One 3D VOI-based deep learning radiomics strategy, clinical model and radiologists for predicting lymph node metastases in pancreatic ductal adenocarcinoma based on multiphasic contrast-enhanced computer tomography.

Liao, Hongfan; Yang, Junjun; Li, Yongmei; Liang, Hongwei; Ye, Junyong; Liu, Yanbing

Liao, Hongfan; Yang, Junjun; Li, Yongmei; Liang, Hongwei; Ye, Junyong; Liu, Yanbing.

Afiliação

Liao H; College of Medical Informatics, Chongqing Medical University, Chongqing, China.
Yang J; Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, Chongqing University, Chongqing, China.
Li Y; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Liang H; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Ye J; Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, Chongqing University, Chongqing, China.
Liu Y; College of Medical Informatics, Chongqing Medical University, Chongqing, China.

Front Oncol ; 12: 990156, 2022.

Article em En | MEDLINE | ID: mdl-36158647

Palavras-chave

contrast-enhanced computer tomography; deep learning; lymph node metastases; pancreatic ductal adenocarcinoma; radiomics

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Oncol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

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