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

Sensors
Chi-Hsiang HuangHsiao-Yu Yang

Abstract

Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79⁻1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80⁻0.99) by the supportive vector machine technique. The combination of the sensor array technique and ...Continue Reading

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Citations

Jun 20, 2019·Sensors·Henike Guilherme Jordan VossSergio Luiz Stevan
Jul 30, 2020·Biosensors·Chiara BaldiniAlessandro Tonacci
Apr 14, 2020·Breathe·Wouter H van GeffenJan van Meerbeeck
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Aug 26, 2021·Lung Cancer : Targets and Therapy·Nir PeledRaul Bitran
Sep 19, 2021·Respiratory Research·I G van der SarC C Moor

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Software Mentioned

MASS
kernlab
SPSS Statistics
R

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