Identifying Prognostic Markers From Clinical, Radiomics, and Deep Learning Imaging Features for Gastric Cancer Survival Prediction.

Hao, Degan; Li, Qiong; Feng, Qiu-Xia; Qi, Liang; Liu, Xi-Sheng; Arefan, Dooman; Zhang, Yu-Dong; Wu, Shandong

Hao, Degan; Li, Qiong; Feng, Qiu-Xia; Qi, Liang; Liu, Xi-Sheng; Arefan, Dooman; Zhang, Yu-Dong; Wu, Shandong.

Afiliación

Hao D; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States.
Li Q; Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Feng QX; Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Qi L; Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Liu XS; Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Arefan D; Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States.
Zhang YD; Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Wu S; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States.

Front Oncol ; 11: 725889, 2021.

Article en En | MEDLINE | ID: mdl-35186707

Palabras clave

deep learning - CNN; gastric cancer; multi-modal data analysis; radiomics; survival analysis (source: MeSH NLM)

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Oncol Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza

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