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Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists' decisions of deep myometrial invasion.
Otani, Satoshi; Himoto, Yuki; Nishio, Mizuho; Fujimoto, Koji; Moribata, Yusaku; Yakami, Masahiro; Kurata, Yasuhisa; Hamanishi, Junzo; Ueda, Akihiko; Minamiguchi, Sachiko; Mandai, Masaki; Kido, Aki.
Afiliação
  • Otani S; Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.
  • Himoto Y; Department of Diagnostic Radiology and Nuclear Medicine, Kyoto University Hospital, Kyoto 606-8507, Japan. Electronic address: yhimoto@kuhp.kyoto-u.ac.jp.
  • Nishio M; Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.
  • Fujimoto K; Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.
  • Moribata Y; Department of Diagnostic Radiology and Nuclear Medicine, Kyoto University Hospital, Kyoto 606-8507, Japan; Preemptive Medicine and Lifestyle-related Disease Research Center, Kyoto University Hospital, Kyoto 606-8507, Japan.
  • Yakami M; Preemptive Medicine and Lifestyle-related Disease Research Center, Kyoto University Hospital, Kyoto 606-8507, Japan.
  • Kurata Y; Department of Diagnostic Radiology and Nuclear Medicine, Kyoto University Hospital, Kyoto 606-8507, Japan.
  • Hamanishi J; Department of Gynecology and Obstetrics, Kyoto University, Kyoto 606-8507, Japan.
  • Ueda A; Department of Gynecology and Obstetrics, Kyoto University, Kyoto 606-8507, Japan.
  • Minamiguchi S; Department of Diagnostic Pathology, Kyoto University, Kyoto 606-8507, Japan.
  • Mandai M; Department of Gynecology and Obstetrics, Kyoto University, Kyoto 606-8507, Japan.
  • Kido A; Department of Diagnostic Radiology and Nuclear Medicine, Kyoto University Hospital, Kyoto 606-8507, Japan.
Magn Reson Imaging ; 85: 161-167, 2022 01.
Article em En | MEDLINE | ID: mdl-34687853
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias do Endométrio / Aprendizado de Máquina Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias do Endométrio / Aprendizado de Máquina Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article