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Prostate-specific Membrane Antigen: Interpretation Criteria, Standardized Reporting, and the Use of Machine Learning.
Seifert, Robert; Gafita, Andrei; Solnes, Lilja B; Iagaru, Andrei.
Afiliación
  • Seifert R; Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland; Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany. Electronic address: robert.seifert@unibe.ch.
  • Gafita A; Division of Nuclear Medicine and Molecular Imaging, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Solnes LB; Division of Nuclear Medicine and Molecular Imaging, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Iagaru A; Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, 300 Pasteur Drive H2200, Stanford 94305, USA.
PET Clin ; 19(3): 363-369, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38705743
ABSTRACT
Prostate-specific membrane antigen targeting positron emission tomography (PSMA-PET) is routinely used for the staging and restaging of patients with various stages of prostate cancer. For clear communication with referring physicians and to improve inter-reader agreement, the use of standardized reporting templates is mandatory. Increasingly, tumor volume is used by reporting and response assessment frameworks to prognosticate patient outcome or measure response to therapy. However, the quantification of tumor volume is often too time-consuming in routine clinical practice. Machine learning-based tools can facilitate the quantification of tumor volume for improved outcome prognostication.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Aprendizaje Automático Límite: Humans / Male Idioma: En Revista: PET Clin Año: 2024 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Aprendizaje Automático Límite: Humans / Male Idioma: En Revista: PET Clin Año: 2024 Tipo del documento: Article