LYNNWOOD, Wash., Oct. 11, 2021 /PRNewswire/ — Volpara Health is proud to have reached their 200th peer-reviewed paper milestone, making the Volpara AI breast health platform the most independently validated of its kind. Volpara has also been featured in over 200 additional scientific works including conference presentations, books, and review articles. Reaching this achievement has been a decade in the making and emphasizes the company’s investment in using science and research to build evidence-based solutions that improve breast cancer detection, risk assessment and outcomes. Since the company was founded in 2009, based on technology originally from the University of Oxford, Volpara has been focused on building relationships with and supporting researchers in their quest to better understand breast cancer risk and prevention.
Volpara’s robust algorithm has firmly established the company as a trusted research partner across many high-profile studies, which have already generated a number of publications. Such research includes personalized screening and optimization studies, such as the DENSE1, TOMMY2, and To-Be3 trials, and the PROCAS I/II4 and KARMA5 studies. As a prime example, the DENSE trial, led by Professor Carla van Gils from the University Medical Center Utrecht, Netherlands, relied on Volpara’s TruDensityTM product to identify women with extremely dense breasts in their ongoing randomized controlled trial of supplemental MRI. The resulting reduction in interval cancers led the Dutch government to vote unanimously in favor of the addition of MRI exams for women with extremely dense breasts to the national screening program.
“Volpara is an excellent product that provides an array of breast density measures. Having access to Volpara for clinical research has allowed us to move the field of breast cancer risk prediction forward,” said Dr. Karla Kerlikowske of the University of California, San Francisco.
Research has consistently demonstrated strong association of volumetric breast density with breast cancer risk, including a collaboration between Dr. Adam Brentnall and Professor Jack Cuzick (both at the Queen Mary University of London, UK, and developers of the Tyrer-Cuzick (TC) breast cancer risk prediction model), Volpara co-founder Professor Martin Yaffe (Sunnybrook Research Institute, Toronto, ON, Canada), lead investigator Dr. Jennifer Harvey (University of Rochester, NY), and colleagues. Their work demonstrated the improved risk stratification from adding Volpara’s volumetric breast density to the widely used Tyrer-Cuzick (TC) breast cancer risk prediction model.
“The key improvement that Volpara brings to density reporting is an objective, reproducible density value that can be used in risk assessment models. These models are increasingly used to determine if a woman qualifies for MRI based screening, and also to decide if the risk is high enough to warrant preventive therapy to reduce risk,” Professor Cuzick said.
Volpara’s research collaborations span multiple disciplines, something which is only possible due to the company’s diverse team of scientists who are able to provide their expertise and ensure researchers receive customized support to leverage Volpara software to its fullest potential.
“You can’t go wrong with Volpara. They have a dedicated team who understand your research needs on mammographic breast density and will always respond to your questions. Notably, they are constantly improving the software and algorithm that drives Volpara,” Associate Professor Adetunji Toriola of Washington University School of Medicine said.
With the goal of saving more families from cancer through optimal screening pathways for patients, Volpara research that is particularly promising right now includes:
- Understanding the impact of automated image quality metrics on screening outcomes.
- Refining TruDensity measures and expanding its use to other modalities — like contrast-enhanced digital mammography.
- Improving on our understanding of breast density change to improve breast cancer risk prediction and response to drugs, such as hormone replacement therapies and risk-reducing medication
“We continue to use Volpara’s quantitative density measurements in studies… looking at changes in breast density as a possible indicator of changes in breast cancer risk, for example in the evaluation of potential risk-reducing interventions. We also use Volpara density as input to an algorithm that we developed to predict masking of breast cancers in mammograms by tissue density. The goal is precision screening where women for whom there is a high risk of cancer masking can be directed to alternative or supplemental screening modalities,” Professor Yaffe said.
This sentiment is echoed by Volpara’s Clinical Research Lead, Ariane Chan, PhD: “There is increasing scientific, peer-reviewed evidence demonstrating that Volpara’s breast health platform is transforming personalized screening and leading to real improvements in patient care.”
For any sized company, this breadth and volume of papers independently validating their portfolio is significant and impressive. Volpara is committed to continue supporting and collaborating with researchers to drive innovation and clinical advancements in breast cancer screening, risk assessment and prevention.
About Volpara Health
Volpara provides an advanced AI software platform that works with a healthcare provider’s expertise to enable a high-quality, optimized, and personalized cancer screening experience. From the time a patient enters a clinic to the moment they obtain key results, the Volpara Breast Health Platform collects and analyzes information to better understand a patient’s breast cancer risk, while also objectively evaluating image quality and workflow-improvement opportunities. These capabilities are being extended to lung cancer screening. The Volpara Breast Health Platform is supported by numerous patents, trademarks, and regulatory registrations including FDA clearance and CE marking, and is validated by a volume of peer-reviewed publications unrivaled in the breast screening industry.
References:
1. Dense Tissue and Early Breast Neoplasm Screening
2. A comparison of TOMosynthesis with digital MammographY in the UK NHS Breast Screening Programme
3. Digital Breast Tomosynthesis Trial in Bergen
4. Predicting Risk Of Cancer at Screening I/II
5. KARolinska MAmmography Project for RIsk Prediction of Breast Cancer
SOURCE Volpara Health
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