How do AI markings on screening mammograms correspond to cancer location? An informed review of 270 breast cancer cases in BreastScreen Norway.

OBJECTIVES: To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative. METHODS: In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative. RESULTS: All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative. CONCLUSION: AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting. CLINICAL RELEVANCE STATEMENT: In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score. KEY POINTS: • All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. • Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. • Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting.

AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway.

OBJECTIVE: To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program. MATERIALS AND METHOD: We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG. RESULTS: We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4. CONCLUSION: The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. CLINICAL RELEVANCE STATEMENT: Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. KEY POINTS: • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.

Optimizing the Pairs of Radiologists That Double Read Screening Mammograms.

Background Despite variation in performance characteristics among radiologists, the pairing of radiologists for the double reading of screening mammograms is performed randomly. It is unknown how to optimize pairing to improve screening performance. Purpose To investigate whether radiologist performance characteristics can be used to determine the optimal set of pairs of radiologists to double read screening mammograms for improved accuracy. Materials and Methods This retrospective study was performed with reading outcomes from breast cancer screening programs in Sweden (2008-2015), England (2012-2014), and Norway (2004-2018). Cancer detection rates (CDRs) and abnormal interpretation rates (AIRs) were calculated, with AIR defined as either reader flagging an examination as abnormal. Individual readers were divided into performance categories based on their high and low CDR and AIR. The performance of individuals determined the classification of pairs. Random pair performance, for which any type of pair was equally represented, was compared with the performance of specific pairing strategies, which consisted of pairs of readers who were either opposite or similar in AIR and/or CDR. Results Based on a minimum number of examinations per reader and per pair, the final study sample consisted of 3 592 414 examinations (Sweden, n = 965 263; England, n = 837 048; Norway, n = 1 790 103). The overall AIRs and CDRs for all specific pairing strategies (Sweden AIR range, 45.5-56.9 per 1000 examinations and CDR range, 3.1-3.6 per 1000; England AIR range, 68.2-70.5 per 1000 and CDR range, 8.9-9.4 per 1000; Norway AIR range, 81.6-88.1 per 1000 and CDR range, 6.1-6.8 per 1000) were not significantly different from the random pairing strategy (Sweden AIR, 54.1 per 1000 examinations and CDR, 3.3 per 1000; England AIR, 69.3 per 1000 and CDR, 9.1 per 1000; Norway AIR, 84.1 per 1000 and CDR, 6.3 per 1000). Conclusion Pairing a set of readers based on different pairing strategies did not show a significant difference in screening performance when compared with random pairing. © RSNA, 2023.

AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis.

Background Few studies have evaluated the role of artificial intelligence (AI) in prior screening mammography. Purpose To examine AI risk scores assigned to screening mammography in women who were later diagnosed with breast cancer. Materials and Methods Image data and screening information of examinations performed from January 2004 to December 2019 as part of BreastScreen Norway were used in this retrospective study. Prior screening examinations from women who were later diagnosed with cancer were assigned an AI risk score by a commercially available AI system (scores of 1-7, low risk of malignancy; 8-9, intermediate risk; and 10, high risk of malignancy). Mammographic features of the cancers based on the AI score were also assessed. The association between AI score and mammographic features was tested with a bivariate test. Results A total of 2787 prior screening examinations from 1602 women (mean age, 59 years ± 5.1 [SD]) with screen-detected (n = 1016) or interval (n = 586) cancers showed an AI risk score of 10 for 389 (38.3%) and 231 (39.4%) cancers, respectively, on the mammograms in the screening round prior to diagnosis. Among the screen-detected cancers with AI scores available two screening rounds (4 years) before diagnosis, 23.0% (122 of 531) had a score of 10. Mammographic features were associated with AI score for invasive screen-detected cancers (P < .001). Density with calcifications was registered for 13.6% (43 of 317) of screen-detected cases with a score of 10 and 4.6% (15 of 322) for those with a score of 1-7. Conclusion More than one in three cases of screen-detected and interval cancers had the highest AI risk score at prior screening, suggesting that the use of AI in mammography screening may lead to earlier detection of breast cancers. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Mehta in this issue.

Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases.

OBJECTIVES: To compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists. METHODS: In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratified by mammographic density. RESULTS: A total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round. CONCLUSIONS: The high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading. KEY POINTS: • The AI system scored 93% of the screen-detected cancers and 40% of the interval cancers with AI score 10. • The AI system scored all screen-detected cancers and almost 50% of interval cancers among women with the highest breast density with AI score 10. • About 40% of the screen-detected cancers had an AI score of 10 on the prior mammograms, indicating a potential for earlier detection by using AI in screen-reading.

Prolonged screening interval due to the COVID-19 pandemic and its association with tumor characteristics and treatment; a register-based study from BreastScreen Norway.

OBJECTIVE: During the COVID-19 pandemic Norway had to suspend its national breast cancer screening program. We aimed to investigate the effect of the pandemic-induced suspension on the screening interval, and its subsequent association with the tumor characteristics and treatment of screen-detected (SDC) and interval breast cancer (IC). METHODS: Information about women aged 50-69, participating in BreastScreen Norway, and diagnosed with a SDC (N = 3799) or IC (N = 1806) between 2018 and 2021 was extracted from the Cancer Registry of Norway. Logistic regression was used to investigate the association between COVID-19 induced prolonged screening intervals and tumor characteristics and treatment. RESULTS: Women with a SDC and their last screening exam before the pandemic had a median screening interval of 24.0 months (interquartile range: 23.8-24.5), compared to 27.0 months (interquartile range: 25.8-28.5) for those with their last screening during the pandemic. The tumor characteristics and treatment of women with a SDC, last screening during the pandemic, and a screening interval of 29-31 months, did not differ from those of women with a SDC, last screening before the pandemic, and a screening interval of 23-25 months. ICs detected 24-31 months after screening, were more likely to be histological grade 3 compared to ICs detected 0-23 months after screening (odds ratio: 1.40, 95% confidence interval: 1.06-1.84). CONCLUSIONS: Pandemic-induced prolonged screening intervals were not associated with the tumor characteristics and treatment of SDCs, but did increase the risk of a histopathological grade 3 IC. This study provides insights into the possible effects of extending the screening interval.

Early screening outcomes among non-immigrants and immigrants targeted by BreastScreen Norway, 2010-2019.

AIMS: This study aimed to analyse results on early screening outcomes, including recall and cancer rates, and histopathological tumour characteristics among non-immigrants and immigrants invited to BreastScreen Norway. METHODS: We included information about 2, 763,230 invitations and 2,087,222 screening examinations from 805,543 women aged 50-69 years who were invited to BreastScreen Norway between 2010 and 2019. Women were stratified into three groups based on their birth country: non-immigrants, immigrants born in Western countries and immigrants born in non-Western countries. Age-adjusted regression models were used to analyse early screening outcomes. A random intercept effect was included in models where women underwent several screening examinations. RESULTS: The overall attendance was 77.5% for non-immigrants, 68% for immigrants from Western countries and 51.5% for immigrants from non-Western countries. The rate of screen-detected cancers was 5.9/1000 screening examinations for non-immigrants, 6.3/1000 for immigrants from Western countries and 5.1/1000 for immigrants from non-Western countries. Adjusted for age, the rate did not differ statistically between the groups (p=0.091). The interval cancer rate was 1.7/1000 screening examinations for non-immigrants, 2.4/1000 for immigrants from Western countries and 1.6/1000 for non-Western countries (p<0.001). Histological grade was less favourable for screen-detected cancers, and subtype was less favourable for interval cancers among immigrants from non-Western countries versus non-immigrants. CONCLUSIONS: There were no differences in age-adjusted rate of screen-detected cancer among non-immigrants and immigrants from Western countries or non-Western countries among women attending BreastScreen Norway between 2010 and 2019. Small but clinically relevant differences in histopathological tumour characteristics were observed between the three groups.

Screening outcome for interpretation by the first and second reader in a population-based mammographic screening program with independent double reading.

BACKGROUND: Double reading of screening mammograms is associated with a higher rate of screen-detected cancer than single reading, but different strategies exist regarding reader pairing and blinding. Knowledge about these aspects is important when considering strategies for future use of artificial intelligence in mammographic screening. PURPOSE: To investigate screening outcome, histopathological tumor characteristics, and mammographic features stratified by the first and the second reader in a population based screening program for breast cancer. MATERIAL AND METHODS: The study sample consisted of data from 3,499,048 screening examinations from 834,691 women performed during 1996-2018 in BreastScreen Norway. All examinations were interpreted independently by two radiologists, 272 in total. We analyzed interpretation score, recall, and cancer detection, as well as histopathological tumor characteristics and mammographic features of the cancers, stratified by the first and second readers. RESULTS: For Reader 1, the rate of positive interpretations was 4.8%, recall 2.3%, and cancer detection 0.5%. The corresponding percentages for Reader 2 were 4.9%, 2.5%, and 0.5% (P < 0.05 compared with Reader 1). No statistical difference was observed for histopathological tumor characteristics or mammographic features when stratified by Readers 1 and 2. Recall and cancer detection were statistically higher and histopathological tumor characteristics less favorable for cases detected after concordant positive compared with discordant interpretations. CONCLUSION: Despite reaching statistical significance, mainly due to the large study sample, we consider the differences in interpretation scores, recall, and cancer detection between the first and second readers to be clinically negligible. For practical and clinical purposes, double reading in BreastScreen Norway is independent.

Cumulative risk of a false-positive screening result: A retrospective cohort study using empirical data from 10 biennial screening rounds in BreastScreen Norway.

BACKGROUND: False-positive screening results are an inevitable and commonly recognized disadvantage of mammographic screening. This study estimated the cumulative probability of experiencing a first false-positive screening result in women attending 10 biennial screening rounds in BreastScreen Norway, which targets women aged 50 to 69 years. METHODS: This retrospective cohort study analyzed screening outcomes from 421,545 women who underwent 1,894,523 screening examinations during 1995-2019. Empirical data were used to calculate the cumulative risk of experiencing a first false-positive screening result and a first false-positive screening result that involved an invasive procedure over 10 screening rounds. Logistic regression was used to evaluate the effect of adjusting for irregular attendance, age at screening, and number of screens attended. RESULTS: The cumulative risk of experiencing a first false-positive screening result was 18.04% (95% confidence interval [CI], 18.00%-18.07%). It was 5.01% (95% CI, 5.01%-5.02%) for experiencing a false-positive screening result that involved an invasive procedure. Adjusting for irregular attendance or age at screening did not appreciably affect these estimates. After adjustments for the number of screens attended, the cumulative risk of a first false-positive screening result was 18.28% (95% CI, 18.24%-18.32%), and the risk of a false-positive screening result including an invasive procedure was 5.11% (95% CI, 5.11%-5.22%). This suggested that there was minimal bias from dependent censoring. CONCLUSIONS: Nearly 1 in 5 women will experience a false-positive screening result if they attend 10 biennial screening rounds in BreastScreen Norway. One in 20 will experience a false-positive screening result with an invasive procedure. LAY SUMMARY: A false-positive screening result occurs when a woman attending mammographic screening is called back for further assessment because of suspicious findings, but the assessment does not detect breast cancer. Further assessment includes additional imaging. Usually, it involves ultrasound, and sometimes, it involves a biopsy. This study has evaluated the chance of experiencing a false-positive screening result among women attending 10 screening examinations over 20 years in BreastScreen Norway. Nearly 1 in 5 women will experience a false-positive screening result over 10 screening rounds. One in 20 women will experience a false-positive screening result involving a biopsy.

Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program.

Background Artificial intelligence (AI) has shown promising results for cancer detection with mammographic screening. However, evidence related to the use of AI in real screening settings remain sparse. Purpose To compare the performance of a commercially available AI system with routine, independent double reading with consensus as performed in a population-based screening program. Furthermore, the histopathologic characteristics of tumors with different AI scores were explored. Materials and Methods In this retrospective study, 122 969 screening examinations from 47 877 women performed at four screening units in BreastScreen Norway from October 2009 to December 2018 were included. The data set included 752 screen-detected cancers (6.1 per 1000 examinations) and 205 interval cancers (1.7 per 1000 examinations). Each examination had an AI score between 1 and 10, where 1 indicated low risk of breast cancer and 10 indicated high risk. Threshold 1, threshold 2, and threshold 3 were used to assess the performance of the AI system as a binary decision tool (selected vs not selected). Threshold 1 was set at an AI score of 10, threshold 2 was set to yield a selection rate similar to the consensus rate (8.8%), and threshold 3 was set to yield a selection rate similar to an average individual radiologist (5.8%). Descriptive statistics were used to summarize screening outcomes. Results A total of 653 of 752 screen-detected cancers (86.8%) and 92 of 205 interval cancers (44.9%) were given a score of 10 by the AI system (threshold 1). Using threshold 3, 80.1% of the screen-detected cancers (602 of 752) and 30.7% of the interval cancers (63 of 205) were selected. Screen-detected cancer with AI scores not selected using the thresholds had favorable histopathologic characteristics compared to those selected; opposite results were observed for interval cancer. Conclusion The proportion of screen-detected cancers not selected by the artificial intelligence (AI) system at the three evaluated thresholds was less than 20%. The overall performance of the AI system was promising according to cancer detection. © RSNA, 2022.

Possible strategies for use of artificial intelligence in screen-reading of mammograms, based on retrospective data from 122,969 screening examinations.

OBJECTIVES: Artificial intelligence (AI) has shown promising results when used on retrospective data from mammographic screening. However, few studies have explored the possible consequences of different strategies for combining AI and radiologists in screen-reading. METHODS: A total of 122,969 digital screening examinations performed between 2009 and 2018 in BreastScreen Norway were retrospectively processed by an AI system, which scored the examinations from 1 to 10; 1 indicated low suspicion of malignancy and 10 high suspicion. Results were merged with information about screening outcome and used to explore consensus, recall, and cancer detection for 11 different scenarios of combining AI and radiologists. RESULTS: Recall was 3.2%, screen-detected cancer 0.61% and interval cancer 0.17% after independent double reading and served as reference values. In a scenario where examinations with AI scores 1-5 were considered negative and 6-10 resulted in standard independent double reading, the estimated recall was 2.6% and screen-detected cancer 0.60%. When scores 1-9 were considered negative and score 10 double read, recall was 1.2% and screen-detected cancer 0.53%. In these two scenarios, potential rates of screen-detected cancer could be up to 0.63% and 0.56%, if the interval cancers selected for consensus were detected at screening. In the former scenario, screen-reading volume would be reduced by 50%, while the latter would reduce the volume by 90%. CONCLUSION: Several theoretical scenarios with AI and radiologists have the potential to reduce the volume in screen-reading without affecting cancer detection substantially. Possible influence on recall and interval cancers must be evaluated in prospective studies. KEY POINTS: • Different scenarios using artificial intelligence in combination with radiologists could reduce the screen-reading volume by 50% and result in a rate of screen-detected cancer ranging from 0.59% to 0.60%, compared to 0.61% after standard independent double reading • The use of artificial intelligence in combination with radiologists has the potential to identify negative screening examinations with high precision in mammographic screening and to reduce the rate of interval cancer.

Screen-detected and interval breast cancer after concordant and discordant interpretations in a population based screening program using independent double reading.

OBJECTIVES: To analyze rates, odds ratios (OR), and characteristics of screen-detected and interval cancers after concordant and discordant initial interpretations and consensus in a population-based screening program. METHODS: Data were extracted from the Cancer Registry of Norway for 487,118 women who participated in BreastScreen Norway, 2006-2017, with 2 years of follow-up. All mammograms were independently interpreted by two radiologists, using a score from 1 (negative) to 5 (high suspicion of cancer). A score of 2+ by one of the two radiologists was defined as discordant and 2+ by both radiologists as concordant positive. Consensus was performed on all discordant and concordant positive, with decisions of recall for further assessment or dismiss. OR was estimated with logistic regression with 95% confidence interval (CI), and histopathological tumor characteristics were analyzed for screen-detected and interval cancer. RESULTS: Among screen-detected cancers, 23.0% (697/3024) had discordant scores, while 12.8% (117/911) of the interval cancers were dismissed at index screening. Adjusted OR was 2.4 (95% CI: 1.9-2.9) for interval cancer and 2.8 (95% CI: 2.5-3.2) for subsequent screen-detected cancer for women dismissed at consensus compared to women with concordant negative scores. We found 3.4% (4/117) of the interval cancers diagnosed after being dismissed to be DCIS, compared to 20.3% (12/59) of those with false-positive result after index screening. CONCLUSION: Twenty-three percent of the screen-detected cancers was scored negative by one of the two radiologists. A higher odds of interval and subsequent screen-detected cancer was observed among women dismissed at consensus compared to concordant negative scores. Our findings indicate a benefit of personalized follow-up. KEY POINTS: • In this study of 487,118 women participating in a screening program using independent double reading with consensus, 23% screen-detected cancers were detected by only one of the two radiologists. • The adjusted odds ratio for interval cancer was 2.4 (95% confidence interval: 1.9, 2.9) for cases dismissed at consensus using concordant negative interpretations as the reference. • Interval cancers diagnosed after being dismissed at consensus or after concordant negative scores had clinically less favorable prognostic tumor characteristics compared to those diagnosed after false-positive results.

Quality of life among women with symptomatic, screen-detected, and interval breast cancer, and for women without breast cancer: a retrospective cross-sectional study from Norway.

PURPOSE: Breast cancers detected at screening need less aggressive treatment compared to breast cancers detected due to symptoms. The evidence on the quality of life associated with screen-detected versus symptomatic breast cancer is sparse. This study aimed to compare quality of life among Norwegian women with symptomatic, screen-detected and interval breast cancer, and women without breast cancer and investigate quality adjusted life years (QALYs) for women with breast cancer from the third to 14th year since diagnosis. METHODS: This retrospective cross-sectional study was focused on women aged 50 and older. A self-reported questionnaire including EQ-5D-5L was sent to 11,500 women. Multivariable median regression was used to analyze the association between quality of life score (visual analogue scale 0-100) and detection mode. Health utility values representing women's health status were extracted from EQ-5D-5L. QALYs were estimated by summing up the health utility values for women stratified by detection mode for each year between the third and the 14th year since breast cancer diagnosis, assuming that all women would survive. RESULTS: Adjusted regression analyses showed that women with screen-detected (n = 1206), interval cancer (n = 1005) and those without breast cancer (n = 1255) reported a higher median quality of life score using women with symptomatic cancer (n = 1021) as reference; 3.7 (95%CI 2.2-5.2), 2.3 (95%CI 0.7-3.8) and 4.8 (95%CI 3.3-6.4), respectively. Women with symptomatic, screen-detected and interval cancer would experience 9.5, 9.6 and 9.5 QALYs, respectively, between the third and the 14th year since diagnosis. CONCLUSION: Women with screen-detected or interval breast cancer reported better quality of life compared to women with symptomatic cancer. The findings add benefits of organized mammographic screening.

The total prevalence of diagnosed diabetes and the quality of diabetes care for the adult population in Salten, Norway.

Objective: To assess the total prevalence of types 1 and 2 diabetes and to describe and compare cardiovascular risk factors, vascular complications and the quality of diabetes care in adults with types 1 and 2 diabetes in Salten, Norway. Research design and methods: Cross-sectional study including all patients with diagnosed diabetes in primary and specialist care in Salten, 2014 (population 80,338). Differences in cardiovascular risk factors, prevalence of vascular complications and attained treatment targets between diabetes types were assessed using regression analyses. Results: We identified 3091 cases of diabetes, giving a total prevalence in all age groups of 3.8%, 3.4% and 0.45% for types 2 and 1 diabetes, respectively. In the age group 30-89 years the prevalence of type 2 diabetes was 5.3%. Among 3027 adults aged 18 years and older with diabetes, 2713 (89.6%) had type 2 and 304 (10.0%) type 1 diabetes. The treatment target for haemoglobin A1c (⩽7.0%/53 mmol/mol) was reached in 61.1% and 22.5% of types 2 and 1 diabetes patients, respectively. After adjusting for age, sex and diabetes duration we found differences between patients with types 2 and 1 diabetes in mean haemoglobin A1c (7.1% vs. 7.5%, P<0.001), blood pressure (136/78 mmHg vs. 131/74 mmHg, P<0.001) and prevalence of coronary heart disease (23.1% vs. 15.8%, P<0.001). Conclusions: The prevalence of diagnosed type 2 diabetes was slightly lower than anticipated. Glycaemic control was not satisfactory in the majority of patients with type 1 diabetes. Coronary heart disease was more prevalent in patients with type 2 diabetes.

Factors associated with treatment in primary versus specialist care: A population-based study of people with type 2 and type 1 diabetes.

AIMS: The objectives of this study are to identify the proportion and characteristics of people with type 1 and 2 diabetes treated in primary, specialist and shared care and to identify the proportion of persons with type 2 diabetes reaching HbA1c treatment targets and the clinical risk factors and general practitioner and practice characteristics associated with treatment in specialist care. METHODS: Population-based cross-sectional study including all adults ≥18 years diagnosed with diabetes in primary and specialist care in Salten, Norway. We used multivariable mixed-effects logistic regression models with level of care as outcome variable and population, general practitioner, and practice characteristics as exposure variables. RESULTS: Of 2704 people with type 2 diabetes, 13.5% were treated in shared care and 2.1% in specialist care only. Of 305 people with type 1 diabetes, 14.4% received treatment in primary care only. The HbA1c treatment target of 53 mmol/mol (7.0%) was reached by 67.3% of people with type 2 diabetes in primary care versus 30.4% in specialist care. HbA1c , use of insulin, coronary heart disease, retinopathy and urban practice location were positively associated with treatment in specialist care. General practitioners' use of a structured form and a diabetes nurse were negatively associated with specialist care. CONCLUSIONS: Of people with type 2 diabetes, 16% were treated in specialist care. They had higher HbA1c and more vascular complications, as expected from priority guidelines. The use of a structured diabetes form and diabetes nurses seem to support type 2 diabetes follow-up in primary care.

Does it matter for the radiologists' performance whether they read short or long batches in organized mammographic screening?

OBJECTIVE: To analyze the association between radiologists' performance and image position within a batch in screen reading of mammograms in Norway. METHOD: We described true and false positives and true and false negatives by groups of image positions and batch sizes for 2,937,312 screen readings performed from 2012 to 2018. Mixed-effects models were used to obtain adjusted proportions of true and false positive, true and false negative, sensitivity, and specificity for different image positions. We adjusted for time of day and weekday and included the individual variation between the radiologists as random effects. Time spent reading was included in an additional model to explore a possible mediation effect. RESULT: True and false positives were negatively associated with image position within the batch, while the rates of true and false negatives were positively associated. In the adjusted analyses, the rate of true positives was 4.0 per 1000 (95% CI: 3.8-4.2) readings for image position 10 and 3.9 (95% CI: 3.7-4.1) for image position 60. The rate of true negatives was 94.4% (95% CI: 94.0-94.8) for image position 10 and 94.8% (95% CI: 94.4-95.2) for image position 60. Per 1000 readings, the rate of false negative was 0.60 (95% CI: 0.53-0.67) for image position 10 and 0.62 (95% CI: 0.55-0.69) for image position 60. CONCLUSION: There was a decrease in the radiologists' sensitivity throughout the batch, and although this effect was small, our results may be clinically relevant at a population level or when multiplying the differences with the number of screen readings for the individual radiologists. KEY POINTS: • True and false positive reading scores were negatively associated with image position within a batch. • A decreasing trend of positive scores indicated a beneficial effect of a certain number of screen readings within a batch. • False negative scores increased throughout the batch but the association was not statistically significant.

Group-based multimodal physical therapy in women with chronic pelvic pain: A randomized controlled trial.

INTRODUCTION: Chronic pelvic pain in women is a complex condition, and physical therapy is recommended as part of a broader treatment approach. The objective of this study was to compare structured group-based multimodal physical therapy in a hospital setting (intervention group) with primary-care physical therapy (comparator group) for women with chronic pelvic pain. MATERIAL AND METHODS: Women aged 20-65 years with pelvic pain ≥6 months and referred for physical therapy were eligible. The primary outcome measure was change in the mean pelvic pain intensity from baseline to 12 months, measured using the numeric rating scale (0-10). Secondary outcomes were changes in scores of "worst" and "least" pain intensity, health-related quality of life, movement patterns, pain-related fear of movements, anxiety and depression, subjective health complaints, sexual function, incontinence, and obstructed defecation. The differences between the groups regarding change in scores were analyzed using the independent t test and Mann-Whitney U test. Sensitivity analysis of the primary outcome was performed with a linear regression model adjusted for the baseline value. A P value <.05 was considered statistically significant. RESULTS: Of the 62 women included, 26 in the intervention group and 25 in the comparator group were available after 12 months for data collection and analysis. The difference between the groups for change in the mean pain intensity score was -1.2 (95% CI -2.3 to -0.2; P = .027), favoring the intervention group. The intervention group showed greater improvements in respiratory patterns (mean difference 0.9; 95% CI 0.2-1.6; P = .015) and pain-related fear of movements (mean difference 2.9; 95% CI -5.5 to -0.3; P = .032), and no significant differences were observed between the groups for the other secondary outcomes. CONCLUSIONS: Although the reduction in the mean pelvic pain intensity with group-based multimodal physical therapy was significantly more than with primary-care physical therapy, the difference in the change between the groups was less than expected and the clinical relevance is uncertain.

Seroprevalence of hepatitis E virus (HEV) in a general adult population in Northern Norway: the Tromsø study.

Hepatitis E virus (HEV) is a major cause of acute viral hepatitis in many parts of the world but only a few cases have been diagnosed in Norway. To investigate the HEV exposure rate in a presumed low-risk area, we have conducted a population-based study of anti-HEV IgG seroprevalence in Northern Norway. A total of 1800 serum samples from 900 women and 900 men, age 40-79 years, were randomly selected from the 21,083 participants in the 7th Tromsø Study, representing the 32,591 inhabitants of the Tromsø municipality that were ≥ 40 years. All samples were analyzed by ELISA-1 (recomWell HEV IgG). Samples testing positive or borderline, as well as a 1.5-fold excess of negative samples, were retested by ELISA-2 (DiaPro HEV IgG). If still borderline or a result discordant from ELISA-1, the sample was retested by ELISA-3 (Wantai HEV IgG) and strip-immunoassay (recomLine HEV IgG). Anti-HEV IgG was detected in 205 individuals (11.4%), yielding an estimated seroprevalence of 10.4% in the age-matched population of Tromsø. Using logistic regression analysis followed by multivariable backward elimination analysis, increasing age (OR 1.036 per year; p < 0.001) and higher education (OR 2.167; p < 0.001) were found as potential risk factors, whereas travel abroad or eating of red meat were not. Our results indicate that HEV-infection is common in Northern Norway and suggest that HEV testing should be included in the evaluation of elevated liver enzymes.

Correction to: Seroprevalence of hepatitis E virus (HEV) in a general adult population in Northern Norway: the Tromsø study.

Unfortunately, the supplement tables are missing in the original article. The missing files have been included here.

Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway.

Purpose To explore the stand-alone 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 BreastScreen Norway, 2004-2018. The study sample included 3807 screen-detected cancers and 1110 interval breast cancers. 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 screen-detected cancers and interval breast cancers combined and 0.97 (95% CI: 0.97, 0.97) for screen-detected cancers. 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 of 3807) of the screen-detected cancers and 44.6% (495 of 1110) of the interval breast cancers were identified with AI. In this scenario, 68.5% (10 987 of 16 040) of false-positive screening results (negative recall assessment) were considered negative by AI. When 50% was used as the cutoff, 99.3% (3781 of 3807) of the screen-detected cancers and 85.2% (946 of 1110) of the interval breast cancers were identified as positive by AI, whereas 17.0% (2725 of 16 040) of the false-positive results were considered negative. Conclusion The AI system showed high performance in detecting breast cancers within 2 years of screening mammography and a potential for use to triage low-risk mammograms to reduce radiologist workload. Keywords: Mammography, Breast, Screening, Convolutional Neural Network (CNN), Deep Learning Algorithms Supplemental material is available for this article. © RSNA, 2024 See also commentary by Bahl and Do in this issue.