Objective: Ultrasound Resolution Microscopy (URM) is an emerging technique that provides superior delineation of tumor microvasculature. This prospective study aimed to evaluate the diagnostic value of URM in differentiating benign from malignant breast lesions. Methods: From September 2024 to October 2025, 55 patients with 57 breast masses underwent conventional ultrasound and contrast-enhanced URM. Microvascular parameters were quantitatively analyzed and cross-referenced with histopathology. To mitigate overfitting, LASSO regression was employed to screen 14 URM indices. A combined predictive model integrating core URM features with BI-RADS categorization (dichotomized at 4A) was developed and evaluated using ROC and decision curve analysis (DCA). Results: Thirty-four malignant and 23 benign masses were confirmed. Malignant lesions exhibited comprehensively elevated microvascular abundance and architectural chaos. LASSO regression distilled these features down to two core independent predictors: Vessel Count and Max Curvature. The BI-RADS-alone model yielded 100% sensitivity but extremely low specificity (30.43%). Crucially, the Combined model significantly outperformed the single-modality approaches, achieving an excellent AUC of 0.896 (vs. 0.652 for BI-RADS alone, p < 0.001). By integrating URM parameters, the Combined model maintained adequate sensitivity (73.53%) while drastically boosting specificity to 91.30%. DCA confirmed superior net clinical benefit for the combined strategy. Conclusions: Quantitative URM imaging effectively characterizes the distinct microvascular features of breast cancers. Integrating URM functional parameters with conventional BI-RADS categorization significantly improves diagnostic specificity. Consequently, this combined approach provides a reliable non-invasive strategy to optimize risk stratification, effectively minimizing false-positive diagnoses and averting unnecessary invasive biopsies in routine clinical practice.
Li et al. (2026) studied this question.