Spatial Relations of Mammographic Density Regions and their Association with Breast Cancer Risk | Zendy

Maya Alsheh Ali | Zendy; Mickaël Garnier | Zendy; Keith Humphreys | Zendy

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Spatial Relations of Mammographic Density Regions and their Association with Breast Cancer Risk

Author(s) -

Maya Alsheh Ali,

Mickaël Garnier,

Keith Humphreys

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.07.019

Subject(s) - mammographic density , breast cancer , logistic regression , principal component analysis , histogram , computer science , body mass index , mammography , spatial analysis , association (psychology) , cancer , spatial relation , pattern recognition (psychology) , artificial intelligence , statistics , medicine , image (mathematics) , machine learning , mathematics , philosophy , epistemology

We present a new approach for characterising the shape and the spatial relationships of different categories of density in mammograms. Descriptions of regions are encoded using a forces histogram method and across-image variation is captured using functional principal component analysis. We evaluate the association of the features with breast cancer based on a pilot case- control study using logistic regression with percent density, age, and body mass index included as adjustment variables. The spatial relations were significantly associated with breast cancer status (p= 0.009). Our approach can provide insights into the role of different density regions in the development of breast cancer

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