Dataset Information

Textural analysis and lung function study: Predicting lung fitness for radiotherapy from a CT scan.

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.

SUBMITTER: Phillips I

PROVIDER: S-EPMC7592404 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Textural analysis and lung function study: Predicting lung fitness for radiotherapy from a CT scan.

Phillips Iain I Ezhil Veni V Hussein Mohammad M South Christopher C Nisbet Andrew A Alobaidli Sheaka S Prakash Vineet V Ajaz Mazhar M Wang Helen H Evans Philip P

BJR open 20190429 1

<h4>Objective</h4>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).<h4>Methods</h4>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 ...[more]

PMID: 33178905

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Project description:The purpose of this study was to explore the effects of CT slice thickness, reconstruction algorithm, and radiation dose on quantification of CT features to characterize lung nodules using a chest phantom. Spherical lung nodule phantoms of known densities (-630 and + 100 HU) were inserted into an anthropomorphic thorax phantom. CT scan was performed ten times with relocations. CT data were reconstructed using 12 different imaging settings; three different slice thicknesses of 1.25, 2.5, and 5.0 mm, two reconstruction kernels of sharp and standard, and two radiation dose of 30 mAs and 12 mAs. Lesions were segmented using a semiautomated method. Twenty representative CT quantitative features representing CT density and texture were compared using multiple regression analysis. In 100 HU nodule phantoms, 18 and 19 among 20 computer features showed significant difference between different mAs and reconstruction algorithms, respectively (p ≤ 0.05). 20, 19, and 19 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25 mm, respectively (p ≤ 0.05). In -630 HU nodule phantoms, 18 and 19 showed significant difference between different mAs and reconstruction algorithms, respectively (p ≤ 0.05). 18, 11, and 17 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25 mm, respectively (p ≤ 0.05). When comparing the absolute value of regression coefficient, the effect of slice thickness in 100 HU nodule and reconstruction algorithm in -630 HU nodule was greater than the effect of remaining scan parameters. The slice thickness, mAs, and reconstruction algorithm had a significant impact on the quantitative image features. In clinical studies involving deep learning or radiomics, it should be noted that differences in values can occur when using computer features obtained from different CT scan parameters in combination. Therefore, when interpreting the statistical analysis results, it is necessary to reflect the difference in the computer features depending on the scan parameters.

Dataset Information

Textural analysis and lung function study: Predicting lung fitness for radiotherapy from a CT scan.

Publications

Textural analysis and lung function study: Predicting lung fitness for radiotherapy from a CT scan.

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