Multi-Parametric Super-Resolution Ultrasound for Evaluating Breast Cancer Characteristics: Improved Diagnostic...

To investigate the diagnostic superiority of super-resolution ultrasound (SRUS) imaging over conventional contrast-enhanced ultrasound (CEUS) in differentiating breast masses, and to evaluate its clinical potential as a non-invasive tool for predicting histological grade, molecular subtypes and immune microenvironment characteristics.

A prospective cohort of 84 patients (25 benign, 59 malignant) underwent pre-biopsy assessments of CEUS and SRUS parameters. CEUS evaluated qualitative features (shape after enhancement, feeding vessel) and quantitative indices (area under the curve [AUC], peak intensity). SRUS quantified microvascular architecture via Vessel Ratio (area of vascular lumen), Complexity Level (vascular branching complexity), MeanDen (Mean Density, average microbubble signal density) and MeanVel (Mean Velocity, microbubble flow speed). Receiver operating characteristic curve analysis compared diagnostic accuracies, while correlations with histological grade (I-III), molecular subtypes (Luminal A, Luminal B, human epidermal growth factor receptor 2-enriched and triple-negative breast cancer) and CD3⁺ T-cell infiltration were evaluated.

Malignant tumors showed distinct SRUS profiles: higher Vessel Ratio (29.8% vs. 13.6%, p < 0.001), MeanDen (12.46 ± 6.11 vs. 7.47 ± 2.60, p = 0.011) and MeanVel (14.19 ± 4.62 vs. 9.71 ± 2.96, p = 0.038) compared to benign lesions. SRUS alone achieved an AUC of 0.902 for malignancy discrimination, outperforming CEUS (AUC = 0.866), with combined CEUS + SRUS improving AUC to 0.939 (p < 0.05 for all comparisons). MeanDen inversely correlated with histological grade (Grade III < I/II, p < 0.05), while Complexity Level and MeanDen distinguished molecular subtypes. CD3⁺ infiltration was positively associated with higher Complexity Level (p < 0.05).

SRUS provides multi-dimensional characterization of breast masses and performs better than CEUS. Its parameters enable non-invasive prediction of tumor malignancy, molecular phenotypes and immune infiltration, positioning SRUS as a promising tool for pre-operative risk stratification, personalized treatment guidance and therapeutic monitoring in clinical oncology.