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Abstract
Spinal metastases often occur in the advanced stages of breast, lung or prostate cancer, resulting in a significant impact on the patient’s quality of life. Current treatment modalities for spinal metastases include both systemic and localized treatments that aim to decrease pain, improve mobility and structural stability, and control tumour growth. With the development of non-toxic photosensitizer drugs, photodynamic therapy (PDT) has shown promise as a minimally invasive non-thermal alternative in oncology, including for spinal metastases. To apply PDT to spinal metastases, predictive algorithms that optimize tumour treatment and minimize the risk of spinal cord damage are needed to assess the feasibility of the treatment and encourage a broad acceptance of PDT in clinical trials. This work presents a framework for PDT modelling and planning, and simulates the feasibility of using a BPD-MA mediated PDT to treat bone metastases at two different wavelengths (690 nm and 565 nm). An open-source software for PDT planning, PDT-SPACE, is used to evaluate different configurations of light diffusers (cut-end and cylindrical) fibres with optimized power allocation in order to minimize the damage to spinal cord or maximize tumour destruction. The work is simulated on three CT images of metastatically involved vertebrae acquired from three patients with spinal metastases secondary to colorectal or lung cancer. Simulation results show that PDT at a 565 nm wavelength has the ability to treat 90% of the metastatic lesion with less than 17% damage to the spinal cord. However, the energy required, and hence treatment time, to achieve this outcome with the 565 nm is infeasible. The energy required and treatment time for the longer wavelength of 690 nm is feasible (
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1 University of Toronto, Edward S. Rogers Sr. Department of Electrical and Computer Engineering, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938)
2 Harvard Medical School, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Harvard-Massachusetts Institute of Technology, Division of Health Sciences and Technology, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786)
3 University of Toronto, Department of Medical Biophysics, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938)
4 University of Toronto, Institute of Biomedical Engineering, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); Sunnybrook Research Institute, Orthopaedic Biomechanics Laboratory, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938)
5 University of Toronto, Institute of Biomedical Engineering, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); Sunnybrook Research Institute, Orthopaedic Biomechanics Laboratory, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); University of Toronto, Department of Surgery, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); Sunnybrook Research Institute, Holland Bone and Joint Research Program, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938)
6 University of Toronto, Department of Medical Biophysics, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); University of Toronto, Department of Surgery, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); University Health Network, Techna Institute, Toronto, Canada (GRID:grid.231844.8) (ISNI:0000 0004 0474 0428)
7 University of Toronto, Department of Medical Biophysics, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); University Health Network, Princess Margaret Cancer Center, Toronto, Canada (GRID:grid.231844.8) (ISNI:0000 0004 0474 0428)