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.
KEYWORDS: Image segmentation, Breast, Tissues, Cancer, Digital breast tomosynthesis, Computer simulations, Signal to noise ratio, Signal attenuation, Motion models, X-rays
Our lab has built a next-generation tomosynthesis (NGT) system utilizing scanning motions with more degrees of freedom than clinical digital breast tomosynthesis systems. We are working toward designing scanning motions that are customized around the locations of suspicious findings. The first step in this direction is to demonstrate that these findings can be detected with a single projection image, which can guide the remainder of the scan. This paper develops an automated method to identify findings that are prone to be masked. Perlin-noise phantoms and synthetic lesions were used to simulate masked cancers. NGT projections of phantoms were simulated using ray-tracing software. The risk of masking cancers was mapped using the ground-truth labels of phantoms. The phantom labels were used to denote regions of low and high risk of masking suspicious findings. A U-Net model was trained for multiclass segmentation of phantom images. Model performance was quantified with a receiver operating characteristic (ROC) curve using area under the curve (AUC). The ROC operating point was defined to be the point closest to the upper left corner of ROC space. The output predictions showed an accurate segmentation of tissue predominantly adipose (mean AUC of 0.93). The predictions also indicate regions of suspicious findings; for the highest risk class, mean AUC was 0.89, with a true positive rate of 0.80 and a true negative rate of 0.83 at the operating point. In summary, this paper demonstrates with virtual phantoms that a single projection can indeed be used to identify suspicious findings.
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