Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients.

Oh, Seungwon; Kang, Sae-Ryung; Oh, In-Jae; Kim, Min-Soo

Oh, Seungwon; Kang, Sae-Ryung; Oh, In-Jae; Kim, Min-Soo.

Afiliación

Oh S; Department of Mathematics and Statistics, Chonnam National University, Gwangju, Republic of Korea.
Kang SR; Department of Nuclear Medicine, Chonnam National University Medical School and Hwasun Hospital, Hwasun, Jeonnam, Republic of Korea.
Oh IJ; Department of Internal Medicine, Chonnam National University Medical School and Hwasun Hospital, Hwasun, Jeonnam, Republic of Korea. droij@jnu.ac.kr.
Kim MS; Department of Mathematics and Statistics, Chonnam National University, Gwangju, Republic of Korea. kimms@jnu.ac.kr.

BMC Bioinformatics ; 24(1): 39, 2023 Feb 06.

Article en En | MEDLINE | ID: mdl-36747153

Asunto(s)

Carcinoma de Pulmón de Células no Pequeñas; Aprendizaje Profundo; Neoplasias Pulmonares; Humanos; Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen; Neoplasias Pulmonares/diagnóstico por imagen; Fluorodesoxiglucosa F18; Radiofármacos; Tomografía de Emisión de Positrones; Estudios Retrospectivos

Palabras clave

Deep learning; FDG PET; Lung cancer; Multimodal learning; Survival prediction

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Carcinoma de Pulmón de Células no Pequeñas / Aprendizaje Profundo / Neoplasias Pulmonares Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article

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