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Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer.
Haraguchi, Takafumi; Kobayashi, Yasuyuki; Hirahara, Daisuke; Kobayashi, Tatsuaki; Takaya, Eichi; Nagai, Mariko Takishita; Tomita, Hayato; Okamoto, Jun; Kanemaki, Yoshihide; Tsugawa, Koichiro.
Afiliação
  • Haraguchi T; Department of Advanced Biomedical Imaging and Informatics, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan.
  • Kobayashi Y; Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan.
  • Hirahara D; Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan.
  • Kobayashi T; Department of AI Research Lab, Harada Academy, Higashitaniyama, Kagoshima, Kagoshima, Japan.
  • Takaya E; Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan.
  • Nagai MT; Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan.
  • Tomita H; AI Lab, Tohoku University Hospital, Seiryomachi, Aoba-ku, Sendai, Miyagi, Japan.
  • Okamoto J; School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, Japan.
  • Kanemaki Y; Division of Breast and Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan.
  • Tsugawa K; Department of Radiology, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan.
J Xray Sci Technol ; 31(3): 627-640, 2023.
Article em En | MEDLINE | ID: mdl-37038802
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
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PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article