In a recent study published in the journal Radiologyresearchers in Denmark and the Netherlands conducted a retrospective analysis of screening performance and overall mammography-related workload before and after implementation of artificial intelligence (AI) screening systems.
Study: Early indicators of the impact of using artificial intelligence in mammography screening for breast cancer. Image credit: Radiographic Imaging / Shutterstock
Record
Regular screening for breast cancer based on mammography has been found to significantly reduce breast cancer mortality rates. However, population-based mammography screening leads to a significant increase in workload for radiologists who are tasked with reading numerous mammograms, most of which do not indicate suspicious lesions.
In addition, the double-checking process to reduce the false-positive rate and improve detection rates further increases the workload for radiologists. The lack of skilled radiologists to read mammograms is exacerbating an already heavy workload.
Recent studies have extensively examined the use of artificial intelligence in the effective screening of radiology reports while maintaining high standards of screening performance. A combined approach where artificial intelligence tools are used to help radiologists narrow down mammograms with signs of damage is also believed to reduce the workload for radiologists while maintaining the sensitivity of screening.
About the study
The present study used preliminary performance indicators from two cohorts of women undergoing mammographic screening as part of the Danish population-based breast cancer screening program to compare change in workload and screening performance after implementation of screening tools with based on artificial intelligence.
This screening program invited women between the ages of 50 and 69 to undergo breast cancer screening every two years until age 79. Those with markers that showed an increased risk of breast cancer, such as BRCA genes, were examined using different protocols.
Here, the researchers used two groups of women: one who underwent screening before the AI-based screening system was implemented and one who underwent AI-based mammography screening. Only women under the age of 70 were included in the analysis to ensure that those in a high-risk subpopulation were not part of the analysis.
All participants underwent standardized imaging protocols with full-field craniofacial and mediolateral oblique digital mammograms recorded. All positive cases included in this study were ductal carcinoma detected by examination or invasive cancer, which were confirmed on the spot using needle biopsies. Data on pathology reports, lesion size, node positivity and diagnoses were also obtained from the country’s health registry.
The AI system implemented to check the mammograms was trained using deep learning models to detect, highlight and evaluate any suspicious calcifications or lesions seen on the mammogram. The AI tool then stratified the screenings into a score range of 1 to 10, indicating the likelihood of breast cancer.
A team of radiologists, consisting mainly of senior radiologists experienced in reading breast imaging results, read the mammograms for both cohorts. Before the implementation of the AI screening system, each test was read by two radiologists, and the patient was recommended for clinical examination and needle biopsy only if both radiologists indicated that the test warranted recall.
After implementation of the AI review system, mammograms that scored less than or equal to 5 were read by a senior radiologist who was aware that these mammograms were given only one reading. Those warranting withdrawal were then discussed with a second radiologist.
Full-field left mediolateral lateral digital mammogram in a 67-year-old woman with breast imaging reference density and data system 1 submitted to control with the artificial intelligence (AI) system. (A) Image shows labeling provided by AI (square). The screening received a high AI screening score of 10, based on this area with arterial calcifications scoring 85 out of 100 by the AI system. (B) Same image as A, but with radiologist findings. Because of the high AI score, the scan was double-read by two radiologists, who determined that the arterial calcifications (circle) were not suspicious for breast cancer. The woman was not recalled for diagnostic evaluation.
Results
The study found that implementation of the AI-based screening system significantly reduced the workload for radiologists analyzing mammograms from a population-based breast cancer screening program while improving screening performance.
The cohort screened before the implementation of the AI-based screening system consisted of more than 60,000 women, while the cohort screened using the AI system had approximately 58,000 women. AI screening resulted in an increase in breast cancer diagnoses (0.70% vs. 0.82% pre-AI vs. AI, respectively) with a lower false-positive rate (2.39% vs. 1.63%).
AI-based screening had a higher positive predictive value and the rate of invasive cancers was lower when AI-based methods were used for screening. Although the node-negative cancer rate did not change, the other performance indicators showed that AI-based screening significantly improved performance. The reading workload was also found to be reduced by 33.5%.
conclusions
In summary, the study evaluated the effectiveness of an AI-based screening system in reducing radiologists’ workload and improving screening performance in reading mammograms for biennial population-based breast cancer screening in Denmark.
The findings showed that the AI-based system significantly reduced the workload for radiologists while improving screening performance, supported by a significant increase in breast cancer diagnoses and a significant decrease in false positive rates.
In Denmark, the use of #ALL INCLUDED with mammography screening improved breast cancer detection rate, reduced false positives, reduced callbacks and reduced radiologist workload https://t.co/V8pnZUMai8@radiology_rsna pic.twitter.com/IkYSMDKsmT
— Eric Topol (@EricTopol) June 4, 2024
