Discriminating minimal residual disease status in multiple myeloma based on MRI: utility of radiomics and comparison of machine-learning methods.

Xiong, X; Zhu, Q; Zhou, Z; Qian, X; Hong, R; Dai, Y; Hu, C

Xiong, X; Zhu, Q; Zhou, Z; Qian, X; Hong, R; Dai, Y; Hu, C.

Afiliação

Xiong X; Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
Zhu Q; Department of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
Zhou Z; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, 215163, China.
Qian X; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
Hong R; Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
Dai Y; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, 215163, China.
Hu C; Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China. Electronic address: sudahuchunhong@163.com.

Clin Radiol ; 78(11): e839-e846, 2023 Nov.

Article em En | MEDLINE | ID: mdl-37586967

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article