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

   
@article{QIMS101324,
	author = {Eva Gresser and Balthasar Schachtner and Anna Theresa Stüber and Olga Solyanik and Andrea Schreier and Thomas Huber and Matthias Frank Froelich and Giuseppe Magistro and Alexander Kretschmer and Christian Stief and Jens Ricke and Michael Ingrisch and Dominik Nörenberg},
	title = {Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets},
	journal = {Quantitative Imaging in Medicine and Surgery},
	volume = {12},
	number = {11},
	year = {2022},
	keywords = {},
	abstract = {},
	issn = {2223-4306},	url = {https://qims.amegroups.org/article/view/101324}
}