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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Veröffentlicht in:Eur Radiol
Hauptverfasser: 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
Sprache:Inglês
Veröffentlicht: Springer Berlin Heidelberg 2019
Schlagworte:
Imaging Informatics and Artificial Intelligence
Online Zugang: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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