Comput Methods Programs Biomed - Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction.


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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.

Resumo Limpo

task breast densiti quantif becom increas relev due associ breast cancer risk work semiautom fulli autom tool assess breast densiti fullfield digit mammogram present first tool base supervis interact threshold procedur segment dens fatti tissu use twofold goal assess mammograph densiti md object accur way via visualbas method label mammogram later employ train fulli autom tool although autom method reli supervis approach base global label mammogram propos method reli pixellevel label allow better tissu classif densiti measur continu scale fulli autom method present combin classif scheme base local featur threshold oper improv perform classifi dataset mammogram use test concord approach measur md three expert radiologist measur md mammogram use semiautom tool dmscan measur fulli autom system correl method comput relat md breast cancer analyz use casecontrol dataset consist mammogram intraclass correl coeffici icc use comput reliabl among rater techniqu result obtain show averag icc among rater use semiautom tool whilst averag correl semiautom autom measur icc casecontrol studi result obtain show odd ratio per increas md use semiautom fulli autom approach respect can therefor conclud autom semiautom md assess present good correl method also found associ md breast cancer risk warrant propos tool breast cancer risk predict clinic decis make full version dmscan freeli avail

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