Breast density analysis of digital breast tomosynthesis.

Mammography shifted to digital breast tomosynthesis (DBT) in the US. An automated percentage of breast density (PD) technique designed for two-dimensional (2D) applications was evaluated with DBT using several breast cancer risk prediction measures: normalized-volumetric; dense volume; applied to the volume slices and averaged (slice-mean); and applied to synthetic 2D images. Volumetric measures were derived theoretically.

Techniques to produce and evaluate realistic multivariate synthetic data.

Data modeling requires a sufficient sample size for reproducibility. A small sample size can inhibit model evaluation. A synthetic data generation technique addressing this small sample size problem is evaluated: from the space of arbitrarily distributed samples, a subgroup (class) has a latent multivariate normal characteristic; synthetic data can be generated from this class with univariate kernel density estimation (KDE); and synthetic samples are statistically like their respective samples.

Breast Density Analysis Using Digital Breast Tomosynthesis.

We evaluated an automated percentage of breast density (BD) technique (PD) with digital breast tomosynthesis (DBT) data. The approach is based on the wavelet expansion followed by analyzing signal dependent noise. Several measures were investigated as risk factors: normalized volumetric; total dense volume; average of the DBT slices (slice-mean); a two-dimensional (2D) metric applied to the synthetic images; and the mean and standard deviations of the pixel values.

Spatial Correlation and Breast Cancer Risk.

We present a novel method for evaluating local spatial correlation structure in two-dimensional (2D) mammograms and evaluate its capability for risk prediction as one possible application. Two matched case-control studies were analyzed. Study 1 included women (N = 588 pairs) with mammograms acquired with either Hologic Selenia full field digital mammography (FFDM) units or Hologic Dimensions digital breast tomosynthesis units.

Technical challenges in generalizing calibration techniques for breast density measurements.

Purpose: We are developing a calibration methodology for full-field digital mammography (FFDM). Calibration compensates for image acquisition technique influences on the pixel representation, ideally producing improved inter-image breast density estimates. This approach relies on establishing references with rigid breast tissue-equivalent phantoms (BTEs) and requires an accurate estimate of the compressed breast thickness because the system readout is nominal.

Generalized breast density metrics.

Mammograms represent data that can inform future risk of breast cancer. Data from two case-control study populations were analyzed. Population 1 included women (N  =  180 age matched case-control pairs) with mammograms acquired with one indirect x-ray conversion mammography unit.

Automated Percentage of Breast Density Measurements for Full-field Digital Mammography Applications.

Rationale And Objectives: Increased mammographic breast density is a significant risk factor for breast cancer. A reproducible, accurate, and automated breast density measurement is required for full-field digital mammography (FFDM) to support clinical applications. We evaluated a novel automated percentage of breast density measure (PDa) and made comparisons with the standard operator-assisted measure (PD) using FFDM data.

A comparison of calibration data from full field digital mammography units for breast density measurements.

Background: Breast density is a significant breast cancer risk factor measured from mammograms. The most appropriate method for measuring breast density for risk applications is still under investigation. Calibration standardizes mammograms to account for acquisition technique differences prior to making breast density measurements.

Full field digital mammography and breast density: comparison of calibrated and noncalibrated measurements.

Rationale And Objectives: Mammographic breast density is an important and widely accepted risk factor for breast cancer. A statement about breast density in the mammographic report is becoming a requirement in many States. However, there is significant inter-observer variation between radiologists in their interpretation of breast density.