Automated analysis of mammographic densities and breast carcinoma risk

There is considerable evidence that one of the strongest risk factors for breast carcinoma can be assessed from the mammographic appearance of the breast. However, the magnitude of the risk factor and the reliability of the prediction depend on the method of classification. Subjective classification requires specialized observer training and suffers from inter‐ and intraobserver variability. Furthermore, the categoric scales make it difficult to distinguish small differences in mammographic appearance. To address these limitations, automated analysis techniques that characterize mammographic density on a continuous scale have been considered, but as yet, these have been evaluated only for their ability to reproduce subjective classifications of mammographic parenchyma.

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