Optimizing Radiotherapy Plans for Cancer Treatment with Tensor Networks

Abstract

We present a novel application of Tensor Network methods in cancer treatment as a potential tool to solve the dose optimization problem in radiotherapy. In particular, the Intensity-Modulated Radiation Therapy (IMRT) technique - that allows treating irregular and inhomogeneous tumors while reducing the radiation toxicity on healthy organs - is based on the optimization of the radiation beamlets intensities. The optimization aims to maximize the delivery of the therapy dose to cancer while avoiding the organs at risk to prevent their damage by the radiation. Here, we map the dose optimization problem into the search of the ground state of an Ising-like Hamiltonian, describing a system of long-range interacting qubits. Finally, we apply a Tree Tensor Network algorithm to find the ground-state of the Hamiltonian. In particular, we present an anatomical scenario exemplifying a prostate cancer treatment. A similar approach can be applied to future hybrid classical-quantum algorithms, paving the way for the use of quantum technologies in future medical treatments.

arXiv comment12 pages, 8 figures

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Cited by in corpus (3)

• Tensor networks for quantum machine learning
• Survey on Computational Applications of Tensor Network Simulations
• Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics