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Assessing a woman’s risk of breast cancer is important for personalized screening. Mammographic density is a strong risk factor for breast cancer, but parenchymal texture patterns offer additional information which cannot be captured by density. We aimed to combine BI-RADS density score 4th Edition and a deep-learning-based texture score to stratify women in screening and compare rates among the combinations. This retrospective study cohort study included 216,564 women from a Danish populations-based screening program. Baseline mammograms were evaluated using BI-RADS density scores (1–4) and a deep-learning texture risk model, with scores categorized into four quartiles (1–4). The incidence rate ratio (IRR) for screen-detected, interval, and long-term cancer were adjusted for age, year of screening and screening clinic. Compared with subgroup B1-T1, the highest IRR for screen-detected cancer were within the T4 category (3.44 (95% CI: 2.43–4.82)−4.57 (95% CI: 3.66–5.76)). IRR for interval cancer was highest in the BI-RADS 4 category (95% CI: 5.36 (1.77–13.45)−16.94 (95% CI: 9.93–30.15)). IRR for long-term cancer increased both with increasing BI-RADS and increasing texture reaching 5.15 (4.31–6.16) for the combination of B4-T4 compared with B1-T1. Deep-learning-based texture analysis combined with BI-RADS density categories can reveal subgroups with increased rates beyond what density alone can ascertain, suggesting the potential of combining texture and density to improve risk stratification in breast cancer screening. Keywords: breast cancer screening; mammography; breast density; parenchymal texture, Assessing a woman's risk of breast cancer is important for personalized screening. Mammographic density is a strong risk factor for breast cancer, but parenchymal texture patterns offer additional information which cannot be captured by density. We aimed to combine BI-RADS density score 4th Edition and a deep-learning-based texture score to stratify women in screening and compare rates among the combinations. This retrospective study cohort study included 216,564 women from a Danish populations-based screening program. Baseline mammograms were evaluated using BI-RADS density scores (1-4) and a deep-learning texture risk model, with scores categorized into four quartiles (1-4). The incidence rate ratio (IRR) for screen-detected, interval, and long-term cancer were adjusted for age, year of screening and screening clinic. Compared with subgroup B1-T1, the highest IRR for screen-detected cancer were within the T4 category (3.44 (95% CI: 2.43-4.82)-4.57 (95% CI: 3.66-5.76)). IRR for interval cancer was highest in the BI-RADS 4 category (95% CI: 5.36 (1.77-13.45)-16.94 (95% CI: 9.93-30.15)). IRR for long-term cancer increased both with increasing BI-RADS and increasing texture reaching 5.15 (4.31-6.16) for the combination of B4-T4 compared with B1-T1. Deep-learning-based texture analysis combined with BI-RADS density categories can reveal subgroups with increased rates beyond what density alone can ascertain, suggesting the potential of combining texture and density to improve risk stratification in breast cancer screening.

Background Internationally, non-participation in breast screening increased with decreasing level of education indicating importance of information campaigns to enhance awareness of screening. However, in Denmark in the 1990s the association between education and non-participation was U-shaped. We therefore analyzed recent Danish data. Methods Data derived from the Capital Region of Denmark, biennial, organized breast screening program 2008–2020, where women aged 50–69 were personally invited to screening. Non-participation was measured as number of women with no participation out of women eligible for at least three invitations. Sociodemographic determinants were identified by linkage to public registers. Results were reported as age adjusted odds ratios (OR) of non-participation including 95% confidence intervals (CI). Results Among 196,085 women, 86% participated. Using women with low education as baseline, the OR for professional bachelors was 0.64; and for academics 0.75. The strongest determinants of non-participation were being non-married OR 2.03; born outside Denmark OR 2.04; being self-employed OR 1.67; retired OR 3.12; on public support OR 3.66; or having co-morbidity OR 1.56. Conclusion The U-shaped association between education and non-participation in breast screening prevailed. The data further indicated that screening participation was low in women with pertinent health and social problems., Background: Internationally, non-participation in breast screening increased with decreasing level of education indicating importance of information campaigns to enhance awareness of screening. However, in Denmark in the 1990s the association between education and non-participation was U-shaped. We therefore analyzed recent Danish data. Methods: Data derived from the Capital Region of Denmark, biennial, organized breast screening program 2008–2020, where women aged 50–69 were personally invited to screening. Non-participation was measured as number of women with no participation out of women eligible for at least three invitations. Sociodemographic determinants were identified by linkage to public registers. Results were reported as age adjusted odds ratios (OR) of non-participation including 95% confidence intervals (CI). Results: Among 196,085 women, 86% participated. Using women with low education as baseline, the OR for professional bachelors was 0.64; and for academics 0.75. The strongest determinants of non-participation were being non-married OR 2.03; born outside Denmark OR 2.04; being self-employed OR 1.67; retired OR 3.12; on public support OR 3.66; or having co-morbidity OR 1.56. Conclusion: The U-shaped association between education and non-participation in breast screening prevailed. The data further indicated that screening participation was low in women with pertinent health and social problems.

Purpose: Risk-stratified breast cancer screening might improve early detection and efficiency without comprising quality. However, modern mammography-based risk models do not ensure adaptation across vendor-domains and rely on cancer precursors, associated with short-term risk, which might limit long-term risk assessment. We report a cross-vendor mammographic texture model for long-term risk. Approach: The texture model was robustly trained using two systematically designed case-control datasets. Textural features, indicative of future breast cancer, were learned by excluding samples with diagnosed/potential malignancies from training. An augmentation-based domain adaption technique, based on flavorization of mammographic views, ensured generalization across vendor-domains. The model was validated in 66,607 consecutively screened Danish women with flavorized Siemens views and 25,706 Dutch women with Hologic-processed views. Performances were evaluated for interval cancers (IC) within 2 years from screening and long-term cancers (LTC) from 2 years after screening. The texture model was combined with established risk factors to flag 10% of women with the highest risk. Results: In Danish women, the texture model achieved an area under the receiver operating characteristic curve (AUC) of 0.71 and 0.65 for ICs and LTCs, respectively. In Dutch women with Hologic-processed views, the AUCs were not different from AUCs in Danish women with flavorized views. The AUC for texture combined with established risk factors increased to 0.68 for LTCs. The 10% of women flagged as high-risk accounted for 25.5% of ICs and 24.8% of LTCs. Conclusions: The texture model robustly estimated long-term breast cancer risk while adapting to an unseen processed vendor-domain and identified a clinically relevant high-risk subgroup.

Background: Several techniques exist for performing targeted axillary dissection (TAD) after neoadjuvant chemotherapy with the removal of the sentinel node and a marked metastatic lymph node (LN). Two-step methods include coil-marking of the metastatic LN at diagnosis and re-marking with an intraoperatively identifiable marker before surgery. Because nondetection of the marked lymph node (MLN) warrants axillary clearance and many patients achieve axillary pathological complete response (ax-pCR), the success of TAD is crucial. We compare various two-step TAD methods in a Danish national cohort. Methods: We included patients who received two-step TAD between January 1, 2016 and August 31, 2021. Patients were identified from the Danish Breast Cancer Group database and cross-checked with locally accessible lists. Data were extracted from the patient’s medical files. Results: We included 543 patients. In 79.4%, preoperative, ultrasound-guided re-marking was possible. Nonidentification of the coil-marked LN was more likely in patients with ax-pCR. The second markers used were hook-wire, iodine seeds, or ink marking on the axillary skin. Of patients with successful secondary marking, the MLN identification rate (IR) was 91%, and the sentinel node (SN) IR was 95%. Marking with iodine seeds was significantly more successful than ink marking with an odds ratio of 5.34 (95% confidence interval 1.62-17.60). The success rate of the complete TAD with the removal of MLN and SN was 82.3%. Conclusions: With two-step TAD, nonidentification of the coiled LN before surgery is frequent, especially in patients with ax-pCR. Despite successful remarking, the IR of the MLN at surgery is inferior to one-step TAD.

Background: Denmark was one of the few countries where it was politically decided to continue cancer screening during the COVID-19 pandemic. We assessed the actual population uptake of mammography and cervical screening during this period. Methods: The first COVID-19 lockdown in Denmark was announced on 11 March 2020. To investigate possible changes in cancer screening activity due to the COVID-19 pandemic, we analysed data from the beginning of 2017 until the end of 2021. A time series analysis was carried out to discover possible trends and outliers in the screening activities in the period 2017–2021. Data on mammography screening and cervical screening were retrieved from governmental pandemic-specific monitoring of health care activities. Results: A brief drop was seen in screening activity right after the first COVID-19 lockdown, but the activity quickly returned to its previous level. A short-term deficit of 43% [CI –49 to –37] was found for mammography screening. A short-term deficit of 62% [CI –65 to –58] was found for cervical screening. Furthermore, a slight, statistically significant downward trend in cervical screening from 2018 to 2021 was probably unrelated to the pandemic. Other changes, for example, a marked drop in mammography screening towards the end of 2021, also seem unrelated to the pandemic. Conclusions: Denmark continued cancer screening during the pandemic, but following the first lockdown a temporary drop was seen in breast and cervical screening activity.

Early studies reported a 4- to 6-fold risk of breast cancer between women with extremely dense and fatty breasts. As most early studies were case-control studies, we took advantage of a population-based screening program to study density and breast cancer incidence in a cohort design. In the Capital Region, Denmark, women aged 50 to 69 are invited to screening biennially. Women screened November 2012 to December 2017 were included, and classified by BI-RADS density code, version 4, at first screen after recruitment. Women were followed up for incident breast cancer, including ductal carcinoma in situ (DCIS), to 2020 in nationwide pathology data. Rate ratios (RRs) and 95% confidence intervals (CI) were compared across density groups using Poisson-regression. We included 189 609 women; 1 067 282 person-years; and 4110 incident breast cancers/DCIS. Thirty-three percent of women had BI-RADS density code 1; 38% code 2; 24% code 3; 4.7% code 4; and missing 0.3%. Using women with BI-RADS density code 1 as baseline; women with code 2 had RR 1.69 (95% CI 1.56-1.84); women with code 3, RR 2.06 (95% CI 1.89-2.25); and women with code 4, RR 2.37 (95% CI 1.05-2.74). Results differed between observations accumulated during screening and above screening age. Our results indicated less difference in breast cancer risk across level of breast density than normally stated. Translated into absolute risk of breast cancer after age 50, we found a 6.2% risk for the one-third of women with lowest density, and 14.7% for the 5% of women with highest density, Early studies reported a 4- to 6-fold risk of breast cancer between women with extremely dense and fatty breasts. As most early studies were case-control studies, we took advantage of a population-based screening program to study density and breast cancer incidence in a cohort design. In the Capital Region, Denmark, women aged 50 to 69 are invited to screening biennially. Women screened November 2012 to December 2017 were included, and classified by BI-RADS density code, version 4, at first screen after recruitment. Women were followed up for incident breast cancer, including ductal carcinoma in situ (DCIS), to 2020 in nationwide pathology data. Rate ratios (RRs) and 95% confidence intervals (CI) were compared across density groups using Poisson-regression. We included 189 609 women; 1 067 282 person-years; and 4110 incident breast cancers/DCIS. Thirty-three percent of women had BI-RADS density code 1; 38% code 2; 24% code 3; 4.7% code 4; and missing 0.3%. Using women with BI-RADS density code 1 as baseline; women with code 2 had RR 1.69 (95% CI 1.56-1.84); women with code 3, RR 2.06 (95% CI 1.89-2.25); and women with code 4, RR 2.37 (95% CI 1.05-2.74). Results differed between observations accumulated during screening and above screening age. Our results indicated less difference in breast cancer risk across level of breast density than normally stated. Translated into absolute risk of breast cancer after age 50, we found a 6.2% risk for the one-third of women with lowest density, and 14.7% for the 5% of women with highest density.

Importance: Breast cancer-specific mortality is increased among women with intellectual disability (ID), and knowledge about participation in breast cancer screening in this group is needed. Objective: To examine participation in the Danish national breast cancer screening program among women with ID compared with women without ID. Design, Setting, and Participants: This dynamic population-based cohort study assessed participation in the Danish national breast cancer screening program initiated in 2007, targeting women aged 50 to 69 years with a screening interval of 2 years. In all, 6357 women with ID born between 1941 and 1967 and eligible for the screening program were identified in national registers. Women entered the study between January 1, 2007, and December 31, 2017. Subsequently, 273 women were excluded due to a history of carcinoma in situ or breast cancer, and 489 due to registration errors in registers. Each woman was individually age-matched with 10 women without ID (reference group). All women were followed up until March 31, 2021, or censoring (due to death, carcinoma in situ, or breast cancer). Data were analyzed from December 1, 2021, to June 31, 2022. Exposures: Intellectual disability was defined as being registered with an ID diagnosis or a diagnosis most likely leading to ID or residing at an institution for persons with ID. Main Outcomes and Measures: Participation in breast cancer screening (fully, partly, and never). Results: A total of 5595 women with ID and 49 423 age-matched women in the reference group were included in the analysis. Of these, 2747 women with ID (49%) and 24 723 in the reference group (50%) were 50 years of age at study entry; for those older than 50 years, the median age was 51 years (IQR, 50-58 years) in both groups. In all, 1425 women with ID (25%) were fully screened according to guidelines for the Danish breast cancer screening program compared with 30 480 women in the reference group (62%). Women with ID had near

Background Recent mammography-based risk models can estimate short-term or long-term breast cancer risk, but whether risk assessment may improve by combining these models has not been evaluated. Purpose To determine whether breast cancer risk assessment improves when combining a diagnostic artificial intelligence (AI) system for lesion detection and a mammographic texture model. Materials and Methods This retrospective study included Danish women consecutively screened for breast cancer at mammography from November 2012 to December 2015 who had at least 5 years of follow-up data. Examinations were evaluated for short-term risk using a commercially available diagnostic AI system for lesion detection, which produced a score to indicate the probability of cancer. A mammographic texture model, trained on a separate data set, assessed textures associated with long-term cancer risk. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate both the individual and combined performance of the AI and texture models for the prediction of future cancers in women with a negative screening mammogram, including those with interval cancers diagnosed within 2 years of screening and long-term cancers diagnosed 2 years or more after screening. AUCs were compared using the DeLong test. Results The Danish screening cohort included 119 650 women (median age, 59 years [IQR, 53-64 years]), of whom 320 developed interval cancers and 1401 developed long-term cancers. The combination model achieved a higher AUC for interval and long-term cancers grouped together than either the diagnostic AI (AUC, 0.73 vs 0.70; P < .001) or the texture risk (AUC, 0.73 vs 0.66; P < .001) models. The 10% of women with the highest combined risk identified by the combination model accounted for 44.1% (141 of 320) of interval cancers and 33.7% (472 of 1401) of long-term cancers. Conclusion Combining a diagnostic AI system and mammographic texture model resulted in improve

Introduction: Calcific uremic arteriolopathy is a life-threatening cutaneous condition in patients with chronic kidney disease. Often, clinical diagnosis is accompanied by histopathologic evaluations demonstrating vascular calcium deposits. We aimed to investigate the presence of cutaneous calcifications in non-lesional tissue in patients with chronic kidney disease, and the relation to systemic vascular calcification. Methods: We investigated the presence of cutaneous vascular calcifications in non-lesional skin biopsies from patients with current or previous calcific uremic arteriolopathy and patients with different stages of chronic kidney disease without calcific uremic arteriolopathy, and explored their association with vascular calcification in other vascular beds. Systemic vascular calcification was examined by mammography and lumbar X-ray. Results: Thirty-nine adults were enrolled (current or previous calcific uremic arteriolopathy, n = 9; end-stage chronic kidney disease, n = 12; chronic kidney disease stage 3b-4, n = 12; healthy controls, n = 6). All calcific uremic arteriolopathy patients had end-stage kidney disease. Cutaneous vascular calcifications were not present in any of the non-lesional skin punch biopsies. Breast arterial calcification was demonstrated in patients with calcific uremic arteriolopathy (75%) and chronic kidney disease (end-stage 67% and stage 3b-4 25%, respectively), but in none of the controls. All chronic kidney disease patients had systemic calcification on lumbar X-ray (median score 21, 22, and 15 in patients with calcific uremic arteriolopathy, end-stage kidney disease and chronic kidney disease stage 3b-4). The serum calcification propensity was significantly different between groups. Discussion: Despite a high burden of systemic vascular calcification, cutaneous calcium deposits in non-lesional tissue could not be demonstrated histopathologically in patients with chronic kidney disease (with or without current or previous