Prediction of High-Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods.

Liu, Jianfang; Zeng, Piaoe; Guo, Wei; Wang, Chunjie; Geng, Yayuan; Lang, Ning; Yuan, Huishu

Liu, Jianfang; Zeng, Piaoe; Guo, Wei; Wang, Chunjie; Geng, Yayuan; Lang, Ning; Yuan, Huishu.

Afiliação

Liu J; Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, People's Republic of China.
Zeng P; Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, People's Republic of China.
Guo W; Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, People's Republic of China.
Wang C; Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, People's Republic of China.
Geng Y; Huiying Medical Technology (Beijing) Co., Ltd, Beijing, China.
Lang N; Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, People's Republic of China.
Yuan H; Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, People's Republic of China.

J Magn Reson Imaging ; 54(4): 1303-1311, 2021 10.

Article em En | MEDLINE | ID: mdl-33979466

Assuntos

Mieloma Múltiplo; Análise Citogenética; Humanos; Aprendizado de Máquina; Imageamento por Ressonância Magnética; Espectroscopia de Ressonância Magnética; Mieloma Múltiplo/diagnóstico por imagem; Mieloma Múltiplo/genética; Estudos Retrospectivos

Palavras-chave

classifier; cytogenetics; magnetic resonance imaging; multiple myeloma; radiomics

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Mieloma Múltiplo Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

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