AI-assisted biparametric MRI surveillance of prostate cancer: feasibility study

OBJECTIVES: To evaluate the feasibility of automatic longitudinal analysis of consecutive biparametric MRI (bpMRI) scans to detect clinically significant (cs) prostate cancer (PCa). METHODS: This retrospective study included a multi-center dataset of 1513 patients who underwent bpMRI (T2 + DWI) betw...

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Detalles Bibliográficos
Autores principales: Roest, C., Kwee, T.C., Saha, A., Fütterer, J.J., Yakar, D., Huisman, H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Imaging Informatics and Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755080/
https://www.ncbi.nlm.nih.gov/pubmed/35960339
http://dx.doi.org/10.1007/s00330-022-09032-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755080/
https://www.ncbi.nlm.nih.gov/pubmed/35960339
http://dx.doi.org/10.1007/s00330-022-09032-7

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