||5R01CA207084-02 Interpret this number
||Mayo Clinic Rochester
||Radiomic Phenotypes of Breast Parenchyma and Association with Breast Cancer Risk and Detection
The `intrinsic' heterogeneity of breast tissue, reflected in texture and spatial composition on the mammogram,
may provide independent but complementary information to breast density for the assessment of both risk of
breast cancer (BC) and masking that can lead to a missed BC on screening mammography. This may be
especially important for the 40-50% of women with dense breasts who need improved risk stratification. We
have developed automated methods to quantitatively measure parenchymal complexity features from full
field digital mammograms (FFDM) using an innovative lattice-based approach to comprehensively characterize
parenchymal tissue heterogeneity on the mammogram. Using unsupervised clustering applied to features
measured from 2000 screen-FFDM, we found evidence for four reproducible `intrinsic' parenchymal complexity
phenotypes that independently contributed to BC risk, accounting for breast density. In this proposal, we will
expand this set of parenchymal features, classify and validate parenchymal phenotypes generalizable to
multiple racial/ethnic groups, and examine their association with BC risk and masking. In AIM1, we will
characterize and validate parenchymal complexity phenotypes reflecting the `intrinsic' heterogeneity of
the breast parenchyma. We will use established automated algorithms to measure features representing
statistical and structural properties of parenchymal heterogeneity on 36,000 screen-FFDM sampled from three
large multi-ethnic mammography cohorts. We will use hierarchical clustering methods, and a split-sample
approach, to first classify, and then independently validate a robust set of distinct parenchymal phenotypes
among all breast density categories and specifically for dense breasts. In AIM 2, we will examine the
association of parenchymal complexity phenotypes with risk for invasive BC. We will measure these
parenchymal features on screen-FFDM performed within five years prior to diagnosis from 3817 incident
invasive cancer cases and 7634 matched controls, and classify them into the parenchymal phenotypes from
Aim 1. We will examine their association with BC (both across all levels of density and dense breasts only)
adjusting for established risk factors and breast density. Finally, in AIM 3, we will examine the contribution
of parenchymal complexity phenotypes to masking invasive BC. We will examine whether parenchymal
phenotypes are associated with interval vs. screen-detected cancers, compared to true-negative controls,
using the case-control study in AIM 2. AIMS 1 and 2 will also be tested within a subset of women with available
digital breast tomosynthesis (DBT) exams (N=300 invasive BC), to inform the translation of our results to the
emerging DBT technology. Our proposal capitalizes on experienced investigators, productive collaborations,
novel algorithms, and established, well-characterized cohorts and will elucidate novel parenchymal phenotypes
that can improve our ability to define subsets of women at differential BC risk and increased risk of missed BC.
Our study will ultimately pave the way for more effective, tailored BC screening and prevention approaches.
Three-Dimensional Whole Breast Segmentation in Sagittal and Axial Breast MRI With Dense Depth Field Modeling and Localized Self-Adaptation for Chest-Wall Line Detection.
, Weinstein S.
, Hsieh M.K.
, Pantalone L.
, Kontos D.
IEEE transactions on bio-medical engineering, 2019 Jun; 66(6), p. 1567-1579.
Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment.
, Winham S.J.
, Oustimov A.
, Pantalone L.
, Hsieh M.K.
, Gastounioti A.
, Whaley D.H.
, Hruska C.B.
, Kerlikowske K.
, Brandt K.
, et al.
Radiology, 2019 Jan; 290(1), p. 41-49.