Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image.

Pan, Lin; He, Tian; Huang, Zihan; Chen, Shuai; Zhang, Junrong; Zheng, Shaohua; Chen, Xianqiang

Pan, Lin; He, Tian; Huang, Zihan; Chen, Shuai; Zhang, Junrong; Zheng, Shaohua; Chen, Xianqiang.

Afiliación

Pan L; College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China.
He T; College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China.
Huang Z; School of Future Technology, Harbin Institute of Technology, Harbin, 150000, China.
Chen S; Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
Zhang J; Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
Zheng S; College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China. sunphen@fzu.edu.cn.
Chen X; Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China. cxq760818@163.com.

Abdom Radiol (NY) ; 48(4): 1246-1259, 2023 04.

Article en En | MEDLINE | ID: mdl-36859730

Asunto(s)

Neoplasias Colorrectales; Aprendizaje Profundo; Humanos; Tomografía Computarizada por Rayos X; Neoplasias Colorrectales/diagnóstico por imagen; Estudios Retrospectivos

Palabras clave

Deep learning; Obstructive colorectal cancer; Peritumoral region; Radiomics; ResNet

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / Aprendizaje Profundo Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Abdom Radiol (NY) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

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