MKI67 and neoplasm: Incorporating QUS biomarkers—based on parameters such as scattering, attenuation, and amplitude-based signal statistics—together with clinical factors (e.g., age, tumor stage, lymph node status, Ki-67 expression), molecular characteristics (e.g., ER, PR, HER2 status, molecular subtypes, transcriptomic signatures), and additional 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].