Mathematical modelling for patient selection in proton therapy.

Mee, Thomas; Kirkby, Norman; Kirkby, Karen J

dc.contributor.author	Mee, Thomas
dc.contributor.author	Kirkby, Norman
dc.contributor.author	Kirkby, Karen J
dc.date.accessioned	2018-03-17T21:06:12Z
dc.date.available	2018-03-17T21:06:12Z
dc.date.issued	2018-02-13
dc.identifier.citation	Mathematical modelling for patient selection in proton therapy. 2018, Clin Oncol	en
dc.identifier.issn	1433-2981
dc.identifier.pmid	29452724
dc.identifier.doi	10.1016/j.clon.2018.01.007
dc.identifier.uri	http://hdl.handle.net/10541/620840
dc.description.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 used 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.
dc.language.iso	en	en
dc.rights	Archived with thanks to Clinical oncology (Royal College of Radiologists (Great Britain))	en
dc.title	Mathematical modelling for patient selection in proton therapy.	en
dc.type	Article	en
dc.contributor.department	Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester	en
dc.identifier.journal	Clinical Oncology	en
refterms.dateFOA	2020-04-27T11:04:42Z
html.description.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 used 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.