Rationale　and　Objectives:　The　aim　of　this　study　was　to　ascertain　whether　the　utilization　of　multiple　b-value　diffusion-weighted　habitat　imaging,　a　technique　that　depicts　tumor　heterogeneity,　could　aid　in　identifying　breast　cancer　patients　who　would　derive　substantial　benefit　from　neoadjuvant　chemotherapy　(NAC).　Materials　and　Methods:　This　prospective　study　enrolled　143　women　(II-III　breast　cancer),　who　underwent　multi-b-value　diffusion-　weighted　imaging　(DWI)　in　3-T　magnetic　resonance　(MR)　before　NAC.　The　patient　cohort　was　partitioned　into　a　training　set　(consisting　of　100　patients,　of　which　36　demonstrated　a　pathologic　complete　response　[pCR])　and　a　test　set　(featuring　43　patients,　16　of　whom　exhibited　pCR).　Utilizing　the　training　set,　predictive　models　for　pCR,　were　constructed　using　different　parameters:　whole-tumor　radiomics　(ModelWH),　diffusion-weighted　habitat-imaging　(ModelHabitats),　conventional　MRI　features　(ModelCF),　along　with　combined　models　Model　Habitats+CF　.　The　performance　of　these　models　was　assessed　based　on　the　area　under　the　receiver　operating　characteristic　curve　(AUC)　and　calibration　slope.　Results:　In　the　prediction　of　pCR,　ModelWH,　Model　Habitats　,　ModelCF,　and　Model　Habitats+CF　achieved　AUCs　of　0.733,　0.722,　0.705,　and　0.756　respectively,　within　the　training　set.　These　scores　corresponded　to　AUCs　of　0.625,　0.801,　0.700,　and　0.824　respectively　in　the　test　set.　The　DeLong　test　revealed　no　significant　difference　between　ModelWH　and　Model　Habitats　(P　=　0.182),　between　Model　Habitats　and　Model　Habitats+CF　(P　=　0.113).　Conclusion:　The　habitat　model　we　developed,　incorporating　first-order　features　along　with　conventional　MRI　features,　has　demonstrated　accurate　predication　of　pCR　prior　to　NAC.　This　model　holds　the　potential　to　augment　decision-making　processes　in　personalized　treatment　strategies　for　breast　cancer.　(c)　2024　The　Association　of　University　Radiologists.　Published　by　Elsevier　Inc.　All　rights　are　reserved,　including　those　for　text　and　data　mining,　AI　training,　and　similar　technologies.