A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer
Journal
Sensors (Basel, Switzerland)
Journal Volume
18
Journal Issue
9
Date Issued
2018-08-28
Author(s)
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 machine learning can detect lung cancer with high accuracy.
Subjects
electronic nose; lung cancer; sensor array
Electronic nose; Lung cancer; Sensor array
SDGs
Other Subjects
Air quality; Artificial intelligence; Biological organs; Carbon nanotubes; Chemical detection; Chemical sensors; Chemotherapy; Computerized tomography; Diagnosis; Discriminant analysis; Electronic nose; Learning algorithms; Learning systems; Sensor arrays; Tumors; Yarn; Carbon nanotube sensors; Diagnostic accuracy; Linear discriminant analysis; Lung Cancer; Lung cancer screening; Machine learning techniques; Metastatic lung cancer; Receiver operating characteristic curves; Diseases; aged; breath analysis; case control study; clinical trial; devices; early cancer diagnosis; female; human; lung tumor; machine learning; male; middle aged; procedures; prospective study; reproducibility; support vector machine; Aged; Breath Tests; Case-Control Studies; Early Detection of Cancer; Female; Humans; Lung Neoplasms; Machine Learning; Male; Middle Aged; Prospective Studies; Reproducibility of Results; Support Vector Machine
Publisher
MDPI
Type
journal article