Ms. Esther Seoyeon Lee Profile

Esther Seoyeon Lee

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Proceedings Article | 4 April 2022 Presentation + Paper

Association between DCE MRI background parenchymal enhancement and mammographic texture features

Natalie Baughan, Lindsay Douglas, Maya Ballard, Esther Seoyeon Lee, Alexandra Edwards, Li Lan, Hui Li, Maryellen Giger

Proceedings Volume 12033, 120330S (2022) https://doi.org/10.1117/12.2613043

KEYWORDS: Mammography, Magnetic resonance imaging, Breast, Breast cancer, Breast imaging, Statistical analysis, Radiology, Cancer, Image analysis

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Background parenchymal enhancement (BPE) from dynamic contrast-enhanced (DCE) MRI exams has been found in recent studies to be an indicator of breast cancer risk. To further understand the current framework of metrics to evaluate risk, we evaluated the association between human-engineered radiomic texture features calculated from mammograms and radiologist BPE ratings from corresponding DCE-MRI exams. This study included 100 unilaterally affected patients which had undergone both mammographic and DCE-MR breast imaging. BPE levels were provided from the radiology report and included four categories with the following numbers of patients: 14 minimal, 56 mild, 27 moderate, and 3 marked. All mammograms (12-bit quantization and 70-micron pixels) had been acquired with a Hologic Lorad Selenia system and were retrospectively collected under an IRB-approved protocol. A 512x512 pixel region of interest was selected in the central region behind the nipple on the mammogram of the unaffected breast and texture analysis was conducted to extract 45 features. Kendall’s tau-b and a two-sample t-test were used to evaluate relationships between mammographic texture and MRI BPE levels in five selected radiomic features. BPE categories were grouped into low (minimal/mild) and high (moderate/marked) for the t-test. Kendall test results indicated statistically significant correlations in all selected texture features after Holm-Bonferroni multiple comparisons correction. Two-sample t-test results found statistically significant differences between the high and low BPE categories for the selected texture feature of GLCM Sum Variance after Holm-Bonferroni multiple comparisons correction. These results indicate a significant association between coarse, low spatial frequency mammographic patterns and increased BPE.

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