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.
Digital mammography (DM) and digital breast tomosynthesis, the gold standards for breast cancer screening, requires correct breast positioning to ensure accuracy. Improper positioning can result in missed cancers, or can lead to additional imaging. We propose an automated deep learning (DL) segmentation approach to perform multi-class identification of regions of interest (ROI) commonly used for identification of poor positioning in mediolateral oblique (MLO) breast views. We hypothesize that by leveraging the capabilities of DL through the use of the well-founded U-Net model architecture, multi-class DL-based segmentation approaches can accurately identify air, parenchyma, pectoralis, and nipple locations within MLO images. In this study, we employed model hyperparameter searches to determine optimal model parameters for our proposed DL architecture, including the optimal loss function configuration; our best model achieved an average Sørensen-Dice coefficient of 0.919 ± 0.061 on the held-out test set. We identified high levels of localization performance in the nipple ROI. We believe our proposed segmentation model can be a foundational step in further mammogram analysis, such as for breast positioning and localized image processing tools.
Noiseless digital mammograms (DM) are unobtainable in clinical screening environments, limiting the development of deep learning-based (DL) denoising applications. Virtual clinical trials (VCTs) allow the precise simulation of noise levels in DM images for controlled training of DL models. We evaluated a set of DL denoising models, trained using VCT data, that showcases the trade-offs between denoising strength and fine structure preservation. Our results show that metrics, such as peak signal-to-noise ratio (PSNR), are improved with the use of our trained residual convolutional neural network. This quantifiable improvement indicates that our proposed DL methodology can accurately denoise simulated mammograms.
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