Electronic nose in differentiating and ascertaining clinical status among patients with pulmonary nontuberculous mycobacteria: A prospective multicenter study

Lee, Meng-Rui; Huang, Hsiao‐Ling; Huang, Wei-Chang; Wu, Shang-Yu; Liu, Pang-Chun; Wu, Jan‐Jan; Cheng, Meng-Hsuan; Sheu, Chau‐Chyun; Tang, Kea-Tiong; Wang, Jann‐Yuan; Ho, Chao‐Chi; Shih, Jin‐Yuan; Chong, Inn‐Wen

doi:10.1016/j.jinf.2023.06.012

Cited by 1 publication

(4 citation statements)

References 9 publications

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“…4 Forest plot of subgroup analysis. OR, odds ratio consistent with our previous which also found inferior performance in the smoker group [9]. Considering smoking remains a major risk factor for lung cancer [28], Detecting lung cancer in individuals who smoke or have chronic obstructive pulmonary disease is crucial for early intervention and treatment of lung cancer [29].…”

Section: Discussionsupporting

confidence: 80%

“…The breath sample collection process has been described in our previous study [9]. Briefly, the breath sampling system included a one-way VBMax™ filter and two one-litre multi-layer foil gas sampling bags.…”

Section: Breath Sample Collectionmentioning

confidence: 99%

“…Leveraging Metal-Oxide-Semiconductor (MOS) gas sensors sourced from Figaro USA, Inc. and Nissha FIS, Inc., the SEXTANT system operates based on oxidation-reduction sensing mechanisms. These sensors have been enhanced with different materials to optimize both selectivity and sensitivity in detecting various gases [9]. A video describing the process of breath analysis using eNose is also available as Additional File 1: Supplementary Video.…”

Section: Breath Analysis Using Enosementioning

confidence: 99%

“…On the other hand, algorithms for eNose breathprint analysis is also in evolution [8]. Deep learning involving convoluted neural network (CNN) is novel and emerging technique for breathprint analysis [8,9]. Some analytic approaches such as transfer learning and data augmentation have been applied in other aspects of biomedical imaging researches [10].…”

Section: Introductionmentioning

confidence: 99%

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Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study

Lee,

Kao,

Hsieh

et al. 2024

Respir Res

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Background Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed. Methods Patients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site. Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) methods with or without fine-tuning was applied to improve performance. Results In this study, 231 participants were enrolled, comprising a training/validation cohort of 168 individuals (90 with lung cancer, 16 healthy controls, and 62 diseased controls) and a test cohort of 63 individuals (28 with lung cancer, 10 healthy controls, and 25 diseased controls). The model has satisfactory results in the validation cohort from the same hospital while directly applying the trained model to the test cohort yielded suboptimal results (AUC, 0.61, 95% CI: 0.47─0.76). The performance improved after applying data augmentation methods in the training cohort (SDA, AUC: 0.89 [0.81─0.97]; NSA, AUC:0.90 [0.89─1.00]). Additionally, after applying fine-tuning methods, the performance further improved (SDA plus fine-tuning, AUC:0.95 [0.89─1.00]; NSA plus fine-tuning, AUC:0.95 [0.90─1.00]). Conclusion Our study revealed that deep learning models developed for eNose breathprint can achieve cross-site validation with data augmentation and fine-tuning. Accordingly, eNose breathprints emerge as a convenient, non-invasive, and potentially generalizable solution for lung cancer detection. Clinical trial registration This study is not a clinical trial and was therefore not registered.

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Section: Discussionsupporting

confidence: 80%

Section: Breath Sample Collectionmentioning

confidence: 99%

Section: Breath Analysis Using Enosementioning

confidence: 99%