In medicine, digital twins of tumors can be used to perform counterfactual in silico simulations of therapeutic interventions. The goals can, among others, be the prediction of treatment success or effects of e.g. neoadjuvant approaches, or to identify successful treatments among a select set of feasible ones. However, thorough calibration and validation are necessary to ensure both clinical utility and applicability [1]. On the other hand, clinical relevance may be increased when the reliable calibration can be performed early during the patient’s treatment course. Thus, of particular interest are methods that can reliably perform these tasks with data acquired only at diagnosis. Clinical impact can particularly be ensured when considering data that are routinely collected in the clinics. This presentation will focus on refined methods to identify cancer hallmarks as the dynamic behavior of tissues on routinely collected hematoxylin and eosin (H&E)-stained slides from individual patients [2,3]. The mechanistic architectures are identified in the context of mathematical interpretable models. This not only results in microscopic insights for the respective slide, but also in relevant macroscopic dynamics for the in silico tumor. The framework was tested across multiple cancers with different clinical contexts. We will discuss implications of the calibrated dynamics of the digital twin and inferred clinical insights on relevant outcomes, such as treatment response, progression-free survival and overall survival. By design, the presented framework significantly reduces data requirements and therefore enables contributions to personalized oncology management through broad clinical implementation.