Three Breast Cancer Risk Insights. One View.
Some features not available in all markets.
Women diagnosed during the earliest stages of breast cancer tend to have better outcomes and survival rates.1 Volpara®Scorecard™ provides your breast care team with the insights they need to find cancer earlier.
A Clinical Decision-Support Tool for Personalized Breast Care
- Three patient risk insights in a single, customizable view
- Warning icons when patient meets high-risk thresholds
- Study dose and pressure information
- Available to radiologists during mammography interpretation
Volumetric breast density measurements PLUS a breast density category for a more objective and consistent assessment 2
Tyrer-Cuzick version 8 risk model to calculate lifetime risk of developing breast cancer 3
3. Transpara™ by ScreenPoint Medical
Uses machine learning to categorize mammograms by the likelihood of the presence of cancerous lesions 4
Experience the Benefits of VolparaScorecard
- Streamline workflow to inform reporting and early detection
- Focus on the patients that require more attention
- Access objective, science-based data to triage women to the appropriate supplemental screening or diagnostic testing
- Perform supplemental imaging while the patient is still in the facility for her annual mammogram
VolparaScorecard in Practice
- VolparaScorecard is easily accessible from the radiologist workstation as a secondary capture DICOM image
- Integration allows the risks factors to be included in structured reports
- With VolparaEnterprise Analytics, VolparaScorecard+ assists in identifying patient populations with dense breasts that may require additional services
1. Clinical outcomes in very early breast cancer (≤ 1cm): A national population based analysis. Mahvish Muzaffar, Abdul Rafeh Naqash, Nasreen A. Vohra, Darla K. Liles, and Jan H. Wong Journal of Clinical Oncology 2017 35:15_suppl, e12034-e12034
2. Gubern-Mérida, A., Kallenberg, M., Platel, B., Mann, R.M., Martí, R. and Karssemeijer, N. (2014) Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study. PLoS ONE; 9: e85952
3. Terry, M.B. et al. 10-year performance of four models of breast cancer risk: a validation study. Lancet Oncol 20, 504–517 (2019).
4. Rodriguez-Ruiz, et al., Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. Eur Radiology. 2019; 29(9): pp 4825–4832