Biology subjects

Fowler, E.

Publications and source records attributed to Fowler, E..

3 recordsLinked to original sources

bioRxiv2026

Comparison of Total X-Ray Energy Absorbed by Fibroglandular Tissue and its Mass Derived from Full Field Digital Mammography and Digital Breast Tomosynthesis

This study investigated whether total x-ray energy absorbed by fibroglandular tissue and fibroglandular tissue mass derived from raw and clinical full field digital mammography (FFDM) images corresponded with the respective quantities derived from digital breast tomosynthesis (DBT). A total of 840 paired FFDM and DBT observations were analyzed separately for the craniocaudal (CC) and mediolateral oblique (MLO) orientations. Total absorbed x-ray energy and fibroglandular tissue mass were calculated using an automated breast density detection method in conjunction with the recorded average glandular dose. Relationships between FFDM and DBT quantities were evaluated in the log-log domain using linear and quadratic models, with model performance assessed by R2, adjusted R2, mean squared error (MSE), chi-square goodness-of-fit tests, and residual analysis. Strong associations were observed between FFDM and respective DBT quantities. For raw FFDM, linear energy models produced R2 values of 0.734 and 0.901 for the CC and MLO views, respectively, while the corresponding mass models produced R2 values of 0.634 and 0.870. Inclusion of a quadratic term improved model performance modestly. For clinical FFDM, linear energy models produced R2 values of 0.854 and 0.919, while the corresponding mass models produced R2 values of 0.721 and 0.812. The first-order coefficients for the clinical-image energy models were approximately unity (0.99-1.05), and inclusion of the quadratic term produced negligible changes in model performance and residual distributions. The goodness-of-fit tests did not reject adequate fit for any model. Clinical FFDM images can therefore provide estimates of total absorbed x-ray energy and fibroglandular tissue mass that are strongly associated with corresponding quantities derived from DBT. The approximately unit first-order coefficients and minimal benefit from quadratic modeling support a simple linear log-log calibration for clinical FFDM. This approach may facilitate combining FFDM and DBT based data in larger retrospective and longitudinal studies of cumulative mammographic x-ray exposure. These findings demonstrate proof-of-concept internal consistency but do not establish absolute dosimetric accuracy.

bioinformatics↗

bioRxiv2026

Synthetic Data Generation and Nonparametric Techniques for Assessing Multivariate Similarity to Address Small-Sample Size Challenges

Data modeling in biomedical research often operates in the small-sample regime, where the number of observations is small relative to the data dimensionality; the detrimental effects of limited sample sizes are well documented in cancer studies. Synthetic data offers a potential solution to data shortfalls provided that the data generated is an adequate facsimile of the underlying distribution; the adequacy of such synthetic data remains an open-ended problem. In this work, we evaluate a synthetic generator proposed previously. The generator applies a series of transformations to the observed data to accommodate the small-sample size resulting in an uncoupled representation, where uncorrelated marginal distributions are modeled with optimized univariate kernel density estimation. In this report, (1) we develop a nonparametric method for assessing multivariate similarity based on the Cramer-Wold theorem and random projection testing, (2) investigate when the absence of bivariate correlation approximates independence in a non-normal setting, and (3) evaluate artifacts induced by data compression. The presentation is primarily methodological; low-dimensional data were used so each stage of the generation process could be analyzed explicitly. A formal testing framework was developed by comparing random projection level outcomes with a two-sample test, modeling these outcomes as Bernoulli trials, aggregating replicate outcomes within each projection direction, and pooling outcomes across many directions, yielding a scalable standardized normal test-statistic. The key innovation was decoupling the two-sample test significance level from that governing finalized normal inference. We showed the same projection framework also evaluates the full multivariate covariance structure. The generator produced high-fidelity multivariate synthetic data when the bivariate correlation approximates independence in the non-normal setting; in highly compressed data, residual modes were best modeled as normally distributed regardless of their intrinsic distributional form. Ongoing work includes applying these methods to higher-dimensional, diverse data.

bioinformatics↗

bioRxiv2025

Advancements in an Automated Breast Density Detection Technique for Breast Cancer Risk Prediction: a Synthetic Signal-dependent Noise Construct

Breast density is an important breast cancer risk factor estimated from mammograms and useful for breast cancer risk prediction. We describe a new formulation built on the backbone of an established automated percentage of breast density detection method. This framework relies on signal-dependent noise (SDN), characterized by a statistical dependence between the variance and mean signal. Variations in this dependency due to different image data representations cause degradation in the algorithms performance; the current work addresses this problem by synthesizing a stochastic process conditioned on a given mammogram instead of analyzing the mammogram directly. Image data used in the analysis was derived from three breast cancer case-control studies employing different mammographic technologies including full field digital mammography (both raw and clinical images) and digital breast tomosynthesis. The new formulation produced significant odds ratios across all image data representations due to these methodological advancements: (1) synthesis of SDN to an optimal quadratic structure given an arbitrary image; and (2) ensemble averaging over a given image, boosting the signal. We also demonstrate methods to standardize and combine measurements from different technologies using a probability density transformation technique. This automated technique can be applied to images from different technologies with minimal adjustment, thereby making it suitable for both research and clinical applications.

bioengineering↗