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

Eva Gresser | Zendy; Balthasar Schachtner | Zendy; Anna Theresa Stüber | Zendy; Olga Solyanik | Zendy; Andrea Schreier | Zendy; Thomas S. Huber | Zendy; Matthias F. Froelich | Zendy; Giuseppe Magistro | Zendy; Alexander Kretschmer | Zendy; Christian G. Stief | Zendy; Jens Ricke | Zendy; Michael Ingrisch | Zendy; Dominik Nörenberg | Zendy

Open Access

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

Author(s) -

Eva Gresser,

Balthasar Schachtner,

Anna Theresa Stüber,

Olga Solyanik,

Andrea Schreier,

Thomas S. Huber,

Matthias F. Froelich,

Giuseppe Magistro,

Alexander Kretschmer,

Christian G. Stief,

Jens Ricke,

Michael Ingrisch,

Dominik Nörenberg

Publication year - 2022

Publication title -

quantitative imaging in medicine and surgery

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.766

H-Index - 21

eISSN - 2223-4292

pISSN - 2223-4306

DOI - 10.21037/qims-22-265

Subject(s) - radiomics , prostate cancer , logistic regression , medicine , artificial intelligence , robustness (evolution) , discriminative model , random forest , computer science , prostate , support vector machine , machine learning , pattern recognition (psychology) , radiology , cancer , biochemistry , chemistry , gene

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