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Can texture features computed from the joint intensity distribution of different MRI sequences accurately predict prostate cancer grade?

Stavrinides, V; Carmona Echeverria, L; Whitaker, H; (2018) Can texture features computed from the joint intensity distribution of different MRI sequences accurately predict prostate cancer grade? [Editorial comment]. Journal of Medical Artificial Intelligence , 1 , Article 12. 10.21037/jmai.2018.11.01. Green open access

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Type: Article
Title: Can texture features computed from the joint intensity distribution of different MRI sequences accurately predict prostate cancer grade?
Open access status: An open access version is available from UCL Discovery
DOI: 10.21037/jmai.2018.11.01
Publisher version: http://doi.org/10.21037/jmai.2018.11.01
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10070997
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