Field cancerization in the understanding of parenchymal analysis of mammograms for breast cancer risk assessment

Med Hypotheses. 2020 Mar:136:109511. doi: 10.1016/j.mehy.2019.109511. Epub 2019 Nov 27.

Abstract

In recent years, mammographic image analysis has shown great potential for breast cancer risk assessment. The aim of risk assessment is to predict how likely a woman is to develop breast cancer in the future. Several studies suggest that computerized parenchymal analysis of mammograms can be utilized as an independent imaging biomarker of breast cancer. Parenchymal analysis consists of the quantitative assessment of visual texture patterns in mammograms to infer the level of risk. In spite of substantial evidence of the association between parenchymal patterns and breast cancer risk, its biological foundations remain poorly understood. In this work, we draw a hypothesis that links the field cancerization (FC) with breast cancer risk assessment based on the parenchymal analysis. In the literature, the FC is interpreted as a biochemical anomaly amplification in otherwise healthy cells due to the effect of pre-cancerous transformed cells in surrounding regions. Our hypothesis is that these biochemical anomaly amplifications change the cellular micro-environment which, in turn, alter tissue responses to X-ray radiation. As a result, it is reasonable to think that these changes influence the interaction of X-rays with parenchymal - the functional - breast tissue thus enabling cancer prediction by analyzing X-ray images of the breast. We believe that our hypothesis provides an actionable explanation as to how computerized parenchymal analysis of apparently normal mammograms can be successfully utilized for the stratification of breast cancer risk.

Keywords: Breast cancer; Field cancerization; Mammography; Parenchymal analysis; Risk assessment.

MeSH terms

  • Breast Neoplasms / diagnostic imaging*
  • Cell Transformation, Neoplastic
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Mammography*
  • ROC Curve
  • Risk Assessment / methods*
  • Tumor Microenvironment
  • X-Rays