TumorTwin: A python framework for patient-specific digital twins in oncology
Journal:
arXiv
Published Date:
May 1, 2025
Abstract
Background: Advances in the theory and methods of computational oncology have
enabled accurate characterization and prediction of tumor growth and treatment
response on a patient-specific basis. This capability can be integrated into a
digital twin framework in which bi-directional data-flow between the physical
tumor and the digital tumor facilitate dynamic model re-calibration,
uncertainty quantification, and clinical decision-support via recommendation of
optimal therapeutic interventions. However, many digital twin frameworks rely
on bespoke implementations tailored to each disease site, modeling choice, and
algorithmic implementation.
Findings: We present TumorTwin, a modular software framework for
initializing, updating, and leveraging patient-specific cancer tumor digital
twins. TumorTwin is publicly available as a Python package, with associated
documentation, datasets, and tutorials. Novel contributions include the
development of a patient-data structure adaptable to different disease sites, a
modular architecture to enable the composition of different data, model,
solver, and optimization objects, and CPU- or GPU-parallelized implementations
of forward model solves and gradient computations. We demonstrate the
functionality of TumorTwin via an in silico dataset of high-grade glioma growth
and response to radiation therapy.
Conclusions: The TumorTwin framework enables rapid prototyping and testing of
image-guided oncology digital twins. This allows researchers to systematically
investigate different models, algorithms, disease sites, or treatment decisions
while leveraging robust numerical and computational infrastructure.
