Evaluation of the peritumoral features using radiomics and deep learning technology in non-spiculated and noncalcified masses of the breast on mammography.

Guo, Fei; Li, Qiyang; Gao, Fei; Huang, Chencui; Zhang, Fandong; Xu, Jingxu; Xu, Ye; Li, Yuanzhou; Sun, Jianghong; Jiang, Li

Guo, Fei; Li, Qiyang; Gao, Fei; Huang, Chencui; Zhang, Fandong; Xu, Jingxu; Xu, Ye; Li, Yuanzhou; Sun, Jianghong; Jiang, Li.

Afiliación

Guo F; Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.
Li Q; Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.
Gao F; Deepwise Artificial Intelligence Lab, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, China.
Huang C; Deepwise Artificial Intelligence Lab, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, China.
Zhang F; Deepwise Artificial Intelligence Lab, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, China.
Xu J; Deepwise Artificial Intelligence Lab, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, China.
Xu Y; Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.
Li Y; Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.
Sun J; Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.
Jiang L; Department of Oncology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.

Front Oncol ; 12: 1026552, 2022.

Article en En | MEDLINE | ID: mdl-36479079

Palabras clave

deep learning; mammography; non-spiculated and noncalcified masses; peritumoral features; radiomics

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Oncol Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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