title: Mammographic evidence of microenvironment changes in tumorous breasts.
abstract: OBJECTIVE
  The microenvironment of breast tumors plays a critical role in tumorigenesis. As long as the structural integrity of the microenvironment is upheld, the tumor is suppressed. If tissue structure is lost through disruptions in the normal cell cycle, the microenvironment may act as a tumor promoter. Therefore, the properties that distinguish between healthy and tumorous tissues may not be solely in the tumor characteristics but rather in surrounding non-tumor tissue. The goal of this paper was to show preliminary evidence that tissue disruption and loss of homeostasis in breast tissue microenvironment and breast bilateral asymmetry can be quantitatively and objectively assessed from mammography via a localized, wavelet-based analysis of the whole breast.
  
  
  METHODS
  A wavelet-based multifractal formalism called the 2D Wavelet Transform Modulus Maxima (WTMM) method was used to quantitate density fluctuations from mammographic breast tissue via the Hurst exponent (H). Each entire mammogram was cut in hundreds of 360 × 360 pixel subregions in a gridding scheme of overlapping sliding windows, with each window boundary separated by 32 pixels. The 2D WTMM method was applied to each subregion individually. A data mining approach was set up to determine which metrics best discriminated between normal vs. cancer cases. These same metrics were then used, without modification, to discriminate between normal vs. benign and benign vs. cancer cases.
  
  
  RESULTS
  The density fluctuations in healthy mammographic breast tissue are either monofractal anti-correlated (H < 1/2) for fatty tissue or monofractal long-range correlated (H>1/2) for dense tissue. However, tissue regions with H~1/2, as well as left vs. right breast asymetries, were found preferably in tumorous (benign or cancer) breasts vs. normal breasts, as quantified via a combination metric yielding a P-value ~ 0.0006. No metric considered showed significant differences between cancer vs. benign breasts.
  
  
  CONCLUSIONS
  Since mammographic tissue regions associated with uncorrelated (H~1/2) density fluctuations were predominantly in tumorous breasts, and since the underlying physical processes associated with a H~1/2 signature are those of randomness, lack of spatial correlation, and free diffusion, it is hypothesized that this signature is also associated with tissue disruption and loss of tissue homeostasis.
authors.affiliation: CompuMAINE Laboratory, Department of Mathematics & Statistics, University of Maine, Orono, ME, 04469, USA.
authors.forename: Zach
authors.initials: Z
authors.lastname: Marin
authors.affiliation: CompuMAINE Laboratory, Department of Mathematics & Statistics, University of Maine, Orono, ME, 04469, USA.
authors.forename: Kendra A
authors.initials: KA
authors.lastname: Batchelder
authors.affiliation: CompuMAINE Laboratory, Department of Mathematics & Statistics, University of Maine, Orono, ME, 04469, USA.
authors.forename: Brian C
authors.initials: BC
authors.lastname: Toner
authors.affiliation: CompuMAINE Laboratory, Department of Mathematics & Statistics, University of Maine, Orono, ME, 04469, USA.
authors.forename: Lyne
authors.initials: L
authors.lastname: Guimond
authors.affiliation: Laboratory of Physical Foundation of Strength, Institute of Continuous Media Mechanics UB RAS, Perm, Russia.
authors.forename: Evgeniya
authors.initials: E
authors.lastname: Gerasimova-Chechkina
authors.affiliation: Spectrum Medical Group, Eastern Maine Medical Center, Bangor, ME, 04401, USA.
authors.forename: Amy R
authors.initials: AR
authors.lastname: Harrow
authors.affiliation: LOMA, Universite de Bordeaux, CNRS, UMR 5798, 33405, Talence, France.
authors.forename: Alain
authors.initials: A
authors.lastname: Arneodo
authors.affiliation: CompuMAINE Laboratory, Department of Mathematics & Statistics, University of Maine, Orono, ME, 04469, USA.
authors.forename: Andre
authors.initials: A
authors.lastname: Khalil
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001940:Breast / Q000000981:diagnostic imaging / Q000473:pathology; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D006706:Homeostasis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008327:Mammography; D008875:Middle Aged; D059016:Tumor Microenvironment; D058067:Wavelet Analysis
keywords: Hurst exponent; radiomics; tissue disruption; tissue homeostasis; wavelets