Recognition of breathprints of lung cancer and chronic obstructive pulmonary disease using the Aeonose^® electronic nose

Abstract

Objectives: There is a high unmet need in a non-invasive screening of lung cancer (LC). We conducted this single-center trial to evaluate the effectiveness of the electronic nose Aeonose® in LC recognition. Materials and Methods: Exhaled volatile organic compound (VOC) signatures were collected by Aeonose® in 42 incident and 78 prevalent LC patients, of them 29 LC patients in complete remission (LC CR), 33 healthy controls (HC) and 23 COPD patients. By dichotomous comparison of VOC’s between incident LC and HC, a discriminating algorithm was established and also applied to LC CR and COPD subjects. Area under Curve (AUC), sensitivity, specificity and Matthews’s correlation coefficient (MC) were used to interpret the data. Results: The established algorithm of Aeonose® signature allowed safe separation of LC and HC, showing an AUC of 0.92, sensitivity of 0.84 and a specificity of 0.97. When tested in a blinded fashion, the device recognized 19 out of 29 LC CR patients (=65.5%) as LC-positive, of which only five developed recurrent LC later on (after 18.6 months ; mean value ). Unfortunately, the algorithm also recognized 11 of 24 COPD patients as being LC positive (with only one of the 24 COPD patients developing LC 56 months after the measurement). Conclusion: The Aeonose® revealed some potential in distinguishing LC from HC, however, with low specificity when applying the algorithm in a blinded fashion to other disease cohorts. We conclude that relevant VOC signals originating from comorbidities in LC such as COPD may have erroneously led to the separation between LC and controls. Clinical trial registration: (NCT02951416)

Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath

“…All studies were feasibility studies. The most commonly used e-noses were the Cyranose 320 (n = 12), 16 , 17 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 66 Aeonose (n = 11), 18 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 and PEN3 (n = 3). 41 , 42 , 43 Furthermore, a large heterogeneous group of custom-made e-noses were used (n = 26).…”

“…A meta-analysis of all e-noses reporting diagnostic performance values was conducted on 46 studies, which included Aeonose (n = 11), 18 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 Cyranose 320 (n = 11), 16 , 17 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 PEN3 (n = 3), 41 , 42 , 43 and custom-made e-noses (n = 21). 6 , 8 , 9 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 Six studies 62 , 63 , 64 , 65 , 66 , 67 did not report diagnostic performance values and, therefore, could not be included in the meta-analysis.…”

Breath Analysis: A Promising Tool for Disease Diagnosis—The Role of Sensors

“…It is noteworthy that commercial MOS-based chemiresistive e-Noses have been developed and studied for breath analysis and diagnosis. “Common Invent e-Nose”, for instance, has been used for asthma phenotyping and exacerbation prediction [ 90 ], while “SpiroNose” [ 91 ] and “ AeoNose® ” [ 92 ] have been used for asthma, COPD and LC patients and COPD, LC patients and HC differentiation, respectively. “DiagNose”, composed of 12 different doped MOS-sensors, comprises another promising example since it managed to differentiate 10 individuals through their breath samples [ 93 ].…”

ILD: VOC-Analyse des Atemabdrucks als vielversprechende Methode der nicht invasiven Diagnostik

“…Tausende dieser VOCs können im ausgeatmeten Atem gefunden werden. Künstliche Sensorsysteme, die meist aus einer Reihe von Sensoren für verschiedene Chemikalien bestehen, sind in der Lage, VOC-Muster in der ausgeatmeten Luft zu erkennen und verwenden dann erlernte Algorithmen zur Klassifizierung des «Atemabdrucks» und Vergleich mit zuvor aufgezeichneten Proben [1, 2].…”