Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores | Zendy

David Winkel | Zendy; HannsChristian Breit | Zendy; Bibo Shi | Zendy; Daniel T. Boll | Zendy; HansHelge Seifert | Zendy; Christian Wetterauer | Zendy

Open Access

Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores

Author(s) -

David Winkel,

HannsChristian Breit,

Bibo Shi,

Daniel T. Boll,

HansHelge Seifert,

Christian Wetterauer

Publication year - 2020

Publication title -

quantitative imaging in medicine and surgery

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.766

H-Index - 21

eISSN - 2223-4292

pISSN - 2223-4306

DOI - 10.21037/qims.2020.03.08

Subject(s) - prostate cancer , ground truth , support vector machine , artificial intelligence , random forest , medicine , computer science , magnetic resonance imaging , receiver operating characteristic , pattern recognition (psychology) , nuclear medicine , cancer , machine learning , radiology

To investigate if supervised machine learning (ML) classifiers would be able to predict clinically significant cancer (sPC) from a set of quantitative image-features and to compare these results with established PI-RADS v2 assessment scores.

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