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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Spatial Correlation and Breast Cancer Risk.

Erin E E Fowler1, Cassandra Hathaway1, Fabryann Tillman1

  • 1Cancer Epidemiology Department, MCC, Moffitt Cancer Center & Research Institute, 12902 Bruce B. Downs Blvd, Tampa FL, United States of America, 33612 (MCC).

Biomedical Physics & Engineering Express
|December 11, 2020
PubMed
Summary
This summary is machine-generated.

A new method analyzing spatial patterns in mammograms shows promise for predicting breast cancer risk. This technique identified significant associations with breast cancer in two separate studies, suggesting broader applications in medical imaging.

Keywords:
Fourier analysisbreast cancer riskbreast densitycalibrationmammography

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Area of Science:

  • Radiology and Medical Imaging
  • Biostatistics and Epidemiology
  • Biomedical Engineering

Background:

  • Accurate breast cancer risk prediction is crucial for early detection and prevention strategies.
  • Evaluating local spatial correlation in mammographic images offers a potential new avenue for risk assessment.
  • Existing methods may not fully capture subtle textural patterns indicative of malignancy risk.

Purpose of the Study:

  • To introduce and validate a novel method for assessing local spatial correlation in 2D mammograms.
  • To evaluate the capability of this novel method for predicting breast cancer risk.
  • To explore the generalizability of the developed correlation metrics across different mammography units and studies.

Main Methods:

  • A novel method using Fourier analysis to determine local autocorrelation functions in 2D mammograms was developed.
  • Two matched case-control studies (N=588 pairs and N=180 pairs) were analyzed using mammograms from Hologic and GE units.
  • Key metrics, mean (mn+1) and standard deviation (sn+1) of local correlation differences, were calculated and tested for association with breast cancer risk using conditional logistic regression.

Main Results:

  • Two selected correlation metrics, m75 and s25, demonstrated significant associations with breast cancer in Study 1 (OR=1.45 and OR=1.30, respectively).
  • These significant associations were replicated in Study 2, with m75 showing an OR of 1.49 and s25 showing an OR of 1.34.
  • The developed novel correlation metrics consistently predicted breast cancer risk across different mammography systems.

Conclusions:

  • The novel correlation metrics presented are effective in identifying significant associations with breast cancer risk.
  • This spatial correlation analysis method shows potential as a valuable tool for breast cancer risk prediction.
  • The approach is generalizable and may find applications in other areas of medical imaging beyond mammography.