Despite extensive investigation into using electronic nose (eNose) technology for diagnosing lung cancer, the challenge of cross-site validation has remained unaddressed, limiting its widespread adoption. To bridge this gap, researchers conducted a multi-centre prospective study to validate eNose-based lung cancer diagnosis across 2 referral centres, a task never before undertaken in existing literature. Over the period from 2019 to 2022, patients with lung cancer, healthy controls, and diseased controls were recruited from both sites. Deep learning models were developed using a training cohort from 1 centre and subsequently tested on a separate cohort from the other. To enhance model performance, the study group employed Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) techniques, both with and without fine-tuning.
The study comprised 231 participants, with the training/validation cohort comprising 168 individuals and the test cohort comprising 63 individuals. While the model demonstrated satisfactory performance within the validation cohort from the same hospital, its direct application to the test cohort yielded suboptimal results. However, the implementation of data augmentation methods in the training cohort significantly improved performance, with SDA and NSA achieving impressive AUC values. Further enhancements were observed with the application of fine-tuning methods, resulting in notably higher AUC scores.
The findings highlight the feasibility of achieving cross-site validation for eNose-based lung cancer diagnosis through the judicious use of data augmentation and fine-tuning techniques. Consequently, eNose breathprints present themselves as a promising, non-invasive, and potentially scalable solution for the detection of lung cancer.
Source:Â respiratory-research.biomedcentral.com/articles/10.1186/s12931-024-02840-z
Originally Published By Physicianâs Weekly. Reused by Medicom Medical Publishers with permission.
©2024 Physicianâs Weekly. All rights reserved. No works may be reproduced without expressed written consent from Physicianâs Weekly. Inquire about licensing here. No article should be construed as medical advice and is not intended as such by the authors or by Physicianâs Weekly.
Posted on
Previous Article
« Analysing HR-QoL trends and predictors in lung cancer Next Article
Dynamic FDG PET/CTO enhances lung cancer subtype differentiation and EGFR mutation status prediction »
« Analysing HR-QoL trends and predictors in lung cancer Next Article
Dynamic FDG PET/CTO enhances lung cancer subtype differentiation and EGFR mutation status prediction »
Related Articles
© 2024 Medicom Medical Publishers. All rights reserved. Terms and Conditions | Privacy Policy
HEAD OFFICE
Laarderhoogtweg 25
1101 EB Amsterdam
The Netherlands
T: +31 85 4012 560
E: publishers@medicom-publishers.com