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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Detalhes bibliográficos
Publicado no:Eur Radiol
Main Authors: 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
Formato: Artigo
Idioma:Inglês
Publicado em: Springer Berlin Heidelberg 2019
Assuntos:
Imaging Informatics and Artificial Intelligence
Acesso em linha: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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