A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer

Huang, Chi‐Hsiang; Zeng, Chian; Wang, Yi-Chia; Peng, Hsien Yu; Lin, Ching-Yen; Chang, Che-Jui; Yang, Ya‐Chen

doi:10.3390/s18092845

Cited by 56 publications

(51 citation statements)

References 44 publications

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“…The electronic nose analysis followed our standardized procedure [20]. In brief, the collected air was sent back to the laboratory for analysis within 48 h. The air was analyzed using the Cyranose 320 electronic nose (Sensigent, CA, USA), which has 32 thin-film nanocomposite sensors.…”

Section: Electronic Nose Analysismentioning

confidence: 99%

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

Lin

Yang

2020

Respir Res

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Background: Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique. Methods: We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy. Results: A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement. Conclusions: There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.

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Section: Electronic Nose Analysismentioning

confidence: 99%

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

Lin

Yang

2020

Respir Res

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“…109 In 2018, several studies were presented or published on this topic and are also summarized in Table 2. 101,105,[110][111][112][113][114][115][116][117] With the exception of one study (de Vries et al 110 ), they all included patients with a suspicion of or proven lung cancer. The percentage of patients with early-stage disease varied between 12% and 79%.…”

Section: Breath-based Screeningmentioning

confidence: 99%

“…The percentage of patients with early-stage disease varied between 12% and 79%. 101,105,[111][112][113][114][115][116][117] In the study that included patients without a suspicion of lung cancer, exhaled breathprints were collected from 639 patients with COPD who were managed according to standard care and the incidence of lung cancer was monitored at 1 year after sampling. The study showed a sensitivity of 80% and a specificity of 90% for lung cancer prediction.…”

Section: Breath-based Screeningmentioning

confidence: 99%

Prevention and Early Detection for NSCLC: Advances in Thoracic Oncology 2018

Balata

Fong

Hendriks

et al. 2019

Journal of Thoracic Oncology

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Lung cancer remains the leading cause of cancer-related mortality worldwide. Tobacco consumption remains the most important risk factor. Although the prevalence of smoking has decreased overall, it continues to be a significant burden for global health. It is estimated that there are still nearly 1 billion cigarette smokers worldwide. Prevention strategies have largely focused on tobacco control and prevention. However, we have witnessed a dramatic increase in the use of e-cigarettes and other vaping products. Primary chemoprevention has historically not been a successful strategy for lung cancer; however, focused approaches in specific groups of patients at high risk for development of lung cancer are underway. The majority of cases with NSCLC are diagnosed with locally advanced or metastatic disease, where the overall prognosis remains very poor. Early-stage NSCLC on the other hand has a much better prognosis and can usually be treated radically with either surgical resection or radical radiotherapy, with relatively favorable long-term outcomes. In addition to image-based screening, other methods such as breath-based and biofluidbased approaches are now being investigated for early detection of NSCLC. This review will focus on recent advancements in the field of prevention, screening, and early detection of NSCLC.

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“…These patterns need to be "learned" first by the machine using artificial intelligence in a manner analogous to the training of dogs used in the original McCulloch study [21]. With this principle, it has now been possible to differentiate lung cancer from healthy subjects and from COPD patients [14,[26][27][28]. Currently, issues preventing the technique from being widespread in clinical practice include stability of the VOCs, and stability and interchangeability of the devices [29].…”

Section: Vocs In Lung Cancermentioning

confidence: 99%

The electronic nose: emerging biomarkers in lung cancer diagnostics

Geffen

Lamote

Costantini

et al. 2019

Breathe

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Lung cancer is very common and the most common cause of cancer death worldwide. Despite recent progress in the systemic treatment of lung cancer (checkpoint inhibitors and tyrosine kinase inhibitors), each year, >1.5 million people die due to this disease. Most lung cancer patients already have advanced disease at the time of diagnosis. Computed tomography screening of high-risk individuals can detect lung cancer at an earlier stage but at a cost of false-positive findings. Biomarkers could lead towards a reduction of these false-positive findings and earlier lung cancer diagnosis, and have the potential to improve outcomes and treatment monitoring. To date, there is a lack of such biomarkers for lung cancer and other thoracic malignancies, although electronic nose (e-nose)-derived biomarkers are of interest.E-nose techniques using exhaled breath component measurements can detect lung cancer with a sensitivity ranging from 71% to 96% and specificity from 33 to 100%. In some case series, such results have been validated but this is mostly using internal validation and hence, more work is needed. Furthermore, standardised sampling and analysis methods are lacking, impeding interstudy comparison and clinical implementation. In this narrative review, we provide an overview of the currently available data on E-nose technology for lung cancer detection.

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A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer

Cited by 56 publications

References 44 publications

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

Prevention and Early Detection for NSCLC: Advances in Thoracic Oncology 2018

The electronic nose: emerging biomarkers in lung cancer diagnostics

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