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TumorTwin: a Python framework for patient-specific digital twins in oncology.

Michael G Kapteyn1, Anirban Chaudhuri2, Ernesto A B F Lima2,3

  • 1Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA. michael.kapteyn@austin.utexas.edu.

BMC Medical Informatics and Decision Making
|May 12, 2026
PubMed
Summary
This summary is machine-generated.

TumorTwin is a new software framework for creating patient-specific cancer digital twins. It enables dynamic recalibration and supports clinical decisions for optimal cancer treatment strategies.

Keywords:
Computational oncologyDifferentiable programmingDigital twinImage-based modelingMagnetic resonance imagingPythonSoftware

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Area of Science:

  • Computational oncology
  • Digital twin technology
  • Medical imaging analysis

Background:

  • Computational oncology advances enable patient-specific tumor growth and treatment response prediction.
  • Digital twin frameworks integrate physical tumor data for dynamic recalibration and decision support.
  • Existing digital twin frameworks often require bespoke implementations for specific diseases and models.

Purpose of the Study:

  • To introduce TumorTwin, a modular and differentiable software framework for patient-specific cancer digital twins.
  • To provide a flexible and reusable infrastructure for computational oncology research.
  • To facilitate the development and testing of image-guided oncology digital twins.

Main Methods:

  • Developed a modular and differentiable Python package named TumorTwin.
  • Created an adaptable patient-data structure for diverse disease sites.
  • Implemented a modular architecture for composing data, model, solver, and optimization components.
  • Enabled CPU or GPU parallelized forward model solves and gradient computations.

Main Results:

  • Demonstrated TumorTwin's functionality using an in silico dataset of high-grade glioma growth and radiation therapy response.
  • Showcased the framework's ability to initialize, update, and leverage patient-specific digital twins.
  • Highlighted the public availability of TumorTwin with comprehensive documentation and tutorials.

Conclusions:

  • The TumorTwin framework accelerates the prototyping and testing of image-guided oncology digital twins.
  • Enables systematic investigation of various models, algorithms, disease sites, and treatment decisions.
  • Leverages robust numerical and computational infrastructure for advanced cancer research.