Lunit
Back to List

Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study

Mattie Salim, MD et al. — Lancet Digital Health (2020)

Background



We examined the potential change in cancer detection when using an artificial intelligence (AI) cancer-detection software to triage certain screening examinations into a no radiologist work stream, and then after regular radiologist assessment of the remainder, triage certain screening examinations into an enhanced assessment work stream. The purpose of enhanced assessment was to simulate selection of women for more sensitive screening promoting early detection of cancers that would otherwise be diagnosed as interval cancers or as next-round screen-detected cancers. The aim of the study was to examine how AI could reduce radiologist workload and increase cancer detection.








Methods



In this retrospective simulation study, all women diagnosed with breast cancer who attended two consecutive screening rounds were included. Healthy women were randomly sampled from the same cohort; their observations were given elevated weight to mimic a frequency of 0·7% incident cancer per screening interval. Based on the prediction score from a commercially available AI cancer detector, various cutoff points for the decision to channel women to the two new work streams were examined in terms of missed and additionally detected cancer.








Findings



7364 women were included in the study sample: 547 were diagnosed with breast cancer and 6817 were healthy controls. When including 60%, 70%, or 80% of women with the lowest AI scores in the no radiologist stream, the proportion of screen-detected cancers that would have been missed were 0, 0·3% (95% CI 0·0–4·3), or 2·6% (1·1–5·4), respectively. When including 1% or 5% of women with the highest AI scores in the enhanced assessment stream, the potential additional cancer detection was 24 (12%) or 53 (27%) of 200 subsequent interval cancers, respectively, and 48 (14%) or 121 (35%) of 347 next-round screen-detected cancers, respectively.








Interpretation



Using a commercial AI cancer detector to triage mammograms into no radiologist assessment and enhanced assessment could potentially reduce radiologist workload by more than half, and pre-emptively detect a substantial proportion of cancers otherwise diagnosed later.


전체 내용 보기
AUTHORS

Mattie Salim, MD1,2; Erik Wåhlin, MSc3; Karin Dembrower, MD4,5; Edward Azavedo, MD, PhD1,6; Theodoros Foukakis, MD, PhD1,2; Yue Liu, MSc7; Kevin Smith, MSc, PhD8; Martin Eklund, MSc, PhD9; Fredrik Strand, MD, PhD1,10

1Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden, 2Department of Radiology, Karolinska University Hospital, Stockholm, Sweden, 3Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden, 4Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden, 5Department of Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden, 6Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden, 7Division of Computational Science and Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden, 8KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden, 9Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden, 10Breast Radiology, Karolinska University Hospital, Stockholm, Sweden

PUBLISHED
Lancet Digital Health (2020)

Read more