Recent Lancet Regional Health The study evaluates the performance of an artificial intelligence (AI)-based risk model for breast cancer screening in Europe.
Study: European validation of an artificial intelligence-based short-term risk model for personalized breast cancer screening – a nested case-control study. Image credit: Gagliardiphotography / Shutterstock.com
Record
Regular mammography screening has reduced breast cancer deaths in women. Even after two years of breast cancer screening, about 25% of breast cancers are diagnosed. In these cases, some women may have tested negative on a mammogram but could have been diagnosed with breast cancer before attending their next appointment.
Between 25-40% of women are diagnosed with stage two or higher breast cancer. Therefore, it is important to determine whether the tumor was detected during routine mammographic screening, as it is a strong predictor of breast cancer-related mortality.
Previous studies have suggested adding other risk assessment measures to improve the screening process and ultimately prevent interval cancer risk before the next screening. This strategy could also reduce the incidence of late-stage breast cancer at the next screening. In the United States, women who have dense breasts or are at high risk due to family risk factors are given additional tests.
Current breast cancer screening programs conducted in Europe do not have guidelines indicating additional screening for women at higher risk of breast cancer. However, several clinical risk assessment tools based on family history and lifestyle factors have been developed to improve screening outcomes.
Although a new image-based risk model has shown significant potential in identifying women at higher risk of breast cancer, this model requires additional external validation to assess its clinical feasibility.
About the study
The current study evaluated a previously developed artificial intelligence-based breast cancer risk model designed to identify breast cancer risk in the short term. More specifically, this model has been used to identify women who developed cancer in the interval between two mammogram screenings in two years after a negative screening.
The overall risk classification and discriminative performance of the ProFound AI Risk model was evaluated. This AI-based model was previously developed using a Swedish control cohort.
The current study used four screening populations that included women between 45 and 69 years of age who underwent mammography screening. From this screening population, two cohorts were designed in Germany and one each in Italy and Spain.
Some of the key eligibility criteria included breast cancer incidence on digital mammography at baseline. These women were diagnosed before or at the next screening program.
The study excluded women with a family history of breast cancer. A nested case-control study was performed for each population. Control groups for each screening population were randomly drawn from the underlying screening cohort.
Study findings
The validation study included a total of 739 breast cancer patients and 7,812 controls. Cancer outcome was assessed at the second examination, in which women were randomly assigned to undergo digital mammography or digital breast tomosynthesis (DBT). The AI-based risk model used these mammograms to predict which women were at risk of breast cancer in two years.
Compared to the initial evaluation of the AI-based risk model for breast cancer screening using a Swedish cohort, a small variability of discriminatory performance was observed between populations of different European countries. However, the model showed similar discrimination to that of the previous report. Women with dense and non-dense breasts showed similar risk stratification performance.
Advanced breast cancer was more likely to be diagnosed in women at high risk compared to those at moderate risk of developing breast cancer. The current study showed that an image-based AI risk model could be affected by ethnic differences and screening frequencies.
Women with less dense breasts were found to be at greater risk of developing more aggressive mediastinal cancers. Conversely, women with dense breasts could have their tumor covered by dense tissue, which increases the chance of interstitial cancer and later-stage breast cancer.
Radiologists face significant challenges associated with dense tissue coverage of tumors. Therefore, high-risk women with dense breasts could benefit positively from more sensitive testing after a negative screening. However, a shorter screening interval is preferable for high-risk women with nondense breasts because of the increased risk of a rapidly growing tumor.
conclusions
The current study provided insight into the importance of performing additional tests beyond mammographic density to identify women at higher risk of breast cancer, which would positively improve screening outcomes. A combination of density and risk estimation approaches could be more effective in population-based breast cancer screening programs.
