Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features

OBJECTIVES: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). METHODS: Ret...

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Detalles Bibliográficos
Autores principales: Bernatz, Simon, Ackermann, Jörg, Mandel, Philipp, Kaltenbach, Benjamin, Zhdanovich, Yauheniya, Harter, Patrick N., Döring, Claudia, Hammerstingl, Renate, Bodelle, Boris, Smith, Kevin, Bucher, Andreas, Albrecht, Moritz, Rosbach, Nicolas, Basten, Lajos, Yel, Ibrahim, Wenzel, Mike, Bankov, Katrin, Koch, Ina, Chun, Felix K.-H., Köllermann, Jens, Wild, Peter J., Vogl, Thomas J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Urogenital
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599168/
https://www.ncbi.nlm.nih.gov/pubmed/32676784
http://dx.doi.org/10.1007/s00330-020-07064-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599168/
https://www.ncbi.nlm.nih.gov/pubmed/32676784
http://dx.doi.org/10.1007/s00330-020-07064-5

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