Date of Award

Summer 8-18-2023

Level of Access Assigned by Author

Open-Access Dissertation

Degree Name

Doctor of Philosophy (PhD)


Interdisciplinary Program


Andre Khalil

Second Committee Member

David Bradley

Third Committee Member

Amy Harrow

Additional Committee Members

Karissa Tilbury

Christine Lary


Breast density is a known risk factor for breast cancer. However, there has been limited research on potential subtypes of mammographic dense breast tissue to identify areas of active dense tissue that is structurally reorganizing and links to cancer dynamics, versus areas of passive dense tissue which remains organized. We hypothesize that the amounts of subtypes of mammographic dense tissue and the associated rate of change through time could provide insights into breast cancer risk. A retrospective study was conducted to investigate breast cancer using longitudinal screening mammograms and accompanying pathology reports collected in 2015. Patients were matched by age based on the time of diagnosis, or the last visit for controls. The study comprised 25 pathology-confirmed benign cases, 21 pathology-confirmed cancer cases, and 24 controls. The standard bilateral mammographic images were divided into overlapping subimages using a sliding window approach to identify subtypes of breast tissue, i.e, fatty, active dense, and passive dense tissue. Each subimage was assigned a Hurst exponent (H) using the 2D Wavelet-Transform Modulus Maxima Method to classify each subregion as either fatty tissue (H<0.45), active dense tissue (H~0.50) or passive dense tissue (H>0.55). The area (cm2) and relative amounts of each tissue type were calculated for each mammographic view. A linear mixed effects model was fitted for both the area and relative amounts for each image with time as an interaction term. This study found that the amount of active and passive dense tissue, and the rate of change in active dense tissue were associated with developing breast cancer. Incorporating measurable changes in mammographic tissue in risk models could assist with identifying women who could benefit from enhanced surveillance to prevent invasive breast cancer.

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