Robust evaluation of tissue-specific radiomic features for classifying breast tissue density grades

Breast 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.

We 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 ( $n_I=651$ , $n_{\mathrm{II}}=100$ ). 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.

LR classifiers yielded cross-validated areas under the receiver operating characteristic (AUCs) per density grade of [ $A$ : $0.909\pm 0.032$ , $B$ : $0.858\pm 0.027$ , $C$ : $0.927\pm 0.013$ , $D$ : $0.890\pm 0.089$ ] and an AUC of $0.936\pm 0.016$ 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.

Our 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.