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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Detalles Bibliográficos
Autores principales: 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: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Imaging Informatics and Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6682575/
https://www.ncbi.nlm.nih.gov/pubmed/31187216
http://dx.doi.org/10.1007/s00330-019-06244-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6682575/
https://www.ncbi.nlm.nih.gov/pubmed/31187216
http://dx.doi.org/10.1007/s00330-019-06244-2

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