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1. Quality of life of women with a screen-detected versus clinically detected breast cancer in the Netherlands: a prospective cohort study

3. Improving Lesion Volume Measurements on Digital Mammograms

4. Dutch, UK and US professionals perceptions of screening for Barretts esophagus and esophageal adenocarcinoma: a concept mapping study.

9. Changes in breast cancer treatment during the COVID-19 pandemic: a Dutch population-based study

10. Personalized early detection and prevention of breast cancer: ENVISION consensus statement

11. Publisher Correction: Personalized early detection and prevention of breast cancer: ENVISION consensus statement

15. A response to "Personalised medicine and population health: breast and ovarian cancer".

19. The prognostic potential of mammographic growth rate of invasive breast cancer in the Nijmegen breast cancer screening cohort.

20. Patients' perceptions of targeted breast ultrasound and digital breast tomosynthesis in the diagnostic setting: A mixed methods study.

27. Diagnostic accuracy of supplemental three-dimensional breast ultrasound in the work-up of BI-RADS 0 screening recalls

28. Recommendations from the European Commission Initiative on Breast Cancer for multigene testing to guide the use of adjuvant chemotherapy in patients with early breast cancer, hormone receptor positive, HER-2 negative

29. Implementing Multifactorial Risk Assessment with Polygenic Risk Scores for Personalized Breast Cancer Screening in the Population Setting: Challenges and Opportunities.

31. Statistical Methods for Estimating the Cumulative Risk of Screening Mammography Outcomes

37. Variation in detection of ductal carcinoma in situ during screening mammography: A survey within the International Cancer Screening Network

38. Women's Experiences with Digital Breast Tomosynthesis and Targeted Breast Ultrasound for Focal Breast Complaints: A Survey Study.

39. Method of primary breast cancer detection and the disease-free interval, adjusting for lead time.

46. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study

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