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

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

<h4>Purpose</h4>To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2).<h4>Methods&l...

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Bibliographic Details
Main Authors: Jussi Toivonen, Ileana Montoya Perez, Parisa Movahedi, Harri Merisaari, Marko Pesola, Pekka Taimen, Peter J Boström, Jonne Pohjankukka, Aida Kiviniemi, Tapio Pahikkala, Hannu J Aronen, Ivan Jambor
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0217702

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https://doi.org/10.1371/journal.pone.0217702

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