Utilizing size-based thresholds of stiffness gradient to reclassify BI-RADS category 3-4b lesions increases diagnostic performance.

Aim: To investigate the role of utilizing size-based thresholds of stiffness gradient in diagnosing solid breast lesions and optimizing original Breast Imaging-Reporting And Data System (BI-RADS) classifications.

Materials And Methods: Two-hundred and twenty-seven consecutive women underwent shear-wave elastography (SWE) before ultrasound-guided biopsy, and 234 solid breast lesions categorized as BI-RADS 3-5 were analysed. Receiver operating characteristic curve analysis was performed based on histopathology. Diagnostic performance among SWE, BI-RADS, and their combination were compared.

Results: The stiffness gradient correlated with the standard deviation of elasticity (SD, r=0.90), and with Tozaki's pattern classification (r=0.64). The area under the receiver operating characteristic curves (AUC) for stiffness gradient (0.939) outperformed SD (0.897) or colour pattern (0.852). Due to significant association with lesion size (r=0.394, p<0.001), stiffness gradient's size-based thresholds (lesions >15 mm: 82.5 kPa; lesions ≤15 mm: 51.1 kPa) were established to reclassify BI-RADS 3-4b lesions. Upgrading category 3 lesions (over the corresponding cut-off value, 3 to 4a) and downgrading categories 4a-4b lesions (less than or equal to the corresponding cut-off value, 4b to 4a, 4a to 3), yielded significant improvement in specificity (90.28% versus 77.78%, p<0.001) and AUC (0.948 versus 0.926, p=0.035) than BI-RADS alone. No significant loss emerged in the sensitivity (88.89% versus 91.11%, p=0.500).

Conclusion: Stiffness gradient exhibited better discriminatory ability than SD or four-colour pattern classification in determining solid breast lesions and applying its size-specific thresholds to categorize BI-RADS 3-4b lesions could improve diagnostic performance.