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A Margin Sharpness Measurement for the Diagnosis of Breast Cancer from Magnetic Resonance Imaging Examinations

Published:September 28, 2011DOI:https://doi.org/10.1016/j.acra.2011.08.004

      Rationale and Objectives

      Cancer screening by magnetic resonance imaging (MRI) has been shown to be one of the most sensitive methods available for the early detection of breast cancer. There is high variability in the diagnostic accuracy of radiologists analyzing the large amounts of data acquired in a breast MRI examination, and this has motivated substantial research toward the development of computer-aided detection and diagnosis systems. Most computer-aided diagnosis systems for breast MRI focus on dynamic information (how a lesion’s brightness changes over the course of an examination after the injection of a contrast agent). The inclusion of lesion margin measurements is much less common. One characteristic of malignant tumors is that they grow into neighboring tissues. This growth creates tumor margins that are variably fuzzy or diffuse (ie, they are not sharp).

      Materials and Methods

      In this short report, the authors present a new method for measuring a tumor’s margin from breast MRI examinations and compare it with an existing mathematical technique for margin measurements.

      Results

      The proposed method can yield a test with sensitivity of 77% (specificity, 65%) on screening data, outperforming existing mathematical lesion margin measurement methods. Furthermore, when the presented margin measurement is combined with existing dynamic features, there is a statistically significant improvement in computer-aided diagnosis test performance (P < .0014).

      Conclusions

      The proposed method for measuring a tumor’s margin outperforms existing mathematical methods on an extremely challenging data set containing many small lesions. The technique presented may be useful in discriminating between malignant and benign lesions in the context of the computer-aided diagnosis of breast cancer from MRI.

      Key Words

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