Abstract
ObjectivesTo investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening.Materials and methodsPreceding screening mammograms of 429 consecutive women diagnosed with interval cancer in Southern Sweden between 2013 and 2017 were analysed with a deep learning–based AI system. The system assigns a risk score from 1 to 10. Two experienced breast radiologists reviewed and classified the cases in consensus as true negative, minimal signs or false negative and assessed whether the AI system correctly localised the cancer. The potential reduction of interval cancer was calculated at different risk score thresholds corresponding to approximately 10%, 4% and 1% recall rates.ResultsA statistically significant correlation between interval cancer classification groups and AI risk score was observed (p < .0001). AI scored one in three (143/429) interval cancer with risk score 10, of which 67% (96/143) were either classified as minimal signs or false negative. Of these, 58% (83/143) were correctly located by AI, and could therefore potentially be detected at screening with the aid of AI, resulting in a 19.3% (95% CI 15.9–23.4) reduction of interval cancer. At 4% and 1% recall thresholds, the reduction of interval cancer was 11.2% (95% CI 8.5–14.5) and 4.7% (95% CI 3.0–7.1). The corresponding reduction of interval cancer with grave outcome (women who died or with stage IV disease) at risk score 10 was 23% (8/35; 95% CI 12–39).ConclusionThe use of AI in screen reading has the potential to reduce the rate of interval cancer without supplementary screening modalities.Key Points• Retrospective study showed that AI detected 19% of interval cancer at the preceding screening exam that in addition showed at least minimal signs of malignancy. Importantly, these were correctly localised by AI, thus obviating supplementary screening modalities.• AI could potentially reduce a proportion of particularly aggressive interval cancers.• There was a correlation between AI risk score and interval cancer classified as true negative, minimal signs or false negative.
Highlights
Despite population-based mammography screening and improved and effective treatments, breast cancer is still a majorEur Radiol (2021) 31:5940–5947 cause of cancer-related death in women
Two breast radiologists with 7 and 47 years of experience reviewed the preceding mammograms of all interval cancers in consensus and classified them according to interval cancer type: true negative, minimal signs or false negative
The review included an assessment of women who had died or had metastatic breast cancer as a result of their interval cancer, based on the clinical history ascertained in the Radiology Information System
Summary
Despite population-based mammography screening and improved and effective treatments, breast cancer is still a majorEur Radiol (2021) 31:5940–5947 cause of cancer-related death in women. Contributing factors are low sensitivity of mammography in dense breasts, certain cancer growth patterns resulting in subtle mammographic presentation or with a fast growth rate that outpaces screening intervals, as well as radiologists’ reading errors (perceptual or interpretive) [2, 3]. Interval cancers can be classified as either true negative, showing minimal signs or false negative. On the other hand, could have been recalled in screening but were either missed or misinterpreted by the readers. Depending on the review method, including availability to diagnostic mammograms, it has been shown that up to 30% of all interval cancers are classified as false negatives [2, 6,7,8,9], which presents an opportunity for improvement
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