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Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE Trial

Bianca M. den Dekker, Marije F. Bakker, Stéphanie V. de Lange, Wouter B. Veldhuis, P. J. van Diest, Katya M. Duvivier, Marc B. I. Lobbes, Claudette E. Loo, Ritse M. Mann, Evelyn M. Monninkhof, Jeroen Veltman, Ruud M. Pijnappel, Carla H. van Gils, For the DENSE Trial Study Group, C. H. van Gils, M. F. Bakker, S. V. de Lange, S. G. A. Veenhuizen, W. B. Veldhuis, R. M. Pijnappel, M. J. Emaus, P. H. M. Peeters, E. M. Monninkhof, M. A. Fernandez-Gallardo, W. P. T. M. Mali, M. A. A. J. van den Bosch, P. J. van Diest, R. M. Mann, R. Mus, M. W. Imhof-Tas, N. Karssemeijer, C. E. Loo, P. K. de Koekkoek-Doll, H. A. O. Winter-Warnars, R. H. C. Bisschops, M. C. J. M. Kock, R. K. Storm, P. H. M. van der Valk, M. B. I. Lobbes, S. Gommers, M. D. F. de Jong, M. J. C. M. Rutten, K. M. Duvivier, P. de Graaf, J. Veltman, R. L. J. H. Bourez, H. J. de Koning

2021Radiology23 citationsDOI

Abstract

See also the editorial by Imbriaco in this issue.

Topics & Concepts

MedicineRadiologyFalse positive rateArtificial intelligenceComputer scienceMRI in cancer diagnosisRadiomics and Machine Learning in Medical ImagingDigital Radiography and Breast Imaging
Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE Trial | Litcius