Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging.
The purpose of this study was to investigate the effect of image preprocessing on radiomic features estimation from computed tomography (CT) imaging of locally advanced rectal cancer (LARC). CT images of 20 patients with LARC were used to estimate 105 radiomic features of 7 classes (shape, first-ord...
| Publicado en: | BioMed Research International pp. 1 - 22 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
3/20/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155845636&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155845636 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/20/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 155845636 155845636 155845636 10.1155/2022/2003286 155845636 ppf: 1 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging. aug: au: Linsalata, Stefania Borgheresi, Rita Marfisi, Daniela Barca, Patrizio Sainato, Aldo Paiar, Fabiola Neri, Emanuele Traino, Antonio Claudio Giannelli, Marco affil: Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy sug: subj: Rectal Neoplasms Radiography Tomography, X-Ray Computed Cancer Patients Radiographic Image Interpretation, Computer-Assisted Classification Diagnostic Imaging Intraclass Correlation Coefficient Repeated Measures Correlational Studies Linear Regression Reproducibility of Results Phantoms, Imaging Multicenter Studies Data Analysis Software Descriptive Statistics Human ab: The purpose of this study was to investigate the effect of image preprocessing on radiomic features estimation from computed tomography (CT) imaging of locally advanced rectal cancer (LARC). CT images of 20 patients with LARC were used to estimate 105 radiomic features of 7 classes (shape, first-order, GLCM, GLDM, GLRLM, GLSZM, and NGTDM). Radiomic features were estimated for 6 different isotropic resampling voxel sizes, using 10 interpolation algorithms (at fixed bin width) and 6 different bin widths (at fixed interpolation algorithm). The intraclass correlation coefficient (ICC) and the coefficient of variation (CV) were calculated to assess the variability in radiomic features estimation due to preprocessing. A repeated measures correlation analysis was performed to assess any linear correlation between radiomic feature estimate and resampling voxel size or bin width. Reproducibility of radiomic feature estimate, when assessed through ICC analysis, was nominally excellent (ICC > 0.9) for shape features, good (0.75 < ICC ≤ 0.9) or moderate (0.5 < ICC ≤ 0.75) for first-order features, and moderate or poor (0 ≤ ICC ≤ 0.5) for textural features. A number of radiomic features characterized by good or excellent reproducibility in terms of ICC showed however median CV values greater than 15%. For most textural features, a significant (p < 0.05) correlation between their estimate and resampling voxel size or bin width was found. In CT imaging of patients with LARC, the estimate of textural features, as well as of first-order features to a lesser extent, is appreciably biased by preprocessing. Accordingly, this should be taken into account when planning clinical or research studies, as well as when comparing results from different studies and performing multicenter studies. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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