AI-supported mammography found to be safe during trials: Study
The study underlines AI-supported mammography analysis is as good as two breast radiologists working together to detect breast cancer
Mammography screening supported by artificial intelligence is safe, more accurate and efficient, according to an interim safety analysis of the first randomised trial that was published in the Lancet Oncology journal on Tuesday, which could potentially reduce the workload of radiologists.
According to the interim safety analysis involving at least 80,000 Swedish women, AI-supported screening detected 20% more cancers compared with the routine double reading of mammograms by two breast radiologists.
In effect, the study underlines 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.
“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,” lead author Dr Kristina Lång of Sweden’s Lund University said in a statement. “But they are not enough on their own to confirm that AI is ready to be implemented in mammography screening.”
“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,” she said.
Breast cancer screening through mammography has been shown to improve prognosis and reduce mortality by detecting breast cancer at an earlier, more treatable stage.
In the study, however, researchers said the final trial results looking at whether the use of AI in interpreting mammography images translates into a reduction in interval cancers (cancers detected between screenings that generally have a poorer prognosis than screen-detected cancers) in 100,000 women followed over two years—and ultimately whether AI’s use in mammography screening is justified—are not expected for several years.
European guidelines recommend double reading of screening mammograms by two radiologists to ensure high sensitivity.
“But there is a shortage of breast radiologists in many countries, including a shortfall of around 41 (8%) in the UK in 2020 and about 50 in Sweden, and it takes over a decade to train a radiologist capable of interpreting mammograms,” said the paper in the peer-reviewed journal. “AI has been proposed as an automated second reader for mammograms that might help reduce this workload and improve screening accuracy.”
“The technology has shown encouraging results in retrospective studies using AI to triage examinations to either single or double reading and by providing radiologists with computer-aided detection (CAD) marks highlighting suspicious features to reduce false negative results,” it said.
Between April 2021 and July 2022, 80,033 women aged 40-80 years who had undergone mammogram screening at four sites in south-west Sweden were randomly assigned in a 1:1 ratio 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).
The AI system first analysed the mammography image and predicted the risk of cancer on a scale of one to 10, with one representing the lowest risk and 10 the highest. If the risk score was less than 10, the image was further analysed by one radiologist, whereas if the AI system predicted a risk score of 10, then two radiologists analysed the image.
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, which was 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% reduction in the screen reading workload of radiologists, said the researchers.
“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.”
