MKI67 and neoplasm: Combining QUS biomarkers—based on parameters such as scattering, attenuation, and amplitude-based signal statistics—with clinical factors (e.g., patient age, tumor stage, lymph node status, Ki-67 expression), molecular characteristics (e.g., ER, PR, HER2 status, molecular subtypes, transcriptomic signatures), and other imaging modalities (MRI, mammography, elastography), as well as radiomic features and artificial intelligence (AI) algorithms, enables the construction of multimodal predictive models with high translational potential [38,39,40,41,42,47,48,53,98,99,100,101,102].