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Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets

Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets

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Main Authors: Gresser, Eva Kristina (Author) , Schachtner, Balthasar (Author) , Stüber, Anna Theresa (Author) , Solyanik, Olga (Author) , Schreier, Andrea (Author) , Huber, Thomas (Author) , Froelich, Matthias F. (Author) , Magistro, Giuseppe (Author) , Kretschmer, Alexander (Author) , Stief, Christian (Author) , Ricke, Jens (Author) , Ingrisch, Michael (Author) , Nörenberg, Dominik (Author)
Format: Article (Journal)
Language:English
Published: November 01, 2022
In: Quantitative imaging in medicine and surgery
Year: 2022, Volume: 12, Issue: 11, Pages: 4991-5003
ISSN:2223-4306
DOI:10.21037/qims-22-265
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.21037/qims-22-265
Verlag, lizenzpflichtig, Volltext: https://qims.amegroups.org/article/view/101324
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Author Notes:Eva Gresser, Balthasar Schachtner, Anna Theresa Stüber, Olga Solyanik, Andrea Schreier, Thomas Huber, Matthias Frank Froelich, Giuseppe Magistro, Alexander Kretschmer, Christian Stief, Jens Ricke, Michael Ingrisch, Dominik Nörenberg
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Summary:Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets
Item Description:Gesehen am 24.06.2024
Physical Description:Online Resource
ISSN:2223-4306
DOI:10.21037/qims-22-265

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