Related Experiment Video For Breast cancer
Updated: Jan 4, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Clinical evaluation of a fully-automated parenchymal analysis software for breast cancer risk assessment: A pilot
Said Pertuz1, Antti Sassi2, Kirsi Holli-Helenius2
1Connectivity and Signal Processing group, Universidad Industrial de Santander, 680002 Bucaramanga, Colombia; Computing Sciences, Tampere University, 33710 Tampere, Finland.
Purpose:
To assess the association between breast cancer risk and mammographic parenchymal measures obtained using a fully-automated, publicly available software, OpenBreast.
Methods:
This retrospective case-control study involved screening mammograms of asymptomatic women diagnosed with breast cancer between 2016 and 2017. The 114 cases were matched with corresponding healthy controls by birth and screening years and the mammographic system used. Parenchymal analysis was performed using OpenBreast, a software implementing a computerized parenchymal analysis algorithm. Breast percent density was measured with an interactive thresholding method. The parenchymal measures were Box-Cox transformed and adjusted for age and percent density. Changes in the odds ratio per standard deviation (OPERA) with 95% confidence intervals (CIs) and the area under the ROC curve (AUC) for parenchymal measures and percent densities were used to evaluate the discrimination between cases and controls. Differences in AUCs were assessed using DeLong's test.
Results:
The adjusted OPERA value of parenchymal measures was 2.49 (95% CI: 1.79-3.47). Parenchymal measures using OpenBreast were more accurate (AUC = 0.779) than percent density (AUC = 0.609) in discriminating between cases and controls (p < 0.001).
Conclusions:
Parenchymal measures obtained with the evaluated software were positively associated with breast cancer risk and were more accurate than percent density in the prediction of risk.
