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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Publicat a:Eur Radiol
Autors principals: 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
Format: Artigo
Idioma:Inglês
Publicat: Springer Berlin Heidelberg 2019
Matèries:
Imaging Informatics and Artificial Intelligence
Accés en línia: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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