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Purpose: The effectiveness of current follow-up guidelines after breast cancer treatment is uncertain. Tailored surveillance based on patient age and tumor characteristics may be more adequate. This study aimed to analyze the frequency of ipsilateral locoregional recurrences (LR) and second primary breast cancers (SP) detected outside of scheduled surveillance and to analyze risk factors associated with these events. Methods: Patients with surgically treated early-stage breast cancer from the Malmö Diet and Cancer Study (MDCS), 1991–2014 (n = 1080), and the Västernorrland region, 2009–2018 (n = 1648), were included. Clinical and pathological information on the primary tumor and recurrences was retrieved from medical records. The mode of recurrence detection was defined as detection within (planned) or outside (symptomatic) of scheduled surveillance. Results: The median follow-up was 6.5 years. Overall, 461 patients experienced a recurrence. The most common initial event was distant metastasis (47%), followed by locoregional recurrence (LR) (22%) and second primary (SP) (18%). 56% of LR and 28% of SP were identified outside of scheduled surveillance. Logistic regression analysis revealed that younger age (under 50 years) (OR 2.57, 95% CI 1.04–6.88), lymph node-positive breast cancer (OR 2.10, 95% CI 1.03–4.39) and breast cancer of the HER2 positive subtype (OR 5.24, 95% CI 1.40–25.90) were correlated with higher odds of detecting a recurrence outside of planned surveillance. Conclusion: Most recurrent events were detected outside of scheduled surveillance, particularly for locoregional recurrences. Risk-based surveillance, which takes into account patient and tumor characteristics, might be more suitable for specific patient subsets.

Purpose: The effectiveness of current follow-up guidelines after breast cancer treatment is uncertain. Tailored surveillance based on patient age and tumor characteristics may be more adequate. This study aimed to analyze the frequency of ipsilateral locoregional recurrences (LR) and second primary breast cancers (SP) detected outside of scheduled surveillance and to analyze risk factors associated with these events. Methods: Patients with surgically treated early-stage breast cancer from the Malmö Diet and Cancer Study (MDCS), 1991–2014 (n = 1080), and the Västernorrland region, 2009–2018 (n = 1648), were included. Clinical and pathological information on the primary tumor and recurrences was retrieved from medical records. The mode of recurrence detection was defined as detection within (planned) or outside (symptomatic) of scheduled surveillance. Results: The median follow-up was 6.5 years. Overall, 461 patients experienced a recurrence. The most common initial event was distant metastasis (47%), followed by locoregional recurrence (LR) (22%) and second primary (SP) (18%). 56% of LR and 28% of SP were identified outside of scheduled surveillance. Logistic regression analysis revealed that younger age (under 50 years) (OR 2.57, 95% CI 1.04–6.88), lymph node-positive breast cancer (OR 2.10, 95% CI 1.03–4.39) and breast cancer of the HER2 positive subtype (OR 5.24, 95% CI 1.40–25.90) were correlated with higher odds of detecting a recurrence outside of planned surveillance. Conclusion: Most recurrent events were detected outside of scheduled surveillance, particularly for locoregional recurrences. Risk-based surveillance, which takes into account patient and tumor characteristics, might be more suitable for specific patient subsets.

Deep learning has shown to have great potential in medical applications. In critical domains as such, it is of high interest to have trustworthy algorithms which are able to tell when reliable assessments cannot be guaranteed. Detecting out-of-distribution (OOD) samples is a crucial step towards building a safe classifier. Following a previous study, showing that it is possible to classify breast cancer in point-of-care ultrasound images, this study investigates OOD detection using three different methods: softmax, energy score and deep ensembles. All methods are tested on three different OOD data sets. The results show that the energy score method outperforms the softmax method, performing well on two of the data sets. The ensemble method is the most robust, performing the best at detecting OOD samples for all three OOD data sets.

Purpose: The European Society on Breast Imaging has recommended supplemental magnetic resonance imaging (MRI) every two to four years for women with mammographically dense breasts. This may not be feasible in many screening programs. Also, the European Commission Initiative on Breast Cancer suggests not implementing screening with MRI. By analyzing interval cancers and time from screening to diagnosis by density, we present alternative screening strategies for women with dense breasts. Methods: Our BreastScreen Norway cohort included 508 536 screening examinations, including 3125 screen-detected and 945 interval breast cancers. Time from screening to interval cancer was stratified by density measured by an automated software and classified into Volpara Density Grades (VDGs) 1–4. Examinations with volumetric density ≤3.4% were categorized as VDG1, 3.5%–7.4% as VDG2, 7.5%–15.4% as VDG3, and ≥15.5% as VDG4. Interval cancer rates were also determined by continuous density measures. Results: Median time from screening to interval cancer was 496 (IQR: 391–587) days for VDG1, 500 (IQR: 350–616) for VDG2, 482 (IQR: 309–595) for VDG3 and 427 (IQR: 266–577) for VDG4. A total of 35.9% of the interval cancers among VDG4 were detected within the first year of the biennial screening interval. For VDG2, 26.3% were detected within the first year. The highest annual interval cancer rate (2.7 per 1000 examinations) was observed for VDG4 in the second year of the biennial interval. Conclusions: Annual screening of women with extremely dense breasts may reduce the interval cancer rate and increase program-wide sensitivity, especially in settings where supplemental MRI screening is not feasible.

Objectives: Artificial intelligence (AI) is increasingly tested and integrated into breast cancer screening. Still, there are unresolved issues regarding its possible ethical, social and legal impacts. Furthermore, the perspectives of different actors are lacking. This study investigates the views of breast radiologists on AI-supported mammography screening, with a focus on attitudes, perceived benefits and risks, accountability of AI use, and potential impact on the profession.Methods:We conducted an online survey of Swedish breast radiologists. As early adopter of breast cancer screening, and digital technologies, Sweden is a particularly interesting case to study. The survey had different themes, including: attitudes and responsibilities pertaining to AI, and AI’s impact on the profession. Responses were analysed using descriptive statistics and correlation analyses. Free texts and comments were analysed using an inductive approach.Results:Overall, respondents (47/105, response rate 44.8%) were highly experienced in breast imaging and had a mixed knowledge of AI. A majority (n=38, 80.8%) were positive/somewhat positive towards integrating AI in mammography screening. Still, many considered there to be potential risks to a high/somewhat high degree (n=16, 34.1%) or were uncertain (n=16, 34.0%). Several important uncertainties were identified, such as defining liable actor(s) when AI is integrated into medical decision-making.Conclusions:Swedish breast radiologists are largely positive towards integrating AI in mammography screening, but there are significant uncertainties that need to be addressed, especially regarding risks and responsibilities. The results stress the importance of understanding actor-specific and context-specific challenges to responsible implementation of AI in healthcare.

PURPOSE To compare the positive predictive values (PPVs) of BI-RADS categories used to assess pure mammographic calcifications in women with and without a previous history of breast cancer (PHBC). MATERIALS AND METHODS In this retrospective study, all consecutive pure mammographic calcifications (n = 320) undergoing a stereotactic biopsy between 2016 and 2018 were identified. Mammograms were evaluated in consensus by two radiologists according to BI-RADS and blinded to patient history and pathology results. Final pathologic results were used as the standard of reference. PPV of BI-RADS categories were compared between the two groups. Data were evaluated using standard statistics, Mann-Whitney U tests and Chi-square tests. RESULTS Two hundred sixty-eight patients (274 lesions, median age 54 years, inter-quartile range, 50-65 years) with a PHBC (n = 46) and without a PHBC (n = 222) were included. Overall PPVs were the following: BI-RADS 2, 0% (0 of 56); BI-RADS 3, 9.1% (1 of 11); BI-RADS 4a, 16.2% (6 of 37); BI-RADS 4b, 37.5% (48 of 128); BI-RADS 4c, 47.3% (18 of 38) and BI-RADS 5, 100% (4 of 4). The PPV of BI-RADS categories was similar in patients with and without a PHBC (P = .715). Calcifications were more often malignant in patients with a PHBC older than 10 years (47.3%, 9 of 19) compared to 1-2 years (25%, 1 of 4), 2-5 years (20%, 2 of 10) and 5-10 years (0%, of 13) from the first breast cancer (P = .005). CONCLUSION PPV of mammographic calcifications is similar in women with or without PHBC when BI-RADS classification is strictly applied. A higher risk of malignancy was observed in patients with a PHBC longer than 10 years.

Background: Artificial intelligence (AI) systems are proposed as a replacement of the first reader in double reading within mammography screening. We aimed to assess cancer detection accuracy of an AI system in a Danish screening population. Methods: We retrieved a consecutive screening cohort from the Region of Southern Denmark including all participating women between Aug 4, 2014, and August 15, 2018. Screening mammograms were processed by a commercial AI system and detection accuracy was evaluated in two scenarios, Standalone AI and AI-integrated screening replacing first reader, with first reader and double reading with arbitration (combined reading) as comparators, respectively. Two AI-score cut-off points were applied by matching at mean first reader sensitivity (AIsens) and specificity (AIspec). Reference standard was histopathology-proven breast cancer or cancer-free follow-up within 24 months. Coprimary endpoints were sensitivity and specificity, and secondary endpoints were positive predictive value (PPV), negative predictive value (NPV), recall rate, and arbitration rate. Accuracy estimates were calculated using McNemar’s test or exact binomial test. Results: Out of 272,008 screening mammograms from 158,732 women, 257,671 (94.7%) with adequate image data were included in the final analyses. Sensitivity and specificity were 63.7% (95% CI 61.6%-65.8%) and 97.8% (97.7-97.8%) for first reader, and 73.9% (72.0-75.8%) and 97.9% (97.9-98.0%) for combined reading, respectively. Standalone AIsens showed a lower specificity (-1.3%) and PPV (-6.1%), and a higher recall rate (+ 1.3%) compared to first reader (p < 0.0001 for all), while Standalone AIspec had a lower sensitivity (-5.1%; p < 0.0001), PPV (-1.3%; p = 0.01) and NPV (-0.04%; p = 0.0002). Compared to combined reading, Integrated AIsens achieved higher sensitivity (+ 2.3%; p = 0.0004), but lower specificity (-0.6%) and PPV (-3.9%) as well as hig

Purpose: The European Society on Breast Imaging has recommended supplemental magnetic resonance imaging (MRI) every two to four years for women with mammographically dense breasts. This may not be feasible in many screening programs. Also, the European Commission Initiative on Breast Cancer suggests not implementing screening with MRI. By analyzing interval cancers and time from screening to diagnosis by density, we present alternative screening strategies for women with dense breasts. Methods: Our BreastScreen Norway cohort included 508 536 screening examinations, including 3125 screen-detected and 945 interval breast cancers. Time from screening to interval cancer was stratified by density measured by an automated software and classified into Volpara Density Grades (VDGs) 1–4. Examinations with volumetric density ≤3.4% were categorized as VDG1, 3.5%–7.4% as VDG2, 7.5%–15.4% as VDG3, and ≥15.5% as VDG4. Interval cancer rates were also determined by continuous density measures. Results: Median time from screening to interval cancer was 496 (IQR: 391–587) days for VDG1, 500 (IQR: 350–616) for VDG2, 482 (IQR: 309–595) for VDG3 and 427 (IQR: 266–577) for VDG4. A total of 35.9% of the interval cancers among VDG4 were detected within the first year of the biennial screening interval. For VDG2, 26.3% were detected within the first year. The highest annual interval cancer rate (2.7 per 1000 examinations) was observed for VDG4 in the second year of the biennial interval. Conclusions: Annual screening of women with extremely dense breasts may reduce the interval cancer rate and increase program-wide sensitivity, especially in settings where supplemental MRI screening is not feasible.

Background: Artificial intelligence (AI) systems are proposed as a replacement of the first reader in double reading within mammography screening. We aimed to assess cancer detection accuracy of an AI system in a Danish screening population. Methods: We retrieved a consecutive screening cohort from the Region of Southern Denmark including all participating women between Aug 4, 2014, and August 15, 2018. Screening mammograms were processed by a commercial AI system and detection accuracy was evaluated in two scenarios, Standalone AI and AI-integrated screening replacing first reader, with first reader and double reading with arbitration (combined reading) as comparators, respectively. Two AI-score cut-off points were applied by matching at mean first reader sensitivity (AIsens) and specificity (AIspec). Reference standard was histopathology-proven breast cancer or cancer-free follow-up within 24 months. Coprimary endpoints were sensitivity and specificity, and secondary endpoints were positive predictive value (PPV), negative predictive value (NPV), recall rate, and arbitration rate. Accuracy estimates were calculated using McNemar’s test or exact binomial test. Results: Out of 272,008 screening mammograms from 158,732 women, 257,671 (94.7%) with adequate image data were included in the final analyses. Sensitivity and specificity were 63.7% (95% CI 61.6%-65.8%) and 97.8% (97.7-97.8%) for first reader, and 73.9% (72.0-75.8%) and 97.9% (97.9-98.0%) for combined reading, respectively. Standalone AIsens showed a lower specificity (-1.3%) and PPV (-6.1%), and a higher recall rate (+ 1.3%) compared to first reader (p < 0.0001 for all), while Standalone AIspec had a lower sensitivity (-5.1%; p < 0.0001), PPV (-1.3%; p = 0.01) and NPV (-0.04%; p = 0.0002). Compared to combined reading, Integrated AIsens achieved higher sensitivity (+ 2.3%; p = 0.0004), but lower specificity (-0.6%) and PPV (-3.9%) as well as hig

This study aimed to investigate the growth rate of 31 consecutive invasive breast cancers based on volume measures on at least two serial mammograms and its relation to histopathological findings. The average tumour volume-doubling time in all invasive breast cancer subtypes was 282 d (range 46-749 d). Grade III breast cancers had a significantly shorter average tumour volume-doubling time of 105 d (range 46-157 d) compared with Grade I and II tumours (average of 296 d, range 147-531 d and average of 353 d, range 139-749 d, respectively) (p = 0.002). Multiple linear regression identified that tumour volume-doubling time was positively associated with patient age, histological grade and progesterone receptor expression and inversely associated with axillary lymph node involvement, human epidermal growth factor receptor 2 and Ki-67 expression (p < 0.001). In conclusion, tumour volume-doubling time as estimated on serial mammography may provide important prognostic information relevant for clinical decision-making.

In this study, we investigate which factors affect the false positive fraction (FPF) for digital breast tomosynthesis (DBT) compared to digital mammography (DM) in a screening population by using classification and regression trees (C&RT) and binary marginal generalized linear models. The data was obtained from the Malmö Breast Tomosynthesis Screening Trial, which aimed to compare the performance of DBT to DM in breast cancer screening. By using data from the first half of the study population (7500 women), a tree with the recall probability for different groups was calculated. The effect of age and breast density on the FPF was estimated using a binary marginal generalized linear model. Our results show that breast density and breast cancer were the main factors influencing recall. The FPF is mainly affected by breast density and increases with breast density for DBT and DM. In conclusion, the results obtained with C&RT are easy to interpret and similar to those obtained using binary marginal generalized linear models. The FPF is approximately 40% higher for DBT compared to DM for all breast density categories.

Objective: To analyse discrepant breast cancer detection in digital breast tomosynthesis (DBT) and digital mammography (DM). Methods: From a previous detection study comparing DBT and DM, 26 discrepant cases were extracted, 19 detected by DBT only and 7 by DM only. An expert panel of 3 radiologists reviewed these cases and documented level of discrepancy, lesion visibility, radiographic pattern, lesion conspicuity and assessed the reason for non-detection. Differences between groups were tested using the Wilcoxon Rank Sum test, the Kruskal-Wallis test and visual grading characteristics. Results: The proportion of lesion periphery in fatty tissue was statistically significantly larger, and there were significantly more spiculated masses in DBT compared to DM in the DBT Only group (p=0.018; p=0.015). The main reasons for missing a lesion were poor lesion visibility when using DM and interpretative error when using DBT. Conclusion: Lesion visualization is superior with DBT, particularly of spiculated tumours. A major reason for non-detection in DBT seems to be interpretative error, which may be due to lack of experience.