VolparaEnterprise DDP Software—Density, Dose and Pressure Assessment

VolparaEnterprise Clinical Applications provide quick, automated assessment of every mammography and tomosynthesis exam. They quickly produce a Volpara Scorecard for every patient, including:

  • VolparaDensity—for quickly triaging women to supplemental screening that build new revenue streams 
  • VolparaDose—for better understanding the personalized x-ray dose to the patient, based on her own breast density 
  • VolparaPressure—for a clearer understanding of the patient experience and the effectiveness of each mammogram

Understand More About Breast Density

The breast density components of the Volpara Scorecard work together to give you an automated and repeatable platform for triaging women to supplemental imaging. Extensive clinical studies on the Volpara Density Grade (a BI-RADS ®-like breast composition value) and recent reports on the Volumetric Breast Density percentage (VBD%) have shown that increasing Volpara density values correspond to reduced mammography sensitivity1,2. Volpara Density Grade and VBD% have also both been demonstrated to relate to increased risk of breast cancer3,4. Thus, Volpara can be used to provide supplementary screening to women who need it most:

  • Screening Ultrasound: The Volpara Density Grade provides an objective means of identifying those dense breast patients who may benefit. Ultrasound is effective for finding additional cancers in women with dense breasts and negative mammography5
  • Breast MRI: The Volumetric Breast Density percentage (VBD%) provides a more granular assessment of density than is used in the Tyrer-Cuzick v8 Breast Cancer Risk Assessment Model, one of the gateways to breast MRI imaging of high-risk patients6

Best of all, VBD% has shown to strongly and linearly correlate to the sensitivity of mammography, making you aware of the performance of mammography as a test in each patient.

Understand More About Patient Dose

The Volpara Scorecard includes average dose information about the patient:

  • VolparaDose is computed based on the patient's breast composition, rather than manufacturer dose, which is based on phantom measurements
  • VolparaDose analyses each woman’s mammogram differently, because not all women with a 4-cm breast have the same breast density7 or receive the same radiation exposure

Understand More About Compression Pressure

Studies have shown that some women don't return to mammography because of discomfort, or fear of discomfort8:

  • VolparaPressure reveals the pressure applied to the breast by the force of compression that is distributed across each woman's breast tissue
  • VolparaPressure informs your practice about a key aspect of each woman’s experience in your practice9, how she has been treated, which is key to getting them to come back again
  • VolparaPressure also guides you towards optimal clinical performance, as recent studies have shown that too much or too little compression pressure can adversely affect the clinical performance of the exam10

Quality Begins with VolparaEnterprise Clinical Applications

VolparaEnterprise DDP bundles together three key clinical applications that can keep your attention focused on the patient. All VolparaEnterprise clinical applications stem from the breast density and other metrics that are measured from a woman’s mammogram. The clinical applications use repeatable and objective measures to avoid reliance on inter- and intra-reader variability9, or poor assumptions in the case of VolparaDose11.

 

 

1 Destounis, S., et al., Using volumetric breast density to quantify the potential masking risk of mammographic density. Am J Roentgenol, 2017. 208(1): p. 222–227.

2 Wanders, J.O., et al., Volumetric breast density affects performance of digital screening mammography. Breast Cancer Res Treat, 2017. 162(1): p. 95–103.

3 Brandt, K.R., et al., Comparison of clinical and automated breast density measurements: implications for risk prediction and supplemental screening. Radiology, 2016. 279(3): p. 710–9.

4 Eng, A., et al., Digital mammographic density and breast cancer risk: a case–control study of six alternative density assessment methods. Breast Cancer Res, 2014. 16(5): p. 439.

5 Tagliafico, A.S., et al., Adjunct screening with tomosynthesis or ultrasound in women with mammography-negative dense breasts: interim report of a prospective comparative trial. J Clin Oncol, 2016.

6 Smith, R.A., et al., Cancer screening in the United States, 2015: a review of current American cancer society guidelines and current issues in cancer screening. CA Cancer J Clin, 2015. 65(1): p. 30–54.

7 Dance, D. R. et al., “Additional factors for the estimation of mean glandular breast dose using the UK mammography dosimetry protocol,” Phys. Med. Biol., vol. 45, no. 11, p. 3225, 2000.

8 Whelehan, P., et al., The effect of mammography pain on repeat participation in breast cancer screening: a systematic review. Breast, 2013. 22(4): p. 389–94.

9 de Groot, J.E., et al., A novel approach to mammographic breast compression: Improved standardization and reduced discomfort by controlling pressure instead of force. Med Phys, 2013. 40(8): p. 081901.

10 Holland, K., et al., Performance of breast cancer screening depends on mammographic compression, in Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19–22, 2016, Proceedings, A. Tingberg, K. Lång, and P. Timberg, Editors. 2016, Springer International Publishing: Cham. p. 183–189.

11 Sprague, B.L., et al., Variation in mammographic breast density assessments among radiologists in clinical practice: a multicenter observational study. Ann Intern Med, 2016. 165(7): p. 457–464.

12 Tromans, C., A. Chan, and R. Highnam, Comparing personalized mean glandular dose estimates between x-ray systems over time in mammography, in ECR 2014, European Congress of Radiology. 2014: Vienna, Austria.

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