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TumorTwin: A python framework for patient-specific digital twins in oncology.

Michael Kapteyn, Anirban Chaudhuri, Ernesto A B F Lima

    Arxiv
    |May 9, 2025
    PubMed
    Summary
    This summary is machine-generated.

    TumorTwin is a new modular software framework for creating patient-specific digital twins of tumors. This tool aids in predicting tumor growth and treatment response, accelerating research in computational oncology.

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    Area of Science:

    • Computational oncology
    • Digital twin technology
    • Cancer modeling

    Background:

    • Patient-specific computational oncology models predict tumor growth and treatment response.
    • Digital twin frameworks integrate physical tumor data for dynamic recalibration and decision support.
    • Current digital twin frameworks are often disease-site specific and lack modularity.

    Purpose of the Study:

    • To present TumorTwin, a modular software framework for developing and utilizing patient-specific cancer digital twins.
    • To provide a flexible and adaptable platform for computational oncology research.

    Main Methods:

    • Developed a modular Python package, TumorTwin, with adaptable data structures for various disease sites.
    • Implemented a modular architecture allowing composition of data, models, solvers, and optimization objects.
    • Included CPU/GPU parallelized implementations for forward model solves and gradient computations.

    Main Results:

    • TumorTwin facilitates initialization, updating, and leveraging of patient-specific cancer digital twins.
    • Demonstrated functionality using an in silico dataset of high-grade glioma growth and radiation therapy response.
    • The framework supports diverse data types, models, and computational approaches.

    Conclusions:

    • TumorTwin enables rapid prototyping and testing of image-guided oncology digital twins.
    • Researchers can systematically investigate various models, algorithms, and treatment strategies.
    • The framework leverages robust numerical and computational infrastructure for advanced cancer research.