Andrew Douglas Arnold Maidment

Breast density impacts cancer detection by masking tumors within fibroglandular tissue and there are disparities in screening outcomes across racial groups. However, it remains unclear whether these differences reflect inherent tissue characteristics or systemic bias. To isolate the effect of breast density on lesion detectability across racial subgroups, we conducted a retrospective case-control study using raw tomosynthesis projections from 902 women (453 cases, 451 matched controls) across BI-RADS density categories and self-reported race. Identical in-silico spiculated masses (8–15 mm) and microcalcification clusters (10–14 mm) were inserted into the projections using a calibrated lesion model. Images were reconstructed in the same manner to avoid proprietary processing in lesion detection. Lesion detectability was assessed with Channelized Hotelling Observers. Regression and causal mediation analyses examined the relationships between race, density, and detectability. As result, detectability decreased with increasing density; for masses, the area under the receiver operating characteristic curve (ROC AUC) reduced significantly from 0.93 to 0.85 (BIRADS A to D), whereas for microcalcifications AUC decreased from 0.85 to 0.78 across the same density range. Discrimination remained higher for masses than calcifications (AUC=0.89 vs. AUC=0.82). Stratified analyses showed slightly higher detectability in Non-Hispanic Black women compared with Non-Hispanic White and Asian American women, largely reflecting differences in density. Mediation analysis revealed that breast density accounted for 38–55% of the observed race-associated detectability differences. Mediation analyses have shown that density is the dominant factor in detectability. These findings support the development of calibrated detection models and personalized screening strategies that account for breast density.

The editorial introduces the JMI Special Issue on Advances in Breast Imaging, reflecting on the current forefront of breast imaging research.

PurposeWe aim to assess the perceptual tasks in which convolutional neural networks (CNNs) might be better tools than commonly used linear model observers (LMOs) to evaluate medical image quality.ApproachWe compared the LMOs (channelized Hotelling [CHO] and frequency convolution channels observers [FCO]) and CNN detection accuracies for tasks with a few possible signal locations (location known exactly) and for the search for mass and microcalcification signals embedded in 2D/3D breast tomosynthesis phantoms. We also compared the LMOs and CNN accuracies to those of radiologists in the search tasks. We analyzed radiologists’ eye position to assess whether they fixate longer at locations considered suspicious by the LMOs or those by the CNN.ResultsLMOs resulted in similar detection accuracies [area under the receiver operating characteristic curve (AUC)] to the CNN for tasks with up to 100 signal locations but lower accuracies in the search task for microcalcification and mass 3D images. Radiologists’ AUC was significantly higher (p<1e−4) than that of LMOs for the microcalcification 2D search (CHO, FCO) and 3D mass search (p<0.05, CHO) but was not higher than the CNN’s AUC. For both signal types, radiologists fixated longer on the locations of the highest response scores of the CNN than those of the LMOs but only reached statistical significance for the mass (masses: p=0.009 versus CHO and p=0.004 versus FCO)ConclusionWe show that CNNs are a more suitable model observer for search tasks. Like radiologists but not traditional LMOs, CNNs can discount false positives arising from anatomical backgrounds.

Three-dimensional radiomic analysis of digital breast tomosynthesis outperformed two-dimensional measures in breast cancer risk assessment.

PurposeBreast cancer risk depends on an accurate assessment of breast density due to lesion masking. Although governed by standardized guidelines, radiologist assessment of breast density is still highly variable. Automated breast density assessment tools leverage deep learning but are limited by model robustness and interpretability.ApproachWe assessed the robustness of a feature selection methodology (RFE-SHAP) for classifying breast density grades using tissue-specific radiomic features extracted from raw central projections of digital breast tomosynthesis screenings (nI=651, nII=100). RFE-SHAP leverages traditional and explainable AI methods to identify highly predictive and influential features. A simple logistic regression (LR) classifier was used to assess classification performance, and unsupervised clustering was employed to investigate the intrinsic separability of density grade classes.ResultsLR classifiers yielded cross-validated areas under the receiver operating characteristic (AUCs) per density grade of [A: 0.909±0.032, B: 0.858±0.027, C: 0.927±0.013, D: 0.890±0.089] and an AUC of 0.936±0.016 for classifying patients as nondense or dense. In external validation, we observed per density grade AUCs of [A: 0.880, B: 0.779, C: 0.878, D: 0.673] and nondense/dense AUC of 0.823. Unsupervised clustering highlighted the ability of these features to characterize different density grades.ConclusionsOur RFE-SHAP feature selection methodology for classifying breast tissue density generalized well to validation datasets after accounting for natural class imbalance, and the identified radiomic features properly captured the progression of density grades. Our results potentiate future research into correlating selected radiomic features with clinical descriptors of breast tissue density.

Radiomic parenchymal phenotypes on mammograms were used to predict breast cancer risk among both Black and White women, notably for false-negative findings and symptomatic interval cancer.

JMI guest editors present a diverse collection of research and perspectives in a special issue celebrating the past, present, and future of digital tomosynthesisan imaging modality that continues to grow in both clinical relevance and technical sophistication.

PurposeTomosynthesis is a limited-angle multi-projection method that was conceived to address a significant limitation of conventional single-projection x-ray imaging: the overlap of structures in an image. We trace the historical evolution of tomosynthesis.ApproachRelevant papers are discussed including descriptions of technical advances and clinical applications.ResultsWe start with the invention of tomosynthesis by Ziedses des Plantes in the Netherlands and Kaufman in the United States in the mid-1930s and end with our predictions of future technical advances. Some of the other topics that are covered include a respiratory-gated chest tomosynthesis system of the late 1930s, film-based systems of the 1960s and 1970s, coded aperture tomosynthesis, fluoroscopy tomosynthesis, digital detector-based tomosynthesis for imaging the breast and body, orthopedic, dental and radiotherapy applications, optimization of acquisition parameters for breast and body tomosynthesis, reconstruction methods, characteristics of present-day tomosynthesis systems, x-ray tubes, and promising new applications including contrast-enhanced and multimodal breast imaging systems.ConclusionTomosynthesis has had an exciting history that continues today. This should serve as a foundation for other papers in the special issue “Celebrating Digital Tomosynthesis: Past, Present and Future” in the Journal of Medical Imaging.

In this study, we investigate the performance of advanced 2D acquisition geometries--Pentagon and T-shaped--in digital breast tomosynthesis (DBT) and compare them against the conventional 1D geometry. Unlike the conventional approach, our proposed 2D geometries also incorporate anterior projections away from the chest wall. Implemented on the Next-Generation Tomosynthesis (NGT) prototype developed by X-ray Physics Lab (XPL), UPenn, we utilized various phantoms to compare three geometries: a Defrise slab phantom with alternating plastic slabs to study low-frequency modulation; a Checkerboard breast phantom (a 2D adaptation of the Defrise phantom design) to study the ability to reconstruct the fine features of the checkerboard squares; and the 360° Star-pattern phantom to assess aliasing and compute the Fourier-spectral distortion (FSD) metric that assesses spectral leakage and the contrast transfer function. We find that both Pentagon and T-shaped scans provide greater modulation amplitude of the Defrise phantom slabs and better resolve the squares of the Checkerboard phantom against the conventional scan. Notably, the Pentagon geometry exhibited a significant reduction in aliasing of spatial frequencies oriented in the right-left (RL) medio-lateral direction, which was corroborated by a near complete elimination of spectral leakage in the FSD plot. Conversely T-shaped scan redistributes the aliasing between both posteroanterior (PA) and RL directions thus maintaining non-inferiority against the conventional scan which is predominantly affected by PA aliasing. The results of this study underscore the potential of incorporating advanced 2D geometries in DBT systems, offering marked improvements in imaging performance over the conventional 1D approach.

Breast density is an important consideration for breast cancer screening, where the amount of fibroglandular tissue in the breast can mask the detection of cancers. BI-RADS density grade estimates can result in high variability, prompting the need for an objective and reproducible assessment of breast density and tissue complexity. In this study, we investigate the utility of radiomic features to quantify texture and shape characteristics of tissue-specific regions of interest. Using Explainable AI (XAI), we identify key features for distinguishing breast density grade by computing each feature’s SHapley Additive exPlanations (SHAP) value. SHAP values measure a feature’s importance on the classifier’s prediction; the top SHAP value features from each density grade are selected as inputs to our classifier model. These features also identify relationships with clinical knowledge of breast cancer pathophysiology. Logistic regression classifiers fit to our radiomic features achieved a mean AUC per density grade class of [A : 0.949±0.055,B : 0.877±0.055,C : 0.884±0.023,D : 0.893±0.076] over nested five-fold cross-validation. Pooled confusion matrices show that class imbalance can affect the proposed method, particularly in density grades A and D. Furthermore, unsupervised clustering using Uniform Manifold Approximation and Projection (UMAP) on our radiomic feature set show inherent separability of the four density grades. The results of our preliminary analysis highlight how clinically interpretable radiomic features show promise as an important tool for breast cancer screening by preserving predictive performance while introducing AI explainability.