Mathematical Modelling for Patient Selection in Proton Therapy

Thomas Mee; Norman Kirkby; Karen Kirkby

doi:10.1016/j.clon.2018.01.007

Mathematical Modelling for Patient Selection in Proton Therapy

Thomas Mee, Norman Kirkby, Karen Kirkby

Division of Cancer Sciences (L5)

Research output: Contribution to journal › Article

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Abstract

Proton beam therapy (PBT) is still relatively new in cancer treatment and the clinical evidence base is relatively sparse. Mathematical modelling offers assistance when selecting patients for PBT and predicting the demand for service. Discrete event simulation, normal tissue complication probability, quality‐adjusted life years and Markov Chain models are all mathematical and statistical modelling techniques currently employed but none is dominant. As new evidence and outcome data become available from PBT, comprehensive models will emerge that are less dependent on the specific technologies of radiotherapy planning and delivery.

Original language	English
Pages (from-to)	299-306
Number of pages	8
Journal	Clinical Oncology
Volume	30
Issue number	5
Early online date	14 Feb 2018
DOIs	https://doi.org/10.1016/j.clon.2018.01.007
Publication status	Published - 14 Feb 2018

Keywords

Discrete event simulation
NTCP
mathematical modelling
patient selection
proton therapy

Research Beacons, Institutes and Platforms

Manchester Cancer Research Centre

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.clon.2018.01.007

Clin Onc Special Edition v7.1Accepted author manuscript, 214 KB

Cite this

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title = "Mathematical Modelling for Patient Selection in Proton Therapy",

abstract = "Proton beam therapy (PBT) is still relatively new in cancer treatment and the clinical evidence base is relatively sparse. Mathematical modelling offers assistance when selecting patients for PBT and predicting the demand for service. Discrete event simulation, normal tissue complication probability, quality‐adjusted life years and Markov Chain models are all mathematical and statistical modelling techniques currently employed but none is dominant. As new evidence and outcome data become available from PBT, comprehensive models will emerge that are less dependent on the specific technologies of radiotherapy planning and delivery.",

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AB - Proton beam therapy (PBT) is still relatively new in cancer treatment and the clinical evidence base is relatively sparse. Mathematical modelling offers assistance when selecting patients for PBT and predicting the demand for service. Discrete event simulation, normal tissue complication probability, quality‐adjusted life years and Markov Chain models are all mathematical and statistical modelling techniques currently employed but none is dominant. As new evidence and outcome data become available from PBT, comprehensive models will emerge that are less dependent on the specific technologies of radiotherapy planning and delivery.

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KW - proton therapy

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Mathematical Modelling for Patient Selection in Proton Therapy

Abstract

Keywords

Research Beacons, Institutes and Platforms

UN SDGs

Access to Document

Other files and links

Fingerprint

Grand Challenge Network in Network+ in Proton Therapy.

Global Challenge Network + in Advanced Radiotherapy.

Establishing the UK’s first high-energy proton therapy service at the Christie Hospital Manchester and University College London Hospital.

Cite this

Mathematical Modelling for Patient Selection in Proton Therapy

Abstract

Keywords

Research Beacons, Institutes and Platforms

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

Grand Challenge Network in Network+ in Proton Therapy.

Global Challenge Network + in Advanced Radiotherapy.

Impacts

Establishing the UK’s first high-energy proton therapy service at the Christie Hospital Manchester and University College London Hospital.

Cite this