AI as an independent second reader in detection of clinically relevant breast cancers within a population-based screening programme in the Netherlands: a retrospective cohort study.

Suzanne L van Winkel¹, Jim Peters², Natasja Janssen³, Jaap Kroes³, Elizabeth A Loehrer⁴, Jessie Gommers⁵, Ioannis Sechopoulos⁶, Linda de Munck⁷, Jonas Teuwen⁸, Mireille Broeders⁹, Nico Karssemeijer¹⁰, Ritse M Mann¹¹

¹Medical Imaging Department, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands. Electronic address: suzanne.vanwinkel@radboudumc.nl.

²Department for Health Evidence, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands.

³ScreenPoint Medical BV, Nijmegen, Netherlands.

⁴Medical Imaging Department, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands; Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, Netherlands.

⁵Medical Imaging Department, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands.

⁶Medical Imaging Department, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands; ScreenPoint Medical BV, Nijmegen, Netherlands; Technical Medicine Centre, University of Twente, Enschede, Netherlands.

⁷Netherlands Comprehensive Cancer Organisation, Utrecht, Netherlands.

⁸Department of Radiology, Antoni van Leeuwenhoek Netherlands Cancer Institute, Amsterdam, Netherlands.

⁹Department for Health Evidence, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands; LRCB, Dutch Expert Centre for Screening, Nijmegen, Netherlands.

¹⁰Medical Imaging Department, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands; ScreenPoint Medical BV, Nijmegen, Netherlands.

¹¹Medical Imaging Department, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands; Department of Radiology, Antoni van Leeuwenhoek Netherlands Cancer Institute, Amsterdam, Netherlands.

PubMed DOI

Abstract

BACKGROUND: Breast cancer screening programmes have shown to reduce mortality, but current methods face challenges such as limited mammographic sensitivity, limited resources, and variability in radiologist expertise. Artificial intelligence (AI) offers potential to improve screening accuracy and efficiency. This study simulated different screening scenarios, evaluating the performance of populati…

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