Phillips, I;
Ezhil, V;
Hussein, M;
South, C;
Nisbet, A;
Alobaidli, S;
Prakash, V;
... Evans, P; + view all
(2019)
Textural analysis and lung function study: Predicting lung fitness for radiotherapy from a CT scan.
BJR | Open
, 1
(1)
, Article 20180001. 10.1259/bjro.20180001.
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Abstract
Objective: 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). 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. Results: 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 (p = 0.005, 0.0008 and 0.0418 respectively; two-sided Mann–Whitney test). Conclusion: 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 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.
| Type: | Article |
|---|---|
| Title: | Textural analysis and lung function study: Predicting lung fitness for radiotherapy from a CT scan |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1259/bjro.20180001 |
| Publisher version: | https://doi.org/10.1259/bjro.20180001 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10106520 |
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