Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists

OBJECTIVE: The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the...

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Bibliografische gegevens
Gepubliceerd in:Eur Radiol
Hoofdauteurs: Antonelli, Michela, Johnston, Edward W., Dikaios, Nikolaos, Cheung, King K., Sidhu, Harbir S., Appayya, Mrishta B., Giganti, Francesco, Simmons, Lucy A. M., Freeman, Alex, Allen, Clare, Ahmed, Hashim U., Atkinson, David, Ourselin, Sebastien, Punwani, Shonit
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Springer Berlin Heidelberg 2019
Onderwerpen:
Imaging Informatics and Artificial Intelligence
Online toegang:https://ncbi.nlm.nih.gov/pmc/articles/PMC6682575/
https://ncbi.nlm.nih.gov/pubmed/31187216
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s00330-019-06244-2
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