Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques.

Sim, Yongsik; Kim, Minjae; Kim, Jinna; Lee, Seung-Koo; Han, Kyunghwa; Sohn, Beomseok

Sim, Yongsik; Kim, Minjae; Kim, Jinna; Lee, Seung-Koo; Han, Kyunghwa; Sohn, Beomseok.

Afiliación

Sim Y; Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
Kim M; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea.
Kim J; Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
Lee SK; Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
Han K; Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
Sohn B; Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea. beomseoksohn@gmail.com.

Eur Radiol ; 2023 Oct 18.

Article en En | MEDLINE | ID: mdl-37848774

Palabras clave

Diffusion magnetic resonance imaging; Machine learning; Oropharynx; Papillomavirus infections; Squamous cell carcinoma of head and neck

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Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Corea del Sur

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