Background: While most cancer patients are treated at community hospitals, specialized equipment, diagnostic tests, and therapeutic regimens are generally validated at academic settings. Here we present a novel approach to precision medicine to improve outcomes in breast cancer care in the community setting. TumorScope (TS) is a biophysical modelling platform that uses only pretreatment standard of care (SoC) diagnostic data (demographics, drug regimen, imaging (DCE MRI) & pathology) to construct a 3D model of the tumor. TS integrates models for tumor morphology, metabolism, vascularity, and drug behavior and simulates predicted tumor response longitudinally to NAT. With this information TS predicts the reduction in tumor volume and the pathological complete response (pCR), a surrogate marker of long-term outcome, to anticipated NAT in EBC. Methods: We performed a single center validation study to show the clinical applicability of TS. Patients at Northwest Community Healthcare that received NAT with corresponding pretreatment MRI were identified in a chart review. A validation set, independent from the training set, was generated from this list and data processed through TS (n=50). Pretreatment patient SoC diagnostic data was loaded into TS. TS predicted the weekly volumetric response throughout the treatment, and it simulated residual volume to predict pCR. The validation was performed using ground truth from post treatment assessment of pCR, radiographic volumes extracted from MRIs. Results: TS predicted pCR, prospectively defined as a simulated residual tumor volume <0.01 cm³ or a 99.9% or greater reduction in tumor volume. Performance metrics of TS were calculated. TS tumor volume prediction accuracy had an area under the curve (AUC)=0.947 with sensitivity and specificity of 93.3% and 94.3% respectively. Performance was robust across all subtypes (See Table 1). TS predicted the reduction in tumor volume with a median absolute volumetric error of 3.4% as compared to radiographic volume from pre-surgery MRIs (n=50). Conclusions: In summary, TS accurately predicts patient-specific tumor volume reduction and pCR prior to NAT in EBC using only SoC pretreatment data, which could lead to a more personalized cancer care for the patient. Moreover, we demonstrate the feasibility of implementing TS at community hospitals.