In the external validation cohort, this model [AUC = 0.919(95% CI:0.846-0.992)] also demonstrated superior performance compared to either the standalone clinical-radiological model [AUC = 0.801(95% CI:0.680-0.924)] or the delta-radiomics model [AUC = 0.803(95% CI:0.678-0.928)].<h4>Conclusions</h4>Delta-radiomics based on MRI, combined with clinical parameters, represents a promising non-invasive approach for more accurately predicting Ki-67 downstaging in breast cancer following NAT, outperforming conventional radiomics models. The gene discussed is MKI67; the disease is breast carcinoma.