Two critical metrics that describe tumor dynamics are the tumor growth rate and the invasion rate into adjacent healthy tissue (1). These properties can be characterized using reaction-diffusion models that capture the spatiotemporal evolution of tumors. Retrospective fits and evaluations have revealed a high correlation of tumor growth rate and radiation response (2). Previous predictive applications estimated the two mentioned rates from pre-treatment MRI features at multiple distinct time points and were able to forecast overall survival and radiation treatment efficacy in glioblastoma (3-5). However, clinical application has been limited, as these methods require multiple pre-treatment MRI scans, which are unavailable for most cancer types on a routine clinical basis. We propose an innovative mathematical approach analyzing routinely available digitized hematoxylin and eosin (H&E)-stained biopsy tissue slides. From these slides, we extract quantitative cell centroid point patterns and utilize Fourier transformation as well as two-point correlation functions to estimate tissue-specific tumor growth and invasion parameters (6). These parameters can be used to calibrate mathematical tumor models, that effectively enable the construction of individual (passive) digital twins from minimal and widely available routine clinical data from one single time point. We validated this framework across multiple cancer types, demonstrating predictive capability for clinically relevant outcomes, including radiotherapy response, progression-free survival, and overall survival. This method significantly reduces data requirements, enables broader clinical adoption, and thus may contribute to personalized oncology management. 1. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144:646-74 2. Rockne R, Rockhill JK, Mrugala M, Spence AM, Kalet I, Hendrickson K, et al. Predicting the efficacy of radiotherapy in individual glioblastoma patientsin vivo:a mathematical modeling approach. Physics in Medicine and Biology 2010;55:3271-85 3. Swanson KR, Alvord EC, Jr., Murray JD. A quantitative model for differential motility of gliomas in grey and white matter. Cell Prolif 2000;33:317-29 4. Jackson PR, Juliano J, Hawkins-Daarud A, Rockne RC, Swanson KR. Patient-specific mathematical neuro-oncology: using a simple proliferation and invasion tumor model to inform clinical practice. Bull Math Biol 2015;77:846-56 5. Massey SC, White H, Whitmire P, Doyle T, Johnston SK, Singleton KW, et al. Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma. PLoS One 2020;15:e0230492 6. Pasetto S, Montejo M, Zahid MU, Rosa M, Gatenby R, Schlicke P, et al. Calibrating tumor growth and invasion parameters with spectral spatial analysis of cancer biopsy tissues. NPJ Syst Biol Appl 2024;10:112