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Issue 338: In our recent work, we developed TumorTwin, an open-source computational framework for constructing digital twins of cancer. The framework is designed to support the integration of heterogeneous clinical and imaging data with mechanistic or data-driven tumor growth models in a modular architecture that facilitates reuse across disease sites and modeling approaches.TumorTwin is implemented as a Python package and includes a flexible patient data structure, parallelized solvers for forward and adjoint computations, and utilities for uncertainty quantification and treatment optimization. The package is fully documented and includes example datasets and tutorials to support adoption and reproducibility within the research community. To demonstrate its utility, we applied TumorTwin to simulate high-grade glioma growth and response to radiotherapy using synthetic patient data. This use case highlights the framework's capacity for rapid prototyping and evaluation of modeling and treatment strategies in silico. See the full documentation and source code here . This image visualizes the intertwining of the physical and digital components of the digital twin.