Comprehensive Radiomics Analysis to Enhance Breast Lesion Characterization Using QUS Spectral Parametric Imaging

Background/Objectives: We aimed to evaluate the generalizability of QUS spectral parametric imaging radiomics for breast tumor characterization in a large cohort through methodological optimization and rigorous validation, including comprehensive feature extraction and evaluation of multiple classifiers, addressing limitations of previous studies potentially affected by data leakage. Methods: This study included 277 participants (185 malignant cases (median age, 51 years [IQR: 44–63 years]) and 92 benign cases (median age, 46 years [IQR: 38–51 years]) with breast masses, acquired between September 2014 and October 2021. QUS spectroscopic analysis resulted in five maps, from which first-order statistical, various textural, and morphological features were extracted from both the tumor core and a surrounding 5 mm margin. The ground truth label was determined from histopathological analysis. Predictive models were developed to distinguish malignant from benign lesions. Their generalization performance was assessed using hold-out validation. Beyond assessing models’ performance, SHapley Additive exPlanations (SHAP) analysis identified the most influential features to the predictions. Results: 329 radiomics features demonstrated statistically significant differences (p-values < 0.00005). Averaged across 50 partitions, SVM-Linear models produced test performance of 82 ± 8% (CI: 59–97) recall, 79 ± 9% (CI: 50–100) specificity, and 0.87 ± 0.05 (CI: 0.71–0.98) area under the receiver operating characteristic curve (AUROC). The best single partition performance with an SVM-Linear model was 92% recall, 89% specificity, and 0.97 AUROC. Conclusions: This work establishes a performance benchmark for handcrafted radiomic features derived from QUS spectral parametric images in breast lesion characterization, demonstrating its strong generalization and servicing as a reference for future model development.