Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization

PURPOSE: To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T(2)-weighted imaging (T(2)w), diffusion weighted imaging (DWI) acquired using high b values, and T(2)-mapping (T(2)). METHODS: T(2)w, DWI (12 b val...

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Detalles Bibliográficos
Autores principales: Toivonen, Jussi, Montoya Perez, Ileana, Movahedi, Parisa, Merisaari, Harri, Pesola, Marko, Taimen, Pekka, Boström, Peter J., Pohjankukka, Jonne, Kiviniemi, Aida, Pahikkala, Tapio, Aronen, Hannu J., Jambor, Ivan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613688/
https://www.ncbi.nlm.nih.gov/pubmed/31283771
http://dx.doi.org/10.1371/journal.pone.0217702

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613688/
https://www.ncbi.nlm.nih.gov/pubmed/31283771
http://dx.doi.org/10.1371/journal.pone.0217702

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