Semi-automated and fully automated mammographic density measurement and breastcancer risk prediction
Por:
Llobet R, Pollán M, Antón J, Miranda-García J, Casals M, Martínez I, Ruiz-Perales F, Pérez-Gómez B, Salas-Trejo D and Pérez-Cortés JC
Publicada:
1 sep 2014
Resumen:
The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density(MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case-control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case-control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available. (C) 2014 Elsevier Ireland Ltd. All rights reserved.
Filiaciones:
Llobet R:
Institute of Computer Technology, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Pollán M:
National Center for Epidemiology, Carlos III Institute of Health, Monforte de lemos 5, Madrid 28029, Spain
Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid 28029, Spain
Antón J:
Institute of Computer Technology, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
:
Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain
Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain
:
Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain
Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain
:
Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain
Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain
Ruiz-Perales F:
Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain
Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain
Pérez-Gómez B:
Institute of Computer Technology, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
National Center for Epidemiology, Carlos III Institute of Health, Monforte de lemos 5, Madrid 28029, Spain
:
Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain
Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain
Pérez-Cortés JC:
Institute of Computer Technology, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
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