Textural analysis and lung function study: Predicting lung fitness for radiotherapy from a CT scan.
Journal
BJR open
ISSN: 2513-9878
Titre abrégé: BJR Open
Pays: England
ID NLM: 101749810
Informations de publication
Date de publication:
2019
2019
Historique:
received:
02
05
2018
revised:
03
08
2018
accepted:
06
08
2018
entrez:
12
11
2020
pubmed:
29
4
2019
medline:
29
4
2019
Statut:
epublish
Résumé
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 s) and TLCO (transfer factor of carbon monoxide). 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 29 fit patients (defined as FEV1 and TLCO above 50 % predicted value) and 32 unfit patients (FEV1 or TLCO below 50% predicted). Mean and median density and median entropy were significantly different between fit and unfit patients ( Density and entropy assessment can differentiate between fit and unfit patients for radical radiotherapy, using standard CT imaging. This study shows that a novel assessment can generate further data from standard CT imaging. These data could be combined with existing studies to form a multiorgan patient fitness assessment from a single CT scan.
Identifiants
pubmed: 33178905
doi: 10.1259/bjro.20180001
pmc: PMC7592404
doi:
Types de publication
Journal Article
Langues
eng
Pagination
20180001Informations de copyright
© 2019 The Authors. Published by the British Institute of Radiology.
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