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Computerized Analysis of Diffusion-Weighted Images in Breast Cancer Diagnosis

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T White

T White1*, W Weiss2 , M Giger3 , (1) ,,,(2) ,,,(3) Univ Chicago, Chicago, IL


TU-C-12A-6 Tuesday 10:15AM - 12:15PM Room: 12A

Purpose: Diffusion-weighted imaging (DWI) for breast MRI is a non-invasive technique which maps the diffusion process of water molecules, with the use of Brownian motion, in biological tissues, yielding apparent diffusion coefficients (ADC). Quantitative image analyses of the resulting images can yield information about the surrounding tissues in vivo. In this study, we investigated computerized image analyses of DWI images, including automated lesion segmentation, calculation of ADC values, and assessment of tumor heterogeneity, on a dataset of malignant and benign breast lesions.

Method and Database: The IRB-approved, retrospectively collected dataset included 46 breast lesions -- 36 malignant and 10 benign. The DW images had been acquired during clinical breast MRI on a high-field 1.5 T echo-planar system using five b values [b = 0, 500, 1000, 1500, and 2000 s/mm2]. Seed-point-initiated 3D Gaussian-based lesion segmentation was conducted to yield the lesion margins, and within the margin and after b-value fitting, ADC values were calculated. Average ADC values and various measures of heterogeneity were calculated for benign and malignant lesions.

Results: Our findings showed that malignant lesions tended, as expected, to have lower ADC values relative to benign lesions with average ADC values for benign and malignant lesions being 1.70 +/- 0.50 x 10(-3) and 1.07 +/- 0.37 x 10(-3), respectively. In addition, malignant lesions showed higher heterogeneity. In the task of distinguishing between benign and malignant lesions, ROC analysis, from histogram analysis, yielded AUC values of 0.86 +/- 0.07 and 0.88 +/- 0.06 for average ADC values and standard deviation of ADC values, respectively.

Conclusion:. DWI exhibits potential for differentiating between benign and malignant lesions, and inclusion of computerized quantitative image analysis methods allow for more objective and efficient calculations of the ADC values and their variations, for potential use in decision making.

Funding Support, Disclosures, and Conflict of Interest: M. L. Giger is a stockholder in R2 Technology/Hologic, is a shareholder/investor in Quantitative Insights, Qview Medical,receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi and Toshiba.

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