Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data
Abstract Background Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported. Objective The objectives of this stu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | Respiratory Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12931-024-02668-7 |
| _version_ | 1797349748330463232 |
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| author | Ke-Cheng Chen Shuenn-Wen Kuo Ruei-Hao Shie Hsiao-Yu Yang |
| author_facet | Ke-Cheng Chen Shuenn-Wen Kuo Ruei-Hao Shie Hsiao-Yu Yang |
| author_sort | Ke-Cheng Chen |
| collection | DOAJ |
| description | Abstract Background Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported. Objective The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm. Methods We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard. Results We enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84–0.91), the summary specificity was 1.00 (95% CI 0.85–1.00), the AUC was 0.96 (95% CI 0.94–0.98), the pAUC was 0.92 (95% CI 0.89–0.96), and the DOR was 207.62 (95% CI 24.62–924.64). Conclusion Electronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses. |
| first_indexed | 2024-03-08T12:35:55Z |
| format | Article |
| id | doaj.art-e0e0afb02c4b4391b36c22852eeed2bd |
| institution | Directory Open Access Journal |
| issn | 1465-993X |
| language | English |
| last_indexed | 2024-03-08T12:35:55Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Respiratory Research |
| spelling | doaj.art-e0e0afb02c4b4391b36c22852eeed2bd2024-01-21T12:31:33ZengBMCRespiratory Research1465-993X2024-01-0125111010.1186/s12931-024-02668-7Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced dataKe-Cheng Chen0Shuenn-Wen Kuo1Ruei-Hao Shie2Hsiao-Yu Yang3Division of Thoracic Surgery, Department of Surgery, National Taiwan University HospitalDivision of Thoracic Surgery, Department of Surgery, National Taiwan University HospitalGreen Energy and Environmental Research Laboratories, Industrial Technology Research InstituteInstitute of Environmental and Occupational Health Sciences, National Taiwan University College of Public HealthAbstract Background Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported. Objective The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm. Methods We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard. Results We enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84–0.91), the summary specificity was 1.00 (95% CI 0.85–1.00), the AUC was 0.96 (95% CI 0.94–0.98), the pAUC was 0.92 (95% CI 0.89–0.96), and the DOR was 207.62 (95% CI 24.62–924.64). Conclusion Electronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses.https://doi.org/10.1186/s12931-024-02668-7Volatile metaboliteBreathomicsImbalanced learningElectronic nose |
| spellingShingle | Ke-Cheng Chen Shuenn-Wen Kuo Ruei-Hao Shie Hsiao-Yu Yang Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data Respiratory Research Volatile metabolite Breathomics Imbalanced learning Electronic nose |
| title | Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data |
| title_full | Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data |
| title_fullStr | Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data |
| title_full_unstemmed | Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data |
| title_short | Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data |
| title_sort | advancing accuracy in breath testing for lung cancer strategies for improving diagnostic precision in imbalanced data |
| topic | Volatile metabolite Breathomics Imbalanced learning Electronic nose |
| url | https://doi.org/10.1186/s12931-024-02668-7 |
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