Volatile organic compounds of biofluids for detecting lung cancer by an electronic nose based on artificial neural network

Abstract

Lung cancer (LC) incidence represents 11.5% of all new cancers, resulting in 1.72 million deaths worldwide in 2015. With the aim to investigate the capability of the electronic nose (e-nose) technology for detecting and differentiating complex mixtures of volatile organic compounds in biofluids ex-vivo, we enrolled 50 patients with suspected LC and 50 matching controls. Tissue biopsy was taken from suspicious lung mass for histopathological evaluation and blood, exhaled breath, and urine samples were collected from all participants and qualitatively processed using e-nose. Odor-print patterns were further analysed using the principal component analysis (PCA) and artificial neural network (ANN) analysis. Adenocarcinoma, non-small cell LC and squamous cell carcinoma were the predominant pathological types among LC patients. PCA cluster-plots showed a clear distinction between LC patients and controls for all biological samples; where the overall success ratios of classification for principal components #1 and #2 were: 95.46, 82.01, and 91.66% for blood, breath and urine samples, respectively. Moreover, ANN showed a better discrimination between LC patients and controls with success ratios of 95.74, 91.67 and 100% for blood, breath and urine samples, respectively. The e-nose is an easy noninvasive tool, capable of identifying LC patients from controls with great precision.

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

“…Whether an eNose can differentiate lung cancer patients from healthy controls, patients with benign lung nodules or (former) smokers, has been investigated in different cohorts. All studies in (non-) small cell lung cancer ((N)SCLC) showed significant results, albeit with a wide range in reported sensitivity (71–99%) and specificity (13–100%) [ 68 – 80 ]. Smoking status of participants did not seem to influence accuracy of an eNose for detecting cancer [ 77 ].…”

Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

“…The measured results consisted of vectors of 64 values every 20 s for each of the three sensors. Previous studies, with varying degrees of success, have used the “electronic nose” device to diagnose lung [ 15 , 17 , 25 , 26 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], colon [ 36 , 37 ], head and neck [ 34 , 37 , 38 ], urinary bladder [ 37 ], prostate [ 39 ], stomach [ 28 ] and breast cancer [ 27 ].…”

Electronic nose versus quadrupole mass spectrometry for identifying viral hepatitis C patients

“…were extracted and further analysed, as we detailed earlier. [13][14][15] Sensor Name Reference Substance Ontario, Canada). The air was drawn from the top of blood samples using a syringe then dissolved in 10 ml of Alpha-Q purified water (Millipore, Bedford, MA, USA), which acted as a solvent in a volumetric flask.…”

“…12 Moreover, the recent availability of highly sensitive and portable technologies like Electronic Nose (E-Nose) has provided necessary tools for detecting disease-specific VOCs in clinical matrices quickly and cheaply. [13][14][15] An E-Nose is an artificial gas-sensing system consisting of an array of non-specific chemical sensors of variable numbers, usually between 10 and 32, capable of detecting and classifying VOCs using pattern recognition algorithms. 14 However, E-Noses can only qualitatively detect complex odour mixtures of VOCs associated with various diseases without identifying individual chemical species.…”

“…An E‐Nose is an artificial gas‐sensing system consisting of an array of non‐specific chemical sensors of variable numbers, usually between 10 and 32, capable of detecting and classifying VOCs using pattern recognition algorithms 14 . However, E‐Noses can only qualitatively detect complex odour mixtures of VOCs associated with various diseases without identifying individual chemical species 13‐15 . That is why we usually rely on MS/MS instruments to quantitatively detect specific compounds associated with a particular disease 10 …”