Abstract
Background
This study explores the application of machine learning (ML) in analyzing endobronchial ultrasound (EBUS) images for the detection of lymph node (LN) malignancy, aiming to augment diagnostic accuracy and efficiency. We investigated whether ML could outperform conventional classification systems in identifying malignant involvement of LNs, based on eight established sonographic features.
Methods
Retrospective data from two tertiary care hospital bronchoscopy units were utilized, encompassing healthcare reports of patients who had undergone EBUS between January 2017 and March 2023. The ML model was trained and tested using MATLAB, with 80% of the data allocated for training/validation, and 20% for testing. Performance was evaluated based on validation and testing accuracy, and receiver operating characteristic curves with comparing trained models and existing classification rules.
Results
The study analyzed 992 LNs, with 42.3% malignancy prevalence. Malignant LNs showed characteristic features such as larger size and distinct margins. The fine tuned models achieved testing accuracies of 95.9% and 96.4% for fine Gaussian SVM and KNN, respectively. Corresponding AUROC’s were 0.955 and 0.963, outperforming other similar studies and conventional analyses.
Conclusion
Fine tuned ML applications like SVM and KNN, can significantly enhance the analysis of EBUS images, improving diagnostic accuracy.
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This study received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. The authors declare that they have no financial, employment, or other significant/relevant relationships to disclose that could appear to have influenced the submitted work.
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All authors have made substantial contributions to all of the following: (1) the conception and design of the study, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, (3) final approval of the version to be submitted. All authors also agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The initial draft is authored by the researchers themselves. Subsequently, the English manuscript undergoes refinement and polishing by OpenAI, adhering to native language standards. After using the tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.
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Assistant Professor Fatos Dilan Koseoglu, Professor Ibrahim Onur Alici and Professor Orhan Er have no conflicts of interest or financial ties to disclose.
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This study was approved by the ethical committee of our institution (05042023/969). All patients provided signed informed consent for data acquisition anonymously.
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Koseoglu, F.D., Alıcı, I.O. & Er, O. Machine learning approaches in the interpretation of endobronchial ultrasound images: a comparative analysis. Surg Endosc 37, 9339–9346 (2023). https://doi.org/10.1007/s00464-023-10488-x
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DOI: https://doi.org/10.1007/s00464-023-10488-x