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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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2019
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Materias: | |
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 |