Optimizing Radiotherapy Plans for Cancer Treatment with Tensor Networks

TL;DR

This paper introduces a novel tensor network approach to optimize radiotherapy plans, specifically for IMRT in cancer treatment, by mapping dose optimization to a quantum Hamiltonian problem and solving it with tensor algorithms.

Contribution

It presents a new method applying tensor networks to solve the dose optimization problem in radiotherapy, bridging classical and quantum computational techniques.

Findings

Successfully modeled dose optimization as a quantum Hamiltonian problem.

Applied tensor network algorithms to find optimal radiation dose distributions.

Demonstrated approach on a prostate cancer treatment scenario.

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.