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Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores
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@article{{QIMS}{39198}, author = {Winkel, D., Breit, H., Shi, B., Boll, D., Seifert, H., Wetterauer, C.}, title = {Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores}, journal = {Quantitative Imaging in Medicine and Surgery}, volume = {10}, number = {4}, year = {2020}, url = {https://qims.amegroups.org/article/view/39198} }