Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI.

Li, Mengjuan; Chen, Tong; Zhao, Wenlu; Wei, Chaogang; Li, Xiaobo; Duan, Shaofeng; Ji, Libiao; Lu, Zhihua; Shen, Junkang

Li, Mengjuan; Chen, Tong; Zhao, Wenlu; Wei, Chaogang; Li, Xiaobo; Duan, Shaofeng; Ji, Libiao; Lu, Zhihua; Shen, Junkang.

Afiliação

Li M; Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.
Chen T; Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.
Zhao W; Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.
Wei C; Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.
Li X; GE Healthcare Life Science, Shanghai 200000, China.
Duan S; GE Healthcare Life Science, Shanghai 200000, China.
Ji L; Department of Radiology, The Affiliated Changshu Hospital of Soochow University, Suzhou 215501, China.
Lu Z; Department of Radiology, The Affiliated Changshu Hospital of Soochow University, Suzhou 215501, China.
Shen J; Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.

Quant Imaging Med Surg ; 10(2): 368-379, 2020 Feb.

Article em En | MEDLINE | ID: mdl-32190563

Palavras-chave

Prostate cancer; classification; clinical risk factors; machine learning; radiomics

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

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