Computational growth model of breast microcalcification clusters in simulated mammographic environments

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• 作者：Shayne M. Plourde^a ; ¹ ; Zach Marin^a ; ¹ ; Zachary R. Smith^a ; ^b ; Brian C. Toner^a ; Kendra A. Batchelder^a ; Andre Khalil^a ; ^{andre.khalil@maine.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Breast cancer ; Breast tissue ; Microcalcification ; Fractal ; Model ; Simulation
• 刊名：Computers in Biology and Medicine
• 出版年：2016
• 出版时间：1 September 2016
• 年：2016
• 卷：76
• 期：Complete
• 页码：7-13
• 全文大小：1835 K

文摘

When screening for breast cancer, the radiological interpretation of mammograms is a difficult task, particularly when classifying precancerous growth such as microcalcifications (MCs). Biophysical modeling of benign vs. malignant growth of MCs in simulated mammographic backgrounds may improve characterization of these structures

Methods

A mathematical model based on crystal growth rules for calcium oxide (benign) and hydroxyapatite (malignant) was used in conjunction with simulated mammographic backgrounds, which were generated by fractional Brownian motion of varying roughness and quantified by the Hurst exponent to mimic tissue of varying density. Simulated MC clusters were compared by fractal dimension, average circularity of individual MCs, average number of MCs per cluster, and average cluster area.

Results

Benign and malignant clusters were distinguishable by average circularity, average number of MCs per cluster, and average cluster area with p<0.01 across all Hurst exponent values considered. Clusters were distinguishable by fractal dimension with p<0.05 in low Hurst exponent environments. As the Hurst exponent increased (tissue density increased) benign and malignant MCs became indistinguishable by fractal dimension.

Conclusions

The fractal dimension of MCs changes with breast tissue density, which suggests tissue environment plays a role in regulating MC growth. Benign and malignant MCs are distinguishable in all types of tissue by shape, size, and area, which is consistent with findings in the literature. These results may help to better understand the effects of the tissue environment on tumor progression, and improve classification of MCs in mammograms via computer-aided diagnosis.