Your browser doesn't support javascript.
loading
Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer.
Kawaji, Kodai; Nakajo, Masatoyo; Shinden, Yoshiaki; Jinguji, Megumi; Tani, Atsushi; Hirahara, Daisuke; Kitazono, Ikumi; Ohtsuka, Takao; Yoshiura, Takashi.
Afiliação
  • Kawaji K; Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Nakajo M; Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan. toyo.nakajo@dolphin.ocn.ne.jp.
  • Shinden Y; Department of Digestive Surgery, Breast and Thyroid Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Jinguji M; Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Tani A; Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Hirahara D; Department of Management Planning Division, Harada Academy, 2-54-4 Higashitaniyama, Kagoshima, 890-0113, Japan.
  • Kitazono I; Department of Pathology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Ohtsuka T; Department of Digestive Surgery, Breast and Thyroid Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Yoshiura T; Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
Mol Imaging Biol ; 25(5): 923-934, 2023 Oct.
Article em En | MEDLINE | ID: mdl-37193804
Palavras-chave
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Mol Imaging Biol Assunto da revista: BIOLOGIA MOLECULAR / DIAGNOSTICO POR IMAGEM Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Japão
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Mol Imaging Biol Assunto da revista: BIOLOGIA MOLECULAR / DIAGNOSTICO POR IMAGEM Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Japão