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Machine learning-based prediction of invisible intraprostatic prostate cancer lesions on 68 Ga-PSMA-11 PET/CT in patients with primary prostate cancer.
Yi, Zhilong; Hu, Siqi; Lin, Xiaofeng; Zou, Qiong; Zou, MinHong; Zhang, Zhanlei; Xu, Lei; Jiang, Ningyi; Zhang, Yong.
Afiliación
  • Yi Z; Department of Nuclear Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
  • Hu S; Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Lin X; Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Zou Q; Department of Nuclear Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
  • Zou M; Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Zhang Z; Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Xu L; Department of Nuclear Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Jiang N; Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Zhang Y; Department of Nuclear Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China. jiangny@mail.sysu.edu.cn.
Eur J Nucl Med Mol Imaging ; 49(5): 1523-1534, 2022 04.
Article en En | MEDLINE | ID: mdl-34845536
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Tomografía Computarizada por Tomografía de Emisión de Positrones Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans / Male Idioma: En Revista: Eur J Nucl Med Mol Imaging Asunto de la revista: MEDICINA NUCLEAR Año: 2022 Tipo del documento: Article País de afiliación: China
Texto completo
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Tomografía Computarizada por Tomografía de Emisión de Positrones Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans / Male Idioma: En Revista: Eur J Nucl Med Mol Imaging Asunto de la revista: MEDICINA NUCLEAR Año: 2022 Tipo del documento: Article País de afiliación: China