Fig. 4: Attributes contribution.

Comparison of coefficient importance among four deep learning models : rhpc (n = 120), iMGrhpc (n = 120), iMRrhpc (n = 120), and MRP (n = 120), organized by coefficients in descending order. The horizontal bar plot displays normalized coefficients, derived from averaged contributing values among cases and normalized across the included attributes, highlighting comparable trends among these attributes. Positive values stand for favorable attributes for response prediction, and vise versa for negative values. The horizontal line (error bar) represents the standard deviation centered on the corresponding coefficients. Each attribute associated different group (radiological(r), histopathological(h), personal(p), clinical(c)) is viewed in the colors legend in the upper right. iMGrhpc is based on Pre-NAT mammogram and rhpc data, while iMRrhpc is based on pre-NAT MRI and rhpc data. MRP aggregates and optimizes the outputs of iMGrhpc model and iMRrhpc model.