Skip to content Provide accessibility feedback
For Providers
For Patients
Customer Stories
About Us
Contact Us
Magnifying glass Search
Impact on risk categorization with inclusion of mammographic density in the Tyrer-Cuzick model

S. Destounis1, J. Salamone1, A. Arieno1, A. Chan2, B. Drohan2

1. Elizabeth Wende Breast Care, LLC
2. Volpara Health


Association of automated breast positioning assessment with technical repeats and recalls in the nationalized UK screening program

H. Gilroy1, L. Martis1, B. Drohan1, R. P. Highnam1, M. L. Hill1

1. Volpara Health Technologies Ltd


Denser or just thinner? Mammographic breast density characterization around the world

W. Sanderink1, M. Hill2, S. Sagstad3, S. Mariapun4, A. Kuckian5, A. Tomal6, V Atienza-Hipolito7, W. Wu8, S. Teo4, R. Rajapakshe5, S. Hofvind3, A. Vourtsi9, I. Sechopoulos1,10

1. Radboud University Medical Center
2. Volpara Health
3. Cancer Registry of Norway
4. Cancer Research Malaysia
5. British Columbia Cancer Agency
6. University of Campinas
7. Women’s & Breast Imaging (WBI)
8. Changhua Christian Hospital
9. Diagnostic Mammography Center
10. Dutch Expert Centre for Screening


Breast density associations with tumor characteristics among screen-detected and interval breast cancers diagnosed in a US screening setting

S. Destounis1, A. Arieno1, A. Chan2

1. Elizabeth Wende Breast Care, LLC
2. Volpara Health


Volumetric breast density is a strong predictor of breast tumor size in a US screening population

S. Destounis1, A. Arieno1, R. Highnam2, A. Chan2

1. Elizabeth Wende Breast Care, LLC
2. Volapra Health


Deep learning predicts interval and screening-detected cancer from screening mammograms: A case-case-control study in 6369 women

X. Zhu1, T. K. Wolfgruber1, L. Leong1,2, M. Jensen3, C. G. Scott3, S. Winham3, P. Sadowski4, C. M. Vachon3, K. Kerlikowske5, J. A. Shepherd1

1. Department of Epidemiology, University of Hawaii Cancer Center
2. Department of Molecular Bioscience and Bioengineering, University of Hawaii at Manoa
3. Department of Health Sciences, Mayo Clinic
4. Department of Information and Computer Sciences, University of Hawaii at Manoa
5. Departments of Medicine and Epidemiology/Biostatistics, University of California, San Francisco


Triaging women from MRI to mammography to adapt screening to changes in breast density using artificial intelligence

B. H. M. van der Velden1, C. H. van Gils2, E. Verburg1, M. H. A. Janse1, M. F. Bakker2, R. M. Pijnappel3, W. B. Veldhuis3, K. G. A. Gilhuijs1

1. Image Sciences Institute
2. Julius Center for Health Sciences and Primary Care
3. Department of Radiology, UMC Utrecht


AI-triaging of breast MRI for radiological review in the screening of women with extremely dense breasts

E. Verburg1, C. van Gils2, B. van der Velden1, M. Bakker2, R. Pijnappel3, W. Veldhuis3, K. Gilhuijs1

1. Image Sciences Institute
2. Julius Center
3. Department of Radiology, UMC Utrecht


Replacing a radiologist by AI in Dutch population based breast cancer screening and the impact of breast density on performance

S. van Winkel1, N. Janssen2, N. Karssemeijer1,2, R. Mann1

1. Department of Medical Imaging, Radboud University Medical Center
2. ScreenPoint Medical BV


Commercially available AI system for breast cancer detection shows promise for risk prediction, including women with dense breasts

C. Vachon1, C. Scott1, S. Winham1, A. Norman1, C. Hruska1, K. Brandt1, K. Kerlikowske2

1. Mayo Clinic
2. University of California, San Francisco


Mammographic density in relation to breast cancer risk factors among Chinese women

H. Koka1, Y. Tian2, D. Lu1, K. Yu1, E. Li2, C. Guo2, B. Zhu1, J. L. Guida1,3, A. Chan4, N. Hu1, N. Lu2, G. L. Gierach1, J. Li2, X. R. Yang1

1. Division of Cancer Epidemiology and Genetics, National Cancer Institute
2. National Cancer Center/Cancer Hospital, Chinese Academy of Medical ciences and Peking Union Medical College
3. Division of Cancer Control and Population Sciences, National Cancer Institute
4. Volpara Health Technologies, Ltd

Access the full newsletter