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Textural Analysis and Lung Function study: Predicting lung fitness for radiotherapy from a CT scan

Phillips, Iain, Ezhil, Veni, Hussein, Mohammad, South, Christopher, Alobaidli, Sheaka, Nisbet, Andrew, Ajaz, Mazhar, Prakash, Vineet, Wang, Helen and Evans, Philip (2018) Textural Analysis and Lung Function study: Predicting lung fitness for radiotherapy from a CT scan BJR Open.

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Abstract

Objectives

This study tested the hypothesis that shows advanced image analysis can differentiate fit and unfit patients for radical radiotherapy from standard radiotherapy planning imaging, when compared to formal lung function tests (FEV1, Forced Expiratory Volume in 1 second) and TLCO (Transfer Factor of Carbon Monoxide).

Methods

An apical region of interest (ROI) of lung parenchyma was extracted from a standard radiotherapy planning CT scan. Software using a grey level co-occurrence matrix (GLCM) assigned an entropy score to each voxel, based on its similarity to the voxels around it. Density and entropy scores were compared between a cohort of fit patients (defined as FEV1 and TLCO above 50% predicted value) and unfit patients (FEV1 or TLCO below 50% predicted).

Results

29 fit and 32 unfit patients were included. Mean and median density and mean and median entropy were significantly different between fit and unfit patients (p= 0.0021, 0.0019, 0.0357 and 0.0363 respectively, 2 sided t-test).

Conclusions

Density and entropy assessment can differentiate between fit and unfit patients for radical radiotherapy, using standard CT imaging.

Advances in knowledge

This study shows that a novel intervention can generate further data from standard CT imaging. This data could be combined with existing studies to form a multi-organ patient fitness assessment from a single CT scan.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Physics
Authors :
NameEmailORCID
Phillips, Iaini.phillips@surrey.ac.uk
Ezhil, Veni
Hussein, Mohammad
South, Christopher
Alobaidli, Sheaka
Nisbet, AndrewA.Nisbet@surrey.ac.uk
Ajaz, Mazharm.ajaz@surrey.ac.uk
Prakash, Vineet
Wang, Helenh.y.wang@surrey.ac.uk
Evans, Philipp.evans@surrey.ac.uk
Date : 2018
Copyright Disclaimer : This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
Related URLs :
Depositing User : Clive Harris
Date Deposited : 10 Aug 2018 13:12
Last Modified : 13 Aug 2018 12:38
URI: http://epubs.surrey.ac.uk/id/eprint/848889

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