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.
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.
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.
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.
Karin Dembrower, Erik Wåhlin, Yue Liu, Mattie Salim, Kevin Smith, Peter Lindholm, Martin Eklund, Fredrik Strand
Department of Physiology and Pharmacology (K Dembrower MD, P Lindholm PhD), Department of Pathology and Oncology (M Salim MD, F Strand PhD), and Department of Medical Epidemiology and Biostatistics (M Eklund PhD), Karolinska Institute, Stockholm, Sweden; Department of Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden (K Dembrower); Department of Medical Radiation Physics and Nuclear Medicine (E Wåhlin MSc), Department of Radiology (M Salim), and Breast Radiology (F Strand), Karolinska University Hospital, Stockholm, Sweden; and Department of Computational Science and Technology, KTH Royal Institute of Technology and Science for Life Laboratory, Stockholm, Sweden (Y Liu MSc, K Smith PhD)
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