Fully automatic prognostic biomarker extraction from metastatic prostate lesion segmentations in whole-body [(68)Ga]Ga-PSMA-11 PET/CT images

PURPOSE: This study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [(68)Ga]Ga-PSMA-11 PET/CT images for the purpose of extracting patient-level prognostic biomarkers. METHODS: Three hundred thirty-sev...

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Detalles Bibliográficos
Autores principales: Kendrick, Jake, Francis, Roslyn J., Hassan, Ghulam Mubashar, Rowshanfarzad, Pejman, Ong, Jeremy S. L., Ebert, Martin A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668788/
https://www.ncbi.nlm.nih.gov/pubmed/35976392
http://dx.doi.org/10.1007/s00259-022-05927-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668788/
https://www.ncbi.nlm.nih.gov/pubmed/35976392
http://dx.doi.org/10.1007/s00259-022-05927-1

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