RSNA 2021 & SABCS 2021 citations

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. Sechopoulos^1,10

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. Leong^1,2, 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. Karssemeijer^1,2, 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. Guida^1,3, 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