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  1. NTU Scholars
  2. 醫學院
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Please use this identifier to cite or link to this item: https://scholars.lib.ntu.edu.tw/handle/123456789/417348
Title: A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer
Authors: CHI-HSIANG HUANG 
Zeng, Chian
YI-CHIA WANG 
Peng, Hsin-Yi
Lin, Chia-Sheng
CHE-JUI CHANG 
HSIAO-YU YANG 
Keywords: electronic nose; lung cancer; sensor array;Electronic nose; Lung cancer; Sensor array
Issue Date: 28-Aug-2018
Publisher: MDPI
Journal Volume: 18
Journal Issue: 9
Source: Sensors (Basel, Switzerland)
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.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052569697&doi=10.3390%2fs18092845&partnerID=40&md5=6b5a71af55b1779ca187d423361ff692
https://scholars.lib.ntu.edu.tw/handle/123456789/417348
ISSN: 1424-8220
DOI: 10.3390/s18092845
SDG/Keyword: 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
[SDGs]SDG3
Appears in Collections:醫學系

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臺大位居世界頂尖大學之列,為永久珍藏及向國際展現本校豐碩的研究成果及學術能量,圖書館整合機構典藏(NTUR)與學術庫(AH)不同功能平台,成為臺大學術典藏NTU scholars。期能整合研究能量、促進交流合作、保存學術產出、推廣研究成果。

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