A statistical, voxelised model of prostate cancer for biologically optimised radiotherapy

Robert N Finnegan; Hayley M Reynolds; Martin A Ebert; Yu Sun; Lois Holloway; Jonathan R Sykes; Jason Dowling; Catherine Mitchell; Scott G Williams; Declan G Murphy; Annette Haworth

Physics and Imaging in Radiation Oncology (Jan 2022)

A statistical, voxelised model of prostate cancer for biologically optimised radiotherapy

Robert N Finnegan,
Hayley M Reynolds,
Martin A Ebert,
Yu Sun,
Lois Holloway,
Jonathan R Sykes,
Jason Dowling,
Catherine Mitchell,
Scott G Williams,
Declan G Murphy,
Annette Haworth

Affiliations

Robert N Finnegan: Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia; Liverpool Cancer Therapy Centre, South Western Sydney Local Health District, Liverpool, New South Wales, Australia; InghamInstitute for Applied Medical Research, Liverpool, New South Wales, Australia; Corresponding author.
Hayley M Reynolds: Auckland Bioengineering Institute, University of Auckland, New Zealand
Martin A Ebert: Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia; School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia; 5D Clinics, Claremont, Western Australia, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia
Yu Sun: Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
Lois Holloway: Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia; Liverpool Cancer Therapy Centre, South Western Sydney Local Health District, Liverpool, New South Wales, Australia; InghamInstitute for Applied Medical Research, Liverpool, New South Wales, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia; South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
Jonathan R Sykes: Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia; Department of Radiation Oncology, Sydney West Radiation Oncology Network, Blacktown Cancer & Haematology Centre, Blacktown, New South Wales, Australia; Department of Radiation Oncology, Sydney West Radiation Oncology Network, Crown Princess Mary Cancer Centre, Westmead, New South Wales, Australia
Jason Dowling: Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia; School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, New South Wales, Australia; CSIRO Health and Biosecurity, The Australian e-Health and Research Centre, Herston, Queensland, Australia
Catherine Mitchell: Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
Scott G Williams: Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia; Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
Declan G Murphy: Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia; Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
Annette Haworth: Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia

Journal volume & issue: Vol. 21
pp. 136 – 145

Abstract

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Background and purpose: Radiation therapy (RT) is commonly indicated for treatment of prostate cancer (PC). Biologicallyoptimised RT for PC may improve disease-free survival. This requires accurate spatial localisation and characterisation of tumour lesions. We aimed to generate a statistical, voxelised biological model to complement in vivomultiparametric MRI data to facilitate biologically-optimised RT. Material and methods: Ex vivo prostate MRI and histopathological imaging were acquired for 63 PC patients. These data were co-registered to derive three-dimensional distributions of graded tumour lesions and cell density. Novel registration processes were used to map these data to a common reference geometry. Voxelised statistical models of tumour probability and cell density were generated to create the PC biological atlas. Cell density models were analysed using the Kullback–Leibler divergence to compare normal vs. lognormal approximations to empirical data. Results: A reference geometry was constructed using ex vivo MRI space, patient data were deformably registered using a novel anatomy-guided process. Substructure correspondence was maintained using peripheral zone definitions to address spatial variability in prostate anatomy between patients. Three distinct approaches to interpolation were designed to map contours, tumour annotations and cell density maps from histology into ex vivo MRI space. Analysis suggests a log-normal model provides a more consistent representation of cell density when compared to a linear-normal model. Conclusion: A biological model has been created that combines spatial distributions of tumour characteristics from a population into three-dimensional, voxelised, statistical models. This tool will be used to aid the development of biologically-optimised RT for PC patients.

Published in Physics and Imaging in Radiation Oncology

ISSN: 2405-6316 (Online)
Publisher: Elsevier
Country of publisher: Ireland
LCC subjects: Medicine: Medicine (General): Medical physics. Medical radiology. Nuclear medicine; Medicine: Internal medicine: Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Website: https://www.journals.elsevier.com/physics-and-imaging-in-radiation-oncology/

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