London, Aug 2 Artificial Intelligence (AI)-supported mammography analysis is as good as two breast radiologists working together to detect breast cancer, without increasing false positives and almost halving the screen-reading workload, reveal results of a trial published in The Lancet Oncology journal.
Breast cancer screening with mammography has been shown to improve prognosis and reduce mortality by detecting breast cancer at an earlier, more treatable stage.
However, estimates suggest that 20-30 per cent of interval cancers (cancers detected between screenings that generally have a poorer prognosis than screen-detected cancers) that should have been spotted at the preceding screening mammogram are missed, and suspicious findings often turn out to be benign.
The first randomised controlled trial of its kind involving over 80,000 Swedish women provides robust evidence that AI can act as an automated second reader for mammograms that might help reduce this workload and improve screening accuracy.
However, the ultimate use of AI in mammography may not be expected for several years, said researchers.
“These promising interim safety results should be used to inform new trials and programme-based evaluations to address the pronounced radiologist shortage in many countries. But they are not enough on their own to confirm that AI is ready to be implemented in mammography screening,” said lead author Dr Kristina Lang from Lund University, Sweden.
“We still need to understand the implications on patients’ outcomes, especially whether combining radiologists’ expertise with AI can help detect interval cancers that are often missed by traditional screening, as well as the cost-effectiveness of the technology,” Dr Lang said.
Between April 2021 and July 2022, 80,033 women aged 40-80 years who had undergone mammogram screening in Sweden were randomly assigned to either AI-supported analysis, where a commercially available AI-supported mammogram reading system analysed the mammograms before they were also read by one or two radiologists (intervention arm), or standard analysis performed by two radiologists without AI (control arm).
Overall, AI-supported screening resulted in a cancer detection rate of six per 1,000 screened women compared to five per 1,000 for standard double reading without AI -- equivalent to detecting one additional cancer for every 1,000 women screened.
Importantly, there were 36,886 fewer screen readings by radiologists in the AI-supported group than in the control group (46,345 vs 83,231), resulting in a 44 per cent reduction in the screen-reading workload of radiologists.
“The greatest potential of AI right now is that it could allow radiologists to be less burdened by the excessive amount of reading,” said Lang.
“While our AI-supported screening system requires at least one radiologist in charge of detection, it could potentially do away with the need for double reading of the majority of mammograms easing the pressure on workloads and enabling radiologists to focus on more advanced diagnostics while shortening waiting times for patients.”
The researchers also noted several limitations including that the analysis was conducted at a single centre and was limited to one type of mammography device and one AI system which might limit the generalisability of the results.
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