Mathematical Modelling for Patient Selection in Proton TherapyCitation formats

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Mathematical Modelling for Patient Selection in Proton Therapy. / Mee, Thomas; Kirkby, Norman; Kirkby, Karen.

In: Clinical Oncology, Vol. 30, No. 5, 14.02.2018, p. 299-306.

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@article{40d2c455675f4cc4a5b6a5d115637044,
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.",
keywords = "Discrete event simulation, NTCP, mathematical modelling, patient selection, proton therapy",
author = "Thomas Mee and Norman Kirkby and Karen Kirkby",
year = "2018",
month = feb,
day = "14",
doi = "10.1016/j.clon.2018.01.007",
language = "English",
volume = "30",
pages = "299--306",
journal = "Clinical Oncology",
issn = "0936-6555",
publisher = "W.B. Saunders Co. Ltd",
number = "5",

}

RIS

TY - JOUR

T1 - Mathematical Modelling for Patient Selection in Proton Therapy

AU - Mee, Thomas

AU - Kirkby, Norman

AU - Kirkby, Karen

PY - 2018/2/14

Y1 - 2018/2/14

N2 - 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.

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.

KW - Discrete event simulation

KW - NTCP

KW - mathematical modelling

KW - patient selection

KW - proton therapy

UR - http://www.scopus.com/inward/record.url?scp=85041899239&partnerID=8YFLogxK

U2 - 10.1016/j.clon.2018.01.007

DO - 10.1016/j.clon.2018.01.007

M3 - Article

VL - 30

SP - 299

EP - 306

JO - Clinical Oncology

JF - Clinical Oncology

SN - 0936-6555

IS - 5

ER -