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

BACKGROUND: Radiomics promises to enhance the discriminative performance for clinically significant prostate cancer (csPCa), but still lacks validation in real-life scenarios. This study investigates the classification performance and robustness of machine learning radiomics models in heterogeneous...

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Detalles Bibliográficos
Autores principales: Gresser, Eva, Schachtner, Balthasar, Stüber, Anna Theresa, Solyanik, Olga, Schreier, Andrea, Huber, Thomas, Froelich, Matthias Frank, Magistro, Giuseppe, Kretschmer, Alexander, Stief, Christian, Ricke, Jens, Ingrisch, Michael, Nörenberg, Dominik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9622454/
https://www.ncbi.nlm.nih.gov/pubmed/36330197
http://dx.doi.org/10.21037/qims-22-265

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9622454/
https://www.ncbi.nlm.nih.gov/pubmed/36330197
http://dx.doi.org/10.21037/qims-22-265

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