News | Computer-Aided Detection Software | April 11, 2017

Parascript and Volpara Establish Partnership for Improved Early Cancer Detection

Companies combine automated breast density assessment with computer-aided detection to help find breast cancers earlier

Parascript and Volpara Establish Partnership for Improved Early Cancer Detection

April 11, 2017 — Parascript LLC announced a new partnership with Volpara Solutions, creator of Volpara Density automated breast density assessment software. Independent research has associated an increased risk of cancer for patients with high breast density, which can also make cancer harder to detect on conventional mammograms. Together, Parascript AccuDetect CAD (computer-aided detection) and VolparaDensity provide complementary early cancer detection solutions.

Screening patients with dense breasts can pose additional challenges to healthcare providers since dense tissue has been shown to hide tumors and is associated with an increased risk of not only breast cancer, but more aggressive breast cancer. Accurate automated measurement of volumetric fibroglandular breast density assists radiologists in each patient’s personalized screening and decision making for additional diagnostics. Correctly identifying patients at low risk is also important to avoid unnecessary tests, because it can be stressful and costly for patients called back for further assessment. Parascript AccuDetect CAD, powered by deep learning, uses multiple independent cancer detection algorithms and a unique patented voting methodology to combine its findings. Comparing the results of the multiple image recognition processes allows for improved sensitivity and reduced false-positive rates.

In a study by the Karolinska Institute in Sweden of 41,102 women from KARMA (KARolinska MAmmography project for risk prediction of breast cancer) that looked at mammography screenings and clinical mammography at four hospitals in Sweden, VolparaDensity was validated across multiple mammography vendor platforms. This study concluded that automated measurement of volumetric mammographic density using VolparaDensity was a promising tool for breast cancer risk assessment.

In addition to more accurate detection, AccuDetect processes images more rapidly, which can improve response time to patients. AccuDetect CAD processes at 11 seconds per image, the fastest of all available U.S. Food and Drug Administration (FDA)-approved CAD systems, according to the company. The average CAD system takes 30 seconds or more to process a single image. AccuDetect CAD also supports early, more accurate detection, delivering high performance on dense and extremely dense breasts, according to a clinical study reported in Clinical Imaging (M. Lobbes et al., Clinical Imaging 37 (2013) 283-288).

Parascript and Volpara both exhibited at the Society for Breast Imaging (SBI) Breast Imaging Symposium in Los Angeles, held on April 6-8, 2017, and provided product demonstrations.

For more information: www.volparasolutions.com, www.parascript.com

Related Content

In a demonstration on the exhibit floor of the SBI symposium, Koios software identified suspicious lesions in ultrasound images

In a demonstration on the exhibit floor of the SBI symposium, Koios software identified suspicious lesions in ultrasound images. Photo by Greg Freiherr

Feature | Artificial Intelligence | April 19, 2019 | By Greg Freiherr
Commercial efforts to develop...
Artificial Intelligence Performs As Well As Experienced Radiologists in Detecting Prostate Cancer
News | Artificial Intelligence | April 18, 2019
University of California Los Angeles (UCLA) researchers have developed a new artificial intelligence (AI) system to...
Videos | Breast Imaging | April 18, 2019
In a keynote lecture at the Society of Breast Imaging (SBI)/American College of Radiology (ACR) 2019 Symposium, ...
Fatty tissue and breast density may be considered in the context of many factors that affect the occurrence and detection of breast cancer

Fatty tissue and breast density may be considered in the context of many factors that affect the occurrence and detection of breast cancer. Permission to publish provided by DenseBreast-info.org

Feature | Breast Imaging | April 18, 2019 | By Greg Freiherr
When planning a screening program to detect the early signs of breast cancer, age is a major consideration.
Oxipit Introduces Multilingual Support for ChestEye AI Imaging Suite
News | Artificial Intelligence | April 16, 2019
The CE-certified ChestEye artificial intelligence (AI) imaging suite by Oxipit is now available in seven European...
iCAD Appoints Stacey Stevens as President
News | Radiology Business | April 16, 2019
iCAD Inc. recently announced that Stacey Stevens has been named president. As president, Stevens will have expanded...
compressed breast during mammography.
360 Photos | 360 View Photos | April 16, 2019
A 360 view of a simulated breast compression for a...
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used “Deep Learning,“ a form of machine learning, which is a type of artificial intelligence. Graphic courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Artificial Intelligence | April 12, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.