Clinical evaluation of a fully-automated parenchymal analysis software for breast cancer risk assessment: A pilot study in a Finnish sample
| Autor: | Anna-Leena Laaperi, Irina Rinta-Kiikka, Joni Kamarainen, Otso Arponen, Antti Sassi, Kirsi Holli-Helenius, Said Pertuz |
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| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
Imaging biomarker Breast Neoplasms Pilot Projects Asymptomatic 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Breast cancer Cancer risk assessment Risk Factors Image Interpretation Computer-Assisted Medicine Analysis software Humans Radiology Nuclear Medicine and imaging Breast Finland Aged Retrospective Studies business.industry General Medicine Odds ratio Middle Aged medicine.disease Confidence interval 030220 oncology & carcinogenesis Area Under Curve Case-Control Studies Female Radiology medicine.symptom business Risk assessment Algorithms Mammography |
| Zdroj: | European journal of radiology. 121 |
| ISSN: | 1872-7727 |
| Popis: | 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 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. |
| Databáze: | OpenAIRE |
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