Åsne Sørlien Holen

An artificial intelligence model demonstrated performance similar to that of radiologist double reading for breast cancer detection with digital breast tomosynthesis, with minimal additional benefit from incorporating prior examinations.

Background and Objective: Increasing screening volumes, combined with global shortage of radiologists and a high proportion of normal mammograms, challenge the efficiency and sustainability of breast cancer screening. Artificial intelligence (AI) has the potential to improve resource allocation, workflow efficiency and diagnostic performance by supporting and partially replacing radiologists in the interpretation process. This randomized, controlled, parallel-group, non-inferiority, single-blinded trial evaluates whether an AI-supported reading strategy, involving one or two radiologists depending on AI risk stratification, is non-inferior to standard independent double reading. The primary outcome is the number of screen-detected breast cancer cases in each group. Methods: Women invited to BreastScreen Norway in the Western, Central, and Northern Norway Regional Health Authorities are eligible for inclusion. Following written informed consent, participants are randomized 1:1 to the control group (standard independent double reading by two radiologists) or the intervention group. In the intervention group, mammograms are analyzed using Transpara. Examinations with AI scores of 1–7 are interpreted by a single radiologist, whereas examinations with scores of 8–10 undergo independent double reading. Radiologists are blinded to AI scores and AI image markings during the initial interpretation; this information is disclosed during consensus meetings. Non-inferiority will be assessed by estimating confidence interval for the difference in screen-detected cancer rates between groups. Non-inferiority will be concluded if the upper bound of the confidence interval does not exceed the predefined non-inferiority margin. Conclusions: The trial addresses a critical challenge in breast cancer screening: maintaining diagnostic performance while improving efficiency in the context of workforce constraints and a high prevalence of normal examinations. By evaluating a risk-stratified AI-supported reading strategy within a population-based screening program, the study will provide important evidence on whether AI can be safely integrated to optimize workload distribution while preserving cancer detection rates. Trial registration: The ClinicalTrials.gov registry ( NCT06032390 )

Objective: To study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening program concerning cost-effectiveness and time savings for radiologists. Methods: In a decision tree model using recent data from AI vendors and the Cancer Registry of Norway, and assuming equal effectiveness of radiologists plus AI compared to standard practice, we simulated costs, effects and radiologist person-years over the next 20 years under different scenarios: 1) Assuming a €1 additional running cost of AI instead of the €3 assumed in the base case, 2) varying the AI-score thresholds for single vs. double readings, 3) varying the consensus and recall rates, and 4) reductions in the interval cancer rate compared to standard practice. Results: AI was unlikely to be cost-effective, even when only one radiologist was used alongside AI for all screening exams. This also applied when assuming a 10% reduction in the consensus and recall rates. However, there was a 30–50% reduction in the radiologists’ screen-reading volume. Assuming an additional running cost of €1 for AI, the costs were comparable, with similar probabilities of cost-effectiveness for AI and standard practice. Assuming a 5% reduction in the interval cancer rate, AI proved to be cost-effective across all willingness-to-pay values. Conclusions: AI may be cost-effective if the interval cancer rate is reduced by 5% or more, or if its additional cost is €1 per screening exam. Despite a substantial reduction in screening volume, this remains modest relative to the total radiologist person-years available within breast centers, accounting for only 3–4% of person-years.

Artificial intelligence (AI) in mammographic screening in Norway: BreastScreen Norway discusses how the results from their screening programme for early breast cancer detection can influence future artificial intelligence to streamline early breast cancer detection. Breast cancer is a significant global health concern, with more than 2 million new cases diagnosed and over half a million women dying from the disease annually.(1) Many countries, including Norway, have implemented mammographic screening to detect breast cancer in an early stage of disease development, as an early intervention has clear benefits on the disease outcome. In Norway, all women aged 50 to 69 years are invited to biennial mammographic screening through the national screening program, BreastScreen Norway.(2)

In a large breast cancer screening study, the benefits of prevalent digital breast tomosynthesis did not come at the expense of higher rates or more aggressive subsequent round screen-detected cancers. Background Prevalent digital breast tomosynthesis (DBT) has shown higher cancer detection rates and lower recall rates compared with those of digital mammography (DM). However, data are limited on ...