Deep learning-based prediction of nodal metastasis in lung cancer using endobronchial ultrasound
Deep learning-based image classification using convolutional neural networks showed promising diagnostic accuracy for lung cancer nodal metastasis. Future clinical trials are warranted to validate the algorithm's efficacy in a prospective, large-cohort study.
Authors
- Masato Aragaki (ORCID: https://orcid.org/0000-0002-2943-764X)
- Terunaga Inage (ORCID: https://orcid.org/0000-0001-5585-1601)
- Kamran Kafi (ORCID: https://orcid.org/0000-0002-3649-081X)
- Kazuhiro Yasufuku (ORCID: https://orcid.org/0000-0002-3053-4156)
- Alexander Gregor (ORCID: https://orcid.org/0000-0001-7365-6084)
- Tsukasa Ishiwata (ORCID: https://orcid.org/0000-0003-2369-2698)
- Zhenchian Chen
- Nicholas Bernards (ORCID: https://orcid.org/0009-0007-7907-0317)
Institutions
- University Health Network (CA)
- Toronto General Hospital (CA)
- Cybernet Systems Corporation (Canada) (CA)
Publication Details
- Journal
- JTCVS Techniques
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1016/j.xjtc.2024.09.008
- Citations
- 6
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.51