Objective: To perform a radiological review of mammograms from prior screening and diagnosis of screen-detected breast cancer in BreastScreen Norway, a population-based screening program. Methods: We performed a consensus-based informed review of mammograms from prior screening and diagnosis for screen-detected breast cancers. Mammographic density and findings on screening and diagnostic mammograms were classified according to the Breast Imaging-Reporting and Data System®. Cases were classified based on visible findings on prior screening mammograms as true (no findings), missed (obvious findings), minimal signs (minor/non-specific findings), or occult (no findings at diagnosis). Histopathologic tumor characteristics were extracted from the Cancer Registry of Norway. The Bonferroni correction was used to adjust for multiple testing; p < 0.001 was considered statistically significant. Results: The study included mammograms for 1225 women with screen-detected breast cancer. Mean age was 62 years ± 5 (SD); 46% (567/1225) were classified as true, 22% (266/1225) as missed, and 32% (392/1225) as minimal signs. No difference in mammographic density was observed between the classification categories. At diagnosis, 59% (336/567) of true and 70% (185/266) of missed cancers were classified as masses (p = 0.004). The percentage of histological grade 3 cancers was higher for true (30% (138/469)) than for missed (14% (33/234)) cancers (p < 0.001). Estrogen receptor positivity was observed in 86% (387/469) of true and 95% (215/234) of missed (p < 0.001) cancers. Conclusions: We classified 22% of the screen-detected cancers as missed based on a review of prior screening mammograms with diagnostic images available. One main goal of the study was quality improvement of radiologists’ performance and the program. Visible findings on prior screening mammograms were not necessarily indicative of screening failure. Key Points: • After a consensus-based informed review, 46% of screen-detected breast cancers were classified as true, 22% as missed, and 32% as minimal signs. • Less favorable prognostic and predictive tumor characteristics were observed in true screen-detected breast cancer compared with missed. • The most frequent mammographic finding for all classification categories at the time of diagnosis was mass, while the most frequent mammographic finding on prior screening mammograms was a mass for missed cancers and asymmetry for minimal signs.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To explore the standalone breast cancer detection performance at different risk score thresholds of a commercially available artificial intelligence (AI) system. Materials and Methods This retrospective study included information from 661,695 digital mammographic examinations performed among 242,629 female individuals screened as a part of x, 2004-2018. The study sample included 3807 screen-detected cancers (SDC) and 1110 interval breast cancers (IC). A continuous examination level risk score by the AI system was used to measure performance as the area under the receiver operating characteristic curve (AUC) with 95% CIs and cancer detection at different AI risk score thresholds. Results The AUC of the AI system was 0.93 (95% CI: 0.92-0.93) for SDC and IC combined and 0.97 (95% CI: 0.97-0.97) for SDC. In a setting where 10% of the examinations with the highest AI risk scores were defined as positive and 90% with the lowest scores as negative, 92.0% (3502/3807) of the SDC and 44.6% (495/1100) of the IC were identified by AI. In this scenario, 68.5% (10 987/16 029) of false positive screening results (negative recall assessment) were considered negative by AI. When 50% was used as the cut-off, 99.3% (3781/3807) of the SDC and 85.2% (946/1100) of the IC were identified as positive by AI, while 17.0% (2725/16 029) of the false positives were considered as negative. Conclusion The AI system showed high performance in detecting breast cancers within 2 years of screening mammography and a potential for triaging low-risk mammograms to reduce radiologist workload. ©RSNA, 2024.

Background/Objectives: Polygenic risk scores (PRSs) have been extensively studied and are increasingly applied in healthcare. One of the most studied and developed areas is predictive medicine for breast cancer, but there is no wider consensus on the indications for the clinical use of PRSs for breast cancer. This current guidance endeavours to articulate the scientific evidence underpinning the clinical utility of PRSs in stratifying breast cancer risk, with a particular emphasis on clinical application. Methods: This guidance has been prepared by a group of experts who have been active in breast cancer PRS research and development, combining a review of the evidence base with expert opinion for indications for clinical use. Results: Based on data from various studies and existing breast cancer prevention and screening services, the indications for clinical use of breast cancer PRSs can be divided into the following scenarios: (1) Management of cancer-free women with a family history of cancer; (2) individual personalised breast cancer prevention and screening in healthcare services; and (3) breast cancer screening programs for more personalised screening. Conclusions: The integration of PRSs into clinical practice enables healthcare providers to deliver more accurate risk assessments, personalised prevention strategies, and optimised screening programmes, thereby improving patient outcomes and enhancing the effectiveness of breast cancer care. PRS testing represents a novel component in clinical breast cancer risk assessment, supporting a personalised, risk-based approach to breast cancer prevention and screening.

To compare the distribution of mammographic features among women recalled for further assessment after screening with digital breast tomosynthesis (DBT) versus digital mammography (DM), and to assess associations between features and final outcome of the screening, including immunohistochemical subtypes of the tumour. This randomized controlled trial was performed in Bergen, Norway, and included 28,749 women, of which 1015 were recalled due to mammographic findings. Mammographic features were classified according to a modified BI-RADS-scale. The distribution were compared using 95 % confidence intervals (CI). Asymmetry was the most common feature of all recalls, 24.3 % (108/444) for DBT and 38.9 % (222/571) for DM. Spiculated mass was most common for breast cancer after screening with DBT (36.8 %, 35/95, 95 %CI: 27.2-47.4) while calcifications (23.0 %, 20/87, 95 %CI: 14.6-33.2) was the most frequent after DM. Among women screened with DBT, 0.13 % (95 %CI: 0.08-0.21) had benign outcome after recall due to indistinct mass while the percentage was 0.28 % (95 %CI: 0.20-0.38) for DM. The distributions were 0.70 % (95 %CI: 0.57-0.85) versus 1.46 % (95 %CI: 1.27-1.67) for asymmetry and 0.24 % (95 %CI: 0.16-0.33) versus 0.54 % (95 %CI: 0.43-0.68) for obscured mass, among women screened with DBT versus DM, respectively. Spiculated mass was the most common feature among women diagnosed with non-luminal A-like cancer after DBT and after DM. Spiculated mass was the dominant feature for breast cancer among women screened with DBT while calcifications was the most frequent feature for DM. Further studies exploring the clinical relevance of mammographic features visible particularly on DBT are warranted.

Two deep learning–based artificial intelligence (AI) models, one commercially available and one in-house, showed good performance for stand-alone cancer detection on retrospective mammography screening data. AI markings on the mammograms corresponded well to the true cancer location.