4 results match your criteria: "LRCB Dutch Expert Centre for Screening[Affiliation]"

The integration of artificial intelligence (AI) in digital mammography (DM) screening holds promise for early breast cancer detection, potentially enhancing accuracy and efficiency. However, AI performance is not identical to that of human observers. We aimed to identify common morphological image characteristics of true cancers that are missed by either AI or human screening when their interpretations are discrepant.

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We propose a nonparametric figure of merit, the contrast equivalent distance CED, to measure contrast directly from clinical images.A relative brightness distanceis calculated by making use of the order statistic of the pixel values. By multiplyingwith the grey value range, the mean brightness distance MBD is obtained.

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Currently, quality assurance measurements in mammography are performed on unprocessed images. For diagnosis, however, radiologists are provided with processed images. This image processing is optimised for images of human anatomy and therefore does not always perform satisfactorily with technical phantoms.

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Reasons for (non)participation in supplemental population-based MRI breast screening for women with extremely dense breasts.

Clin Radiol

August 2018

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, P.O. Box 85500, 3508 GA Utrecht, The Netherlands. Electronic address:

Aim: To determine the willingness of women with extremely dense breasts to undergo breast cancer screening with magnetic resonance imaging (MRI) in a research setting, and to examine reasons for women to participate or not.

Materials And Methods: Between 2011 and 2015, 8,061 women (50-75 years) were invited for supplemental MRI as part of the Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial (ClinicalTrials.gov Identifier: NCT01315015), after a negative screening mammography in the national population-based mammography screening programme.

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