VOC pattern recognition of lung cancer: a comparative evaluation of different dog- and eNose-based strategies using different sampling materials

Biehl, Wiebke; Hattesohl, Akira; Jörres, Rudolf A.; Duell, Thomas; Althöhn, Ulrike; Koczulla, Andreas Rembert; Schmetzer, Helga

doi:10.1080/0284186x.2019.1634284

Cited by 19 publications

(29 citation statements)

References 21 publications

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“…To this day, there are no known studies describing specific VOC patterns in fibrotic lung diseases. However, there are some successful publications with regard to a screening of various respiratory and systemic diseases, e.g., lung cancer, diabetes mellitus, or even evaluating VOC profiles in critically ill patients [22,23,24]. Without any doubt, the process of ILD diagnosis would profit from new non-invasive forms of diagnostics aside of imaging.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Ability of Electronic Nose Technology to Recognize Interstitial Lung Diseases (ILD) by Non-Invasive Breath Screening of Exhaled Volatile Compounds (VOC): A Pilot Study from the European IPF Registry (eurIPFreg) and Biobank

Krauss

Haberer

Maurer

et al. 2019

JCM

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Background: There is an increasing interest in employing electronic nose technology in the diagnosis and monitoring of lung diseases. Interstitial lung diseases (ILD) are challenging in regard to setting an accurate diagnosis in a timely manner. Thus, there is a high unmet need in non-invasive diagnostic tests. This single-center explorative study aimed to evaluate the usefulness of electronic nose (Aeonose®) in the diagnosis of ILDs. Methods: Exhaled volatile organic compound (VOC) signatures were obtained by Aeonose® in 174 ILD patients, 23 patients with chronic obstructive pulmonary disease (COPD), and 33 healthy controls (HC). Results: By dichotomous comparison of VOC’s between ILD, COPD, and HC, a discriminating algorithm was established. In addition, direct analyses between the ILD subgroups, e.g., cryptogenic organizing pneumonia (COP, n = 28), idiopathic pulmonary fibrosis (IPF, n = 51), and connective tissue disease-associated ILD (CTD-ILD, n = 25) were performed. Area under the Curve (AUC) and Matthews’s correlation coefficient (MCC) were used to interpret the data. In direct comparison of the different ILD subgroups to HC, the algorithms developed on the basis of the Aeonose® signatures allowed safe separation between IPF vs. HC (AUC of 0.95, MCC of 0.73), COP vs. HC (AUC 0.89, MCC 0.67), and CTD-ILD vs. HC (AUC 0.90, MCC 0.69). Additionally, to a case-control study design, the breath patterns of ILD subgroups were compared to each other. Following this approach, the sensitivity and specificity showed a relevant drop, which results in a poorer performance of the algorithm to separate the different ILD subgroups (IPF vs. COP with MCC 0.49, IPF vs. CTD-ILD with MCC 0.55, and COP vs. CT-ILD with MCC 0.40). Conclusions: The Aeonose® showed some potential in separating ILD subgroups from HC. Unfortunately, when applying the algorithm to distinguish ILD subgroups from each other, the device showed low specificity. We suggest that artificial intelligence or principle compound analysis-based studies of a much broader data set of patients with ILDs may be much better suited to train these devices.

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Section: Discussionmentioning

confidence: 99%

Exploring the Ability of Electronic Nose Technology to Recognize Interstitial Lung Diseases (ILD) by Non-Invasive Breath Screening of Exhaled Volatile Compounds (VOC): A Pilot Study from the European IPF Registry (eurIPFreg) and Biobank

Krauss

Haberer

Maurer

et al. 2019

JCM

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“…The race of the conditioned dog as well as factors like age, diet, motivation or health status could influence the suitability of a dog. To get more objective results it might be useful to have the same samples evaluated by several dogs, as it was already tried in other studies [ 9 , 13 , 17 , 18 ]. Another factor influencing detection results had been demonstrated by Biehl et al, who described different outcomes depending on previous experiences of the dog, e.g.…”

Section: Discussionmentioning

confidence: 99%

“…police dog vs. household dog vs. tumor sniffing dog with a better outcome for experienced dogs. However, due to the low number of dogs included in detection dog studies, it is difficult to prove an influence of the individual dog on the result statistically [ 13 , 17 ].…”

Section: Discussionmentioning

confidence: 99%

“…Across published studies, a sensitivity of 78% and a specificity of 71.5% related to olfactory detection of lung cancer samples by sniffer dogs was reported [ 7 – 11 ]. Those study results are encouraging but published studies differ in terms of the experimental setup, kind of odor samples, sample collection methods, dogs’ characteristics and dog training methods as well as in results presented in terms of detection sensitivity and specificity [ 12 , 13 ]. In addition, it could not be clarified so far what chemical compounds dogs respond to and the quantity of these compounds.…”

Section: Introductionmentioning

confidence: 99%

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Sniffer dogs can identify lung cancer patients from breath and urine samples

et al. 2021

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Background Lung cancer is the most common oncological cause of death in the Western world. Early diagnosis is critical for successful treatment. However, no effective screening methods exist. A promising approach could be the use of volatile organic compounds as diagnostic biomarkers. To date there are several studies, in which dogs were trained to discriminate cancer samples from controls. In this study we evaluated the abilities of specifically trained dogs to distinguish samples derived from lung cancer patients of various tumor stages from matched healthy controls. Methods This single center, double-blind clinical trial was approved by the local ethics committee, project no FF20/2016. The dog was conditioned with urine and breath samples of 36 cancer patients and 150 controls; afterwards, further 246 patients were included: 41 lung cancer patients comprising all stages and 205 healthy controls. From each patient two breath and urine samples were collected and shock frozen. Only samples from new subjects were presented to the dog during study phase randomized, double-blinded. This resulted in a specific conditioned reaction pointing to the cancer sample. Results Using a combination of urine and breath samples, the dog correctly predicted 40 out of 41 cancer samples, corresponding to an overall detection rate of cancer samples of 97.6% (95% CI [87.1, 99.9%]). Using urine samples only the dog achieved a detection rate of 87.8% (95% CI [73.8, 95.9%]). With breath samples, the dog correctly identified cancer in 32 of 41 samples, resulting in a detection rate of 78% (95% CI [62.4, 89.4%]). Conclusions It is known from current literature that breath and urine samples carry VOCs pointing to cancer growth. We conclude that olfactory detection of lung cancer by specifically trained dogs is highly suggestive to be a simple and non-invasive tool to detect lung cancer. To translate this approach into practice further target compounds need to be identified.

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“…However, as animals are living organisms, their performances can be subject to variations. Biehl et al 2019 reported that literature data show that some dog trainers included only one dog in scent detection, whereas others had ve to six dogs and collected the individual dogs' data(54) McCulloch et al 2006. state that the sni ng quality of all dogs was comparable, and therefore the results obtained were similar.…”

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confidence: 99%

Remote Medical Scent Detection of Cancer and Infectious Diseases With Dogs and Rats: a Systematic Review

Bauër

Leemans

Audureau

et al. 2021

Preprint

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Background: Remote medical scent detection of cancer and infectious diseases with dogs and rats has been an increasing field of research these last 20 years. If validated, the possibility of implementing such a technique in the clinic raises many hopes. This systematic review was performed to determine the evidence and performance of such methods and assess their potential relevance in the clinic.Methods: Pubmed and Web of Science databases were independently searched based on PRISMA standards. We included studies aiming at detecting cancers and infectious diseases affecting humans with dogs or rats. We excluded studies using other animals, studies aiming to detect agricultural diseases, diseases affecting animals, and others such as diabetes and neurodegenerative diseases. Only original articles were included. Data about patients’ selection, samples, animal characteristics, animal training and testing configurations, and performances were recorded.Results: A total of 62 studies were included. Sensitivity and specificity varied a lot among studies: While some publications report low sensitivities of 17% and specificities around 29%, others achieve rates of 100% sensitivity and specificity. Only 6 studies were evaluated in a double-blind screening like situation. In general, the risk of performance bias was high in most evaluated studies, and the quality of the evidence found was low.Conclusions: Medical detection using animals’ sense of smell lacks evidence and performances so far to be applied in the clinic. What odours the animals detect is not well understood. Further research should be conducted, focusing on patient selection, samples (choice of materials, standardization), and testing conditions. Interpolations of such results to free running detection (direct contact with humans) should be taken with extreme caution.

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VOC pattern recognition of lung cancer: a comparative evaluation of different dog- and eNose-based strategies using different sampling materials

Cited by 19 publications

References 21 publications

Exploring the Ability of Electronic Nose Technology to Recognize Interstitial Lung Diseases (ILD) by Non-Invasive Breath Screening of Exhaled Volatile Compounds (VOC): A Pilot Study from the European IPF Registry (eurIPFreg) and Biobank

Exploring the Ability of Electronic Nose Technology to Recognize Interstitial Lung Diseases (ILD) by Non-Invasive Breath Screening of Exhaled Volatile Compounds (VOC): A Pilot Study from the European IPF Registry (eurIPFreg) and Biobank

Sniffer dogs can identify lung cancer patients from breath and urine samples

Remote Medical Scent Detection of Cancer and Infectious Diseases With Dogs and Rats: a Systematic Review

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