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Clinical Applications of textural analysis in Non-Small Cell Lung cancer

Phillips, Iain, Ajaz, Mazhar, Ezhil, Veni, Prakash, Vineet, Alobaidli, Sheaka, McQuaid, Sarah J., South, Christopher, Scuffham, James, Nisbet, Andrew and Evans, Philip (2017) Clinical Applications of textural analysis in Non-Small Cell Lung cancer British Journal of Radiology, 91 (1081).

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Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed ‘radiomics’ and includes semantic and agnostic approaches. Texture Analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue sub-types of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarise the current published data and understand the potential future role of TA in managing lung cancer.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Physics
Authors :
Ezhil, Veni
Prakash, Vineet
Alobaidli, Sheaka
McQuaid, Sarah J.
South, Christopher
Date : 27 October 2017
DOI : 10.1259/bjr.20170267
Copyright Disclaimer : © 2018 The Authors. Published by the British Institute of Radiology
Depositing User : Clive Harris
Date Deposited : 06 Sep 2017 14:51
Last Modified : 06 Jul 2019 05:24

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