Cancer is a prevalent disease, and while many significant advances have been made, the ability to accurately predict how an individual tumor will grow, and ultimately respond to therapy remains limited. We use spatial spectral analysis of 20 patients accrued to a phase II study of preoperative SABR with 9.5 x 3 Gy for early-stage breast cancer whose tissues were stained with multiplex immunofluorescence. We employ the reaction diffusion framework to compare the data-deduced two-point

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... function and the corresponding spatial power spectral distribution with the theoretically predicted ones. A single histopathological slice suffices to characterize the reaction diffusion equation dynamics through its power spectral density giving us an interpretative key in terms of infiltration and diffusion of cancer on a per-patient basis. This novel approach tackles model parameter inference problems for tumor infiltration forecasting and classification and immediately informs clinical treatments.