Background:　Radiomics　research　based　on　whole　tumors　is　limited　by　the　unclear　biological　significance　of　radiomics　features,　which　therefore　lack　clinical　interpretability.　Purpose:　We　aimed　to　determine　whether　features　extracted　from　subregions　defined　by　habitat　imaging,　reflecting　tumor　heterogeneity,　could　identify　breast　cancer　patients　who　will　benefit　from　neoadjuvant　chemotherapy　(NAC),　to　optimize　treatment.　Methods:　143　women　with　stage　II–III　breast　cancer　were　divided　into　a　training　set　(100　patients,　36　with　pathologic　complete　response　[pCR])　and　a　test　set　(43　patients,　16　with　pCR).　Patients　underwent　3-T　magnetic　resonance　imaging　(MRI)　before　NAC.　With　the　pathological　results　as　the　gold　standard,　we　used　the　training　set　to　build　models　for　predicting　pCR　based　on　whole-tumor　radiomics　(ModelWH),　intravoxel　incoherent　motion　(IVIM)-based　habitat　imaging　(ModelHabitats),　conventional　MRI　features　(ModelCF),　and　immunohistochemical　findings　(ModelIHC).　We　also　built　the　combined　models　ModelHabitats+CF　and　ModelHabitats+CF+IHC.　In　the　test　set,　we　compared　the　performance　of　the　combined　models　with　that　of　the　invasive　ModelIHC　by　using　the　area　under　the　receiver　operating　characteristic　curve　(AUC)　and　decision　curve　analysis　(DCA).　Receiver　operating　characteristic　(ROC)　curve　analysis　was　performed　to　evaluate　the　predictive　value　of　the　model.　The　DeLong　test　was　used　to　compare　diagnostic　efficiency　across　different　parameters.　Results:　In　the　prediction　of　pCR,　ModelWH,　ModelHabitats,　ModelCF,　ModelIHC,　ModelHabitats+CF,　ModelCF+IHC　and　ModelHabitats+CF+IHC　achieved　AUCs　of　0.895,　0.757,　0.705,　0.807,　0.800,　0.856,　and　0.891　respectively,　in　the　training　set　and　0.549,　0.708,　0.700,　0.788,　0.745,　0.909,　and　0.891　respectively,　in　the　test　set.　The　DeLong　test　revealed　no　significant　difference　between　ModelIHC　versus　ModelHabitats+CF　(p = 0.695)　and　ModelHabitats+CF+IHC　versus　ModelCF+IHC　(p = 0.382)　but　showed　a　significant　difference　between　ModelIHC　and　ModelHabitats+CF+IHC　(p = 0.043).　Conclusion:　The　habitat　model　we　established　from　first-order　features　combined　with　conventional　MRI　features　and　IHC　findings　accurately　predicted　pCR　before　NAC.　This　model　can　facilitate　decision-making　during　individualized　treatment　for　breast　cancer.　©　2025　American　Association　of　Physicists　in　Medicine.