AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.

Journal: Nature medicine
Published Date:

Abstract

Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the number of missed cancers. However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited. An effective method for triaging individuals to supplemental MRI screening is therefore needed. We conducted a randomized clinical trial, ScreenTrustMRI, using a recently developed artificial intelligence (AI) tool to score each mammogram. We offered trial participation to individuals with a negative screening mammogram and a high AI score (top 6.9%). Upon agreeing to participate, individuals were assigned randomly to one of two groups: those receiving supplemental MRI and those not receiving MRI. The primary endpoint of ScreenTrustMRI is advanced breast cancer defined as either interval cancer, invasive component larger than 15 mm or lymph node positive cancer, based on a 27-month follow-up time from the initial screening. Secondary endpoints, prespecified in the study protocol to be reported before the primary outcome, include cancer detected by supplemental MRI, which is the focus of the current paper. Compared with traditional breast density measures used in a previous clinical trial, the current AI method was nearly four times more efficient in terms of cancers detected per 1,000 MRI examinations (64 versus 16.5). Most additional cancers detected were invasive and several were multifocal, suggesting that their detection was timely. Altogether, our results show that using an AI-based score to select a small proportion (6.9%) of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography. ClinicalTrials.gov registration: NCT04832594 .

Authors

  • Mattie Salim
    From the Departments of Pathology and Oncology (M.S., F.S.), Physiology and Pharmacology (K.D., P.L.), and Medical Epidemiology and Biostatistics (M.E.), Karolinska Institute, Stockholm, Sweden; Department of Radiology (M.S.) and Breast Radiology (F.S.), Karolinska University Hospital, Dalagatan 90, 113 43 Stockholm, Sweden; and the Department of Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden (K.D.).
  • Yue Liu
    School of Athletic Performance, Shanghai University of Sport, Shanghai, China.
  • Moein Sorkhei
    School of Computer Science and Technology, Royal Institute of Technology (KTH), Stockholm, Sweden.
  • Dimitra Ntoula
    Breast Radiology Unit, Karolinska University Hospital, Stockholm, Sweden.
  • Theodoros Foukakis
    Department of Oncology/Pathology, Karolinska Institutet, Stockholm, Sweden; Theme Cancer, Karolinska University Hospital, Stockholm, Sweden.
  • Irma Fredriksson
    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
  • Yanlu Wang
    Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
  • Martin Eklund
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: martin.eklund@ki.se.
  • Hossein Azizpour
    School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden; Science for Life Laboratory, Stockholm, Sweden.
  • Kevin Smith
  • Fredrik Strand
    Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden.