Impact of contrast-enhanced ultrasound optimized breast imaging reporting and data system category on the biopsy decision for non-mass breast lesions.

Objective: To investigate the impact of contrast-enhanced ultrasound (CEUS) optimized Breast Imaging Reporting and Data System (BI-RADS) prediction model on the biopsy decision for non-mass breast lesions (NMLs). Methods: 148 NMLs histopathologically confirmed from 142 patients who underwent ultrasound (US) and CEUS examination were retrospectively enrolled. US and CEUS features were compared between malignant and benign NMLs. A CEUS-optimized BI-RADS category prediction model was developed using logistic regression. The diagnostic performance and impact on biopsy decision of US and CEUS-optimized BI-RADS category were compared. Results: Of 148 NMLs, 77 were malignant and 71 benign. CEUS features such as earlier wash-in, later wash-out, hyperenhancement, heterogeneous enhancement, enlarged enhancement extent, and crab claw-like enhancement were significantly associated with malignancy (P<0.05). The enlarged enhancement extent and crab claw-like enhancement were independent risk factors for malignancy (P<0.05). Using BI-RADS 4B as the cutoff, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for diagnosing NMLs increased from 53.2%, 63.4%, 61.2%, 55.6%, and 58.1% before optimization to 93.5%, 81.7%, 84.7%, 92.1%, and 87.8% after CEUS optimization, respectively. With BI-RADS 4A or 4B as the biopsy threshold, the biopsy rate, malignancy detection rate, and missed diagnosis rate were 100%, 52.03%, and 0% before optimization, changing to 75%, 66.67%, and 3.90% after optimization for 4A, respectively. The corresponding values for 4B were 45.95%, 60.29%, and 46.75% before optimization, and 57.43%, 84.71%, and 6.49% after optimization, respectively. Conclusion: The CEUS-optimized BI-RADS category prediction model could provide valuable guidance for biopsy decision-making in NMLs.