Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.

Lee, Taehee; Lee, Kyung Hee; Lee, Jong Hyuk; Park, Samina; Kim, Young Tae; Goo, Jin Mo; Kim, Hyungjin

Lee, Taehee; Lee, Kyung Hee; Lee, Jong Hyuk; Park, Samina; Kim, Young Tae; Goo, Jin Mo; Kim, Hyungjin.

Afiliación

Lee T; Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Lee KH; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Lee JH; Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
Park S; Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Kim YT; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Goo JM; Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Kim H; Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.

Eur Radiol ; 2023 Oct 20.

Article en En | MEDLINE | ID: mdl-37861801

Palabras clave

Carcinoma (non-small-cell lung); Deep learning; Prognosis; Tomography (X-ray computed)

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Texto completo: 1 Bases 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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