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DWI was performed using 12 b\u2010values (0\u20132000 s\/mm<jats:sup>2<\/jats:sup>), post\u2010processed using kurtosis function, and PCa areas were annotated using whole mount prostatectomy sections. A total of 1694 radiomic features including Sobel, Kirch, Gradient, Zernike Moments, Gabor, Haralick, CoLIAGe, Haar wavelet coefficients, 3D analogue to Laws features, 2D contours, and corner detectors were calculated. Radiomics and 4 feature pruning methods (area under the receiver operator characteristic curve, maximum relevance minimum redundancy, Spearman\u2019s \u03c1, Wilcoxon rank\u2010sum) were evaluated in terms of Scan1\u2010Scan2 repeatability using intraclass correlation coefficient (ICC)(3,1). Classification performance for clinically significant and insignificant PCa with Gleason grade groups 1 versus &gt;1 was evaluated by area under the receiver operator characteristic curve in unseen random 30% data split.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The ICC(3,1) values for conventional radiomics and feature pruning methods were in the range of 0.28\u20130.90. The machine learning classifications varied between Scan1 and Scan2 with % of same class labels between Scan1 and Scan2 in the range of 61\u201381%. Surface\u2010to\u2010volume ratio and corner detector\u2010based features were among the most represented features with high repeatability, ICC(3,1) &gt;0.75, consistently high ranking using all 4 feature pruning methods, and classification performance with area under the receiver operator characteristic curve &gt;0.70.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Surface\u2010to\u2010volume ratio and corner detectors for prostate DWI led to good classification of unseen data and performed similarly in Scan1 and Scan2 in contrast to multiple conventional radiomic features.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.28058","type":"journal-article","created":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T20:13:20Z","timestamp":1573244000000},"page":"2293-2309","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Repeatability of radiomics and machine learning for DWI: Short\u2010term 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