Identifying cancer progression as soon as feasible is critical for calibrating the patient’s treatment and increasing their chances of survival. As a result, building statistical processes to evaluate routinely obtained histopathological slices of patient biopsy resection areas is critical for forecasting disease infiltration and growth. We use spatial statistical and spectral analysis to link dynamical evolution as defined by a reaction-diffusion framework to morphological changes caused by malignancy growth as seen in histological tissue cellular distribution. We examined tissues stained with multiplex immunofluorescence from patients enrolled in a Phase II Study of preoperative SABR with 9.5 x 3 Gy for early-stage breast cancer to assess the infiltration of antigen-presenting cells, T-cells, NK cells, B-cells, and tumor cells using various combinations of CD3 and CD4-CD8, Foxp3, PD-1, CD68, and PanCytoKeratin. The patient-specific growth parameter showing the tumor expansion and the diffusion parameter describing the tumor infiltration is recovered by comparing the data-deduced two-point correlation function, and the corresponding power spectral distribution with the theoretical predicted one by the reaction-diffusion equation. We developed a mathematical method to determine the characteristics of the infiltration and the progression of the patient’s disease from histopathological slices of biopsy resection areas collected at the single time of the diagnosis. With little information, this cutting-edge method seeks to anticipate a precisely tailored patient treatment.