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A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications.

Johnson, Corinne
; 
Price, Gareth J
; 
Khalifa, Jonathan
; ; 
Dekker, A
; 
Moore, Christopher J
; 
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Abstract
The gross tumour volume (GTV) is predictive of clinical outcome and consequently features in many machine-learned models. 4D-planning, however, has prompted substitution of the GTV with the internal gross target volume (iGTV). We present and validate a method to synthesise GTV data from the iGTV, allowing the combination of 3D and 4D planned patient cohorts for modelling.
Authors
Johnson, Corinne
Price, Gareth J
Khalifa, Jonathan
Faivre-Finn, Corinne
Dekker, A
Moore, Christopher J
van Herk, Marcel
Affiliation
Manchester Cancer Research Centre, Division of Molecular and Clinical Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Sciences Centre, UK
Description
Date
2017-12-06
Publisher
Collections
All Christie Publications
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Type
Article
Citation
A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications. 2017 Radiother Oncol
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Journal ISSN
Volume Title
URI
http://hdl.handle.net/10541/620768
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