 8 LC n = 14 HC
Diagnostic accuracy Training:
Sens 83%
Spec 84%
AUC 0.84
Acc 83%
Validation:
Sens 88%
Spec 86%
Acc 86%
  Aeonose ANN
Tirzite 2019 [77] n = 119 LC smoker
n = 133 LC non-smoker
n = 223 HC + other lung disease
• n = 91 smoking
Diagnostic accuracy LC non-smoker vs HC + other
Sens 96.2%
Spec 90.6%
LC smoker vs HC + other
Sens 95.8%
Spec 92.3%
  Cyranose 320 LRA
Kononov 2020 [78] n = 65 LC
n = 53 HC
Diagnostic accuracy Sens 85.0–95.0%
Spec 81.2–100%
CVA 88.9–97.2%
AUC 0.95–0.98
   Prototype (6 MOS) PCA; Logistic regression; KNN; Random forest; LDA; SVM
Krauss 2020 [79] n = 91 LC active disease
• n = 51 incident LC
n = 29 LC complete response
n = 33 HC
n = 23 COPD
Diagnostic accuracy LC active vs HC
Sens 84%
Spec 97%
AUC 0.92
Incident LC vs HC
Sens 88%
Spec 79%
AUC 89%
  Aeonose ANN
Lung cancer—(non-)small cell lung cancer ((N)SCLC)
 Dragonieri 2009 [69] n = 10 NSCLC
n = 10 COPD
n = 10 HC
Diagnostic accuracy NSCLC vs HC
CVV 90%
NSCLC vs COPD
CVV 85%
  Cyranose 320 PCA; CDA
 Kort 2018 [72] n = 144 NSCLC
n = 18 SCLC
n = 85 HC
n = 61 suspected, LC excluded
Diagnostic accuracy NSCLC vs HC
Sens 92.2%
Spec 51.2%
AUC 0.85
NSCLC vs HC + LC excluded
Sens 94.4%
Spec 32.9%
AUC 0.76
SCLC vs HC
Sens 90.5%
Spec 51.2%
AUC 0.86
Aeonose ANN
 De Vries 2019 [87] Training:
n = 92 NSCLC
• n = 42 response
• n = 50 no response
Validation:
n = 51 NSCLC
• n = 23 response
• n = 28 no response
Therapeutic effect
(anti-PD-1 therapy)
Training:
CVV 82%
AUC 0.89 (0.82–0.96)
Validation:
AUC 0.85 (0.7–0.96)
Sens 43%
Spec 100%
  SpiroNose LDA
 Mohamed 2019 [80] n = 50 NSCLC
n = 50 HC
Diagnostic accuracy Sens 92.9%
Spec 90%
Acc 97.7%
   PEN3 PCA; ANN
 Kort 2020 [74] n = 138 NSCLC
n = 143 controls
• n = 59 suspected, LC excluded
• n = 84 HC
Diagnostic accuracy NSCLC vs controls
(eNose data only)
Sens 94.2%
Spec 44.1%
AUC 0.75
NSCLC vs controls
(multivariate)
Sens 94.2–95.7%
Spec 49.0–59.7%
AUC 0.84–0.86
  Aeonose ANN; Multivariate logistic regression
 Fielding 2020 [82] n = 20 bronchial SCC
• n = 10 in situ
• n = 10 advanced stage
n = 22 laryngeal SCC
• n = 12 in situ
• n = 10 advanced stage
n = 13 HC
Diagnostic accuracy BSCC in situ vs HC
Sens 77%
Spec 80%
Misclassification rate 28%
BSCC vs LSCC adv
Sens 100%
Spec 80%
Misclassification rate 10%
  Cyranose 320 Bootstrap forest
Lung cancer—Malignant Pleural Mesothelioma (MPM)
 Chapman 2012 [86] Training:
n = 10 MPM
n = 10 HC
Validation:
n = 10 MPM
n = 32 HC
n = 18 benign ARD
Diagnostic accuracy MPM vs HC
Training: CVA 95%
Validation: Sens 90%
Spec 91%
MPM vs ARD
Validation: Sens 90%
Spec 83.3%
MPM vs ARD vs HC
Validation: Sens 90%
Spec 88%
Cyranose 320 PCA
 Dragonieri 2012 [85] n = 13 MPM
• internal validation with training set: n = 8, validation set: n = 5
n = 13 HC
n = 13 AEx
Diagnostic accuracy MPM vs HC
Sens 92.3%
Spec 69.2%
AUC 0.893
CVA 84.6%
Validation: AUC 0.83
CVA 85.0%
MPM vs AEx
Sens 92.3%
Spec 85.7%
AUC 0.917
CVA 80.8%
Validation: AUC 0.88
CVA 85.9%
MPM vs AEx vs HC
AUC 0.885
CVA 79.5%
Cyranose 320 PCA; CDA
 Lamote 2017 [84] n = 11 MPM
n = 12 HC
n = 15 AEx
n = 12 benign ARD
Diagnostic accuracy MPM vs HC
Sens 66.7% (37.7–88.4)
Spec 63.6% (33.7–87.2)
AUC 0.667 (0.434–0.900)
Acc 65.2% (44.5–82.3)
MPM vs benign ARD
Sens 75.0% (45.9–93.2)
Spec 64% (33.7–87.2)
AUC 0.758 (0.548–0.967)
Acc 48.9–85.6% (48.9–85.6)
MPM vs benign ARD + AEx
Sens 81.5% (63.7–92.9)
Spec 54.5% (26.0–81.0)
AUC 0.747 (0.582–0.913)
Acc 73.7% (58.1–85.8)
Cyranose 320 PCA
Pulmonary infections
De Heer 2016 [100] n = 168 bottles with strain
• n = 135 bacteria + yeast
• n = 30 medium only
• n = 62 mould (A. fumigatus and R. oryzae)
Diagnostic accuracy
(in vitro)
Mould vs other
Sens 91.9%
Spec 95.2%
AUC 0.970 (0.949–0.991)
Acc 92.9%
   Cyranose 320 PCA; CDA
Suarez-Cuartin 2018 [101] n = 73 bronchiectasis
• n = 41 colonised (n = 27 pseudomonas)
• n = 32 non-colonised
Diagnostic accuracy Colonised vs non-colonised
AUC 0.75
CVA 72.1%
Pseudomonas vs other PPM
AUC 0.96
CVA 89.2%
Pseudomonas vs non-colonised
AUC 0.82
CVA 72.7%
Cyranose 320 PCA
Pulmonary infections—Ventilator-associated pneumonia (VAP)
 Hanson 2005 [104] n = 19 VAP (clinical pneumonia score, CPIS ≥ 6)
n = 19 controls (CPIS < 6)
Diagnostic accuracy Correlation CPIS -breathprint
R2 = 0.81
   Cyranose 320 PLS
 Hockstein 2005 [105] n = 15 VAP (pneumonia score ≥ 7)
n = 29 HC (ventilated)
Diagnostic accuracy Acc 66–70%    Cyranose 320 KNN
 Humphreys 2011 [99] n = 44 VAP suspected
• 98 BAL samples
• Groups: gram-positive, gram-negative, fungi, no growth
n = 6 HC (ventilated)
Diagnostic accuracy
(in vitro)
Differentiation groups (LDA)
Sens 74–95%
Spec 77–100%
Acc 83%
Differentiation groups (cross-validation)
Sens 56–84%
Spec 81–97%
Acc 70%
  Prototype (24 MOS) PCA; LDA
 Schnabel 2015 [106] n = 72 VAP suspected
• n = 33 BAL + 
• n = 39 BAL−
n = 53 HC (ventilated)
Diagnostic accuracy BAL + VAP vs HC
Sens 88%
Spec 66%
AUC 0.82 (0.73–0.91)
BAL + vs BAL− VAP
Sens 76%
Spec 56%
AUC 0.69 (0.57–0.81)
  DiagNose Random Forest; PCA
 Chen 2020 [15] Training: 80% of
n = 33 VAP
n = 26 HC (ventilated)
Validation: 20% of
n = 33 VAP
n = 26 HC (ventilated)
Diagnostic accuracy Training:
AUC 0.823 (0.70–0.94)
Validation:
Sens 79% (± 8)
Spec 83% (± 0)
AUC 0.833 (0.70–0.94)
Acc 0.81 (± 0.04)
  Cyranose 320 KNN; Naive Bayes; decision tree; neural network; SVM; random forest
Pulmonary infections—Tuberculosis (TB)
 Fend 2006 [109] n = 188 TB
n = 142 TB excluded
Diagnostic accuracy
(in vitro)
Sens 89% (80–97)
Spec 88% (85–97)
   Bloodhound BH-114 PSA; DFA; ANN
 Bruins 2013 [107] Training:
n = 15 TB
n = 15 HC
Validation:
n = 34 TB
n = 114 TB excluded
n = 46 HC
Diagnostic accuracy Training:
Sens 95.9% (92.9–97.7)
Spec 98.5% (96.2–99.4)
Validation: TB vs HC
Sens 93.5% (91.1–95.4)
Spec 85.3% (82.7–87.5)
Validation: TB vs TB excl
Sens 76.5% (57.98–88.5)
Spec 74.8% (64.5–82.9)
DiagNose ANN
 Coronel Teixeira 2017 [108] Training:
n = 23 TB
n = 46 HC
Validation:
n = 47 TB
n = 63 HC + asthma + COPD
Diagnostic accuracy Training:
Sens 91%
Spec 93%
Validation:
Sens 88%
Spec 92%
  Aeonose Tucker 3–like algorithm; ANN
 Mohamed 2017 [110] n = 67 TB
n = 56 HC
Diagnostic accuracy Sens 98.5% (92.1–100)
Spec 100% (93.5–100)
Accuracy 99.2%
   PEN3 PCA; ANN
 Saktiawati 2019 [111] Training:
n = 85 TB
n = 97 HC + TB excluded
Validation:
n = 128 TB
n = 159 TB
excluded
Diagnostic accuracy Training:
Sens 85% (75–92)
Spec 55% (44–65)
AUC 0.82 (0.72–0.88)
Validation:
Sens 78% (70–85)
Spec 42% (34–50)
AUC 0.72 (0.66–0.78)
  Aeonose ANN
 Zetola 2017 [112] n = 51 TB
n = 20 HC
Diagnostic accuracy Sens 94.1% (83.8–98.8)
Spec 90.0% (68.3–98.8)
   Prototype (QMB sensors) PCA; KNN
Pulmonary infections—Aspergillosis
 De Heer 2013 [102] n = 11 neutropenia
• n = 5 probable/proven aspergillosis
• n = 6 no aspergillus
Diagnostic accuracy Sens 100% (48–100)
Spec 83.3% (36–100)
AUC 0.933
CVA 90.9% (59–100)
   Cyranose 320 PCA; CDA
 De Heer 2016 [54] n = 9 CF colonised A. fumigatus
n = 18 CF not colonised
Diagnostic accuracy Sens 78%
Spec 94%
AUC 0.80–0.89
CVA 88.9%
   Cyranose 320 PCA; CDA
Pulmonary infections—Corona Virus Disease (COVID-19)
 Wintjens 2020 [114] n = 219 screened
• n = 57 COVID-19 positive
Diagnostic accuracy Sens 86% (74–93)
Spec 54% (46–62)
AUC 0.74
CVA 62%
   Aeonose ANN
Obstructive sleep apnoea (OSA)
Greulich 2013 [89] n = 40 OSA
n = 20 HC
Diagnostic accuracy OSA vs HC
Sens 93%
Spec 70%
AUC 0.85
   Cyranose 320 PCA
  N = 40 OSA
• 3 months CPAP ventilation
Therapeutic effect Before vs after CPAP
Sens 80%
Spec 65%
AUC 0.82
    
Incalzi 2014 [95] n = 50 OSA
• 1 night CPAP ventilation
Therapeutic effect Change in breathprint (visually different, no statistical analysis)    BIONOTE PCA; PLS-DA
Dragonieri 2015 [90] n = 19 OSA
n = 14 obese
n = 20 HC
Diagnostic accuracy Obese OSA vs HC
CVA% 97.4
AUC 1.00
Obese OSA vs obese
CVA% 67.6
AUC 0.77
Obese vs HC
CVA% 94.1
AUC 0.94
Cyranose 320 PCA; CDA; KNN
Kunos 2015 [96] n = 17 OSA
n = 9 non-OSA sleep disorder
n = 10 HC
• 7AM and 7PM sample
n = 26 HC
–7AM sample
Diagnostic accuracy OSA 7AM vs 7PM
Significantly different
Non-OSA or HC 7AM vs 7PM
Not significantly different
(Non-)OSA 7AM vs HC 7AM
Significantly different
Acc 77–81%
Cyranose 320 PCA
Dragonieri 2016 [92] Training:
n = 13 OSA
n = 15 COPD
n = 13 overlap
Validation:
n = 6 OSA
n = 6 COPD
n = 6 overlap
Diagnostic accuracy Training:
OSA vs overlap
CVA 96.2%
AUC 0.98
Validation:
OSA vs overlap
CVA 91.7%
AUC 1.00
Validation:
OSA vs COPD
CVA 75%
AUC 0.83
Cyranose 320 PCA; CDA
Scarlata 2017 [91] n = 40 OSA
• n = 20 hypoxic
n = 20 obese
n = 20 COPD
n = 56 HC
Diagnostic accuracy OSA vs HC
Acc 98–100%
Non-hypoxic vs hypoxic OSA
Acc 60–80%
HC vs COPD
Acc 100%
BIONOTE PLS-DA
Other—Acute respiratory distress syndrome (ARDS)
Bos 2014 [115] Training:
n = 40 ARDS
n = 66 HC
Validation:
n = 18 ARDS
n = 26 HC
Diagnostic accuracy Training:
Sens 95%
Spec 42%
AUC 0.72
Validation:
Sens 89%
Spec 50%
AUC 0.71
  Cyranose 320 Sparse-partial least square logistic regression
Other—Lung transplantation (LTx)
Kovacs 2013 [117] n = 16 LTx recipients
n = 33 HC
Diagnostic accuracy LTx recipients vs HC
Sens 63%
Spec 75%
AUC 0.825
   Cyranose 320 PCA; Linear regression
   Therapeutic effect Correlation breathprint—tacrolimus levels
R = -0.63
   Cyranose 320 PCA; Linear regression
Other—Pulmonary embolism (PE)
Fens 2010 [116] n = 20 PE
• n = 7 comorbidity
n = 20 PE excluded
• n = 13 comorbidity
Diagnostic accuracy Comorbidity: PE vs excluded
Acc 65%
AUC 0.55
No comorbidity: PE vs excluded
Acc 85%
AUC 0.81
No comorbidity: PE vs excluded (breathprint + Wells)
AUC 0.90
Cyranose 320 PCA
  1. An overview of eNose technology studies in lung diseases. Studies are divided per diagnosis and displayed in chronological order. Study results shown in sensitivity/specificity, AUC and CVA (if available). In case of a training and validation set, participant numbers and results of both set are shown. All presented results are statistical significant (p < 0.05) unless stated otherwise
  2. AATd  alpha-1-antitrypsin deficiency, acc accuracy, AUC  area under the curve, AAR  extrinsic asthma with allergic rhinitis, AEx  asbestos exposure, ANN  artificial neural network, ARD  benign asbestos related disease, BMI  body mass index, CDA  canonical discriminant analysis, CVA/CVV  cross-validated accuracy/value, d  days, DFA  discriminate function analysis, EBC  exhaled breath condensate, AECOPD  acute COPD exacerbation, EGFR  epidermal growth factor receptor, eos  eosinophils, FeNO  exhaled nitric oxide test, FVC  forced vital capacity, GOLD  global initiative for chronic obstructive lung disease, HC  healthy control (not suspected for studied disease, not diagnosed with other pulmonary disease), ICS  inhaled corticosteroids, IPF  idiopathic pulmonary fibrosis, KNN  k-nearest neighbours, LDA  linear discriminant analysis, MOS  metal oxide sensor, n6MWD  normalised six minute walking distance, OCS  oral corticosteroids, PAM  partitioning around medoids, PCA  principal component analysis, PEB  pure exhaled breath, PLS-DA  partial least squares discriminant analysis, PPM  potentially pathogenic microorganism, QMB  quartz microbalance, QoL  quality of life, ROC receiver operator characteristics, SCC  squamous cell carcinoma (B  bronchial, L  laryngeal), sens  sensitivity, SLR  Sensor Logic Relations, spec  specificity, SVM  support vector machines, TLC total lung capacity