Editorial for "MRI Radiomics-Based Machine Learning for Predict of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions"

Affiliations

Editorial

Gabriel A Nketiah et al. J Magn Reson Imaging. 2021 Nov.

. 2021 Nov;54(5):1474-1475.

doi: 10.1002/jmri.27752. Epub 2021 May 27.

¹ Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.

No abstract available

References

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1. Fütterer JJ, Briganti A, De Visschere P, et al. Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? A systematic review of the literature. Eur Urol 2015;68:1045-1053.
1. Muller BG, Shih JH, Sankineni S, et al. Prostate cancer: Interobserver agreement and accuracy with the revised prostate imaging reporting and data system at multiparametric MR imaging. Radiology 2015;277:741-750.
1. Ullrich T, Quentin M, Arsov C, et al. Risk stratification of equivocal lesions on multiparametric magnetic resonance imaging of the prostate. J Urol 2018;199:691-698.
1. Sheridan AD, Nath SK, Syed JS, et al. Risk of clinically significant prostate cancer associated with prostate imaging reporting and data system category 3 (equivocal) lesions identified on multiparametric prostate MRI. Am J Roentgenol 2018;210:347-357.