Volpara Solutions, the wholly owned sales and marketing arm of Matakina Technology, is drawing attention to a clinical study conducted by the Karolinska Institute in Sweden validating the firm's VolparaDensity breast imaging software.
The study, published in Cancer Epidemiology, Biomarkers and Prevention, has associated volumetrically assessed breast density measurements made by VolparaDensity with breast cancer risk (10 July 2014).
The prospective cohort Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study was initiated in January 2011 and included 70,876 women attending mammography screening or clinical mammography at four hospitals in Sweden. Digital mammography was performed using five different models from three vendors (GE Healthcare, Philips Healthcare, and Sectra). Volumetric breast density was measured using VolparaDensity. Study participants also donated blood for genotyping and filled out a detailed questionnaire.
Volumetric density was positively associated with never having given birth, age at first birth, hormone use, benign breast disease, and family history of breast cancer. Volumetric density was negatively associated with age and postmenopausal status.
The researchers found good agreement by side and by view, and distributions of volume density were similar across the different mammography systems, Volpara said. Based on the results, automated measurement of volumetric mammographic density using VolparaDensity is a promising tool for widespread breast cancer risk assessment, the researchers concluded.












![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)




