A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications.
Authors
Johnson, CorinnePrice, 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, UKIssue Date
2017-12-06
Metadata
Show full item recordAbstract
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.Citation
A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications. 2017 Radiother OncolJournal
Radiotherapy and OncologyDOI
10.1016/j.radonc.2017.11.015PubMed ID
29223683Type
ArticleLanguage
enISSN
1879-0887ae974a485f413a2113503eed53cd6c53
10.1016/j.radonc.2017.11.015
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