Poster Poster Program Therapy Physics

Reduced-Order, Radiobiologically Informed Simulation Framework for Magnetic Hyperthermia–Radiation Therapy Scenario Analysis

Abstract
Purpose

To develop a spatially resolved, fractionation-aware radiobiological simulation framework for quantitatively evaluating radiation therapy (RT), hyperthermia–radiation therapy (HTRT), and nanoparticle-mediated magnetic hyperthermia–radiation therapy (MHRT).

Methods

A voxel-based model was implemented to integrate radiation dose response, thermal damage, hypoxia modulation, and radiosensitization within a unified framework. Voxel-wise per-fraction survival distributions were explicitly propagated across treatment fractions to compute clinically interpretable endpoints, including tumor control probability as a function of fraction number, TCP(n), and expected clonogen burden. Simulations were performed using a representative prostate cancer cell line (PC3) under identical RT conditions, enabling direct comparison of RT, HTRT, and MHRT.

Results

Under fractionated treatment schedules, TCP(n) curves exhibited systematic separation across modalities, with MHRT achieving tumor control at lower fraction numbers than HTRT and RT. Analysis of voxel-wise survival distributions demonstrated that these differences were driven primarily by expansion of low-survival subvolumes rather than changes in median survival alone. Localized nanoparticle-mediated heating further enhanced this effect beyond uniform hyperthermia. Parametric sweeps over key hyperthermia and radiosensitization parameters showed that the relative ordering of RT, HTRT, and MHRT was preserved.

Conclusion

This framework provides a quantitative approach for linking spatially heterogeneous survival distributions to fractionation-dependent tumor control endpoints. By emphasizing distribution-level effects rather than mean response alone, the model offers mechanistic insight into combined hyperthermia–radiation therapies and establishes a flexible platform for systematic treatment evaluation and optimization.

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