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Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer

OBJECTIVES: To investigate whether adaptive statistical iterative reconstruction (ASIR), a hybrid iterative CT image reconstruction algorithm, affects radiomics feature quantification in primary colorectal cancer compared to filtered back projection. Additionally, to establish whether radiomics from...

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Autores principales: Prezzi, Davide, Owczarczyk, Katarzyna, Bassett, Paul, Siddique, Muhammad, Breen, David J., Cook, Gary J. R., Goh, Vicky
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6717179/
https://www.ncbi.nlm.nih.gov/pubmed/30887205
http://dx.doi.org/10.1007/s00330-019-06073-3
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author Prezzi, Davide
Owczarczyk, Katarzyna
Bassett, Paul
Siddique, Muhammad
Breen, David J.
Cook, Gary J. R.
Goh, Vicky
author_facet Prezzi, Davide
Owczarczyk, Katarzyna
Bassett, Paul
Siddique, Muhammad
Breen, David J.
Cook, Gary J. R.
Goh, Vicky
author_sort Prezzi, Davide
collection PubMed
description OBJECTIVES: To investigate whether adaptive statistical iterative reconstruction (ASIR), a hybrid iterative CT image reconstruction algorithm, affects radiomics feature quantification in primary colorectal cancer compared to filtered back projection. Additionally, to establish whether radiomics from single-slice analysis undergo greater change than those from multi-slice analysis. METHODS: Following review board approval, contrast-enhanced CT studies from 32 prospective primary colorectal cancer patients were reconstructed with 20% ASIR level increments, from 0 to 100%. Radiomics analysis was applied to single-slice and multi-slice regions of interest outlining the tumour: 70 features, including statistical (first-, second- and high-order) and fractal radiomics, were generated per dataset. The effect of ASIR was calculated by means of multilevel linear regression. RESULTS: Twenty-eight CT datasets were suitable for analysis. Incremental ASIR levels determined a significant change (p < 0.001) in most statistical radiomics features, best described by a simple linear relationship. First-order statistical features, including mean, standard deviation, skewness, kurtosis, energy and entropy, underwent a relatively small change in both single-slice and multi-slice analysis (median standardised effect size B = 0.08). Second-order statistical features, including grey-level co-occurrence and difference matrices, underwent a greater change in single-slice analysis (median B = 0.36) than in multi-slice analysis (median B = 0.13). Fractal features underwent a significant change only in single-slice analysis (median B = 0.49). CONCLUSIONS: Incremental levels of ASIR affect significantly CT radiomics quantification in primary colorectal cancer. Second-order statistical and fractal features derived from single-slice analysis undergo greater change than those from multi-slice analysis. KEY POINTS: • Incremental levels of ASIR determine a significant change in most statistical (first-, second- and high-order) CT radiomics features measured in primary colorectal cancer, best described by a linear relationship. • First-order statistical features undergo a small change, both from single-slice and multi-slice radiomics analyses. • Most second-order statistical features undergo a greater change in single-slice analysis than in multi-slice analysis. Fractal features are only affected in single-slice analysis.
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spelling pubmed-67171792019-09-13 Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer Prezzi, Davide Owczarczyk, Katarzyna Bassett, Paul Siddique, Muhammad Breen, David J. Cook, Gary J. R. Goh, Vicky Eur Radiol Computed Tomography OBJECTIVES: To investigate whether adaptive statistical iterative reconstruction (ASIR), a hybrid iterative CT image reconstruction algorithm, affects radiomics feature quantification in primary colorectal cancer compared to filtered back projection. Additionally, to establish whether radiomics from single-slice analysis undergo greater change than those from multi-slice analysis. METHODS: Following review board approval, contrast-enhanced CT studies from 32 prospective primary colorectal cancer patients were reconstructed with 20% ASIR level increments, from 0 to 100%. Radiomics analysis was applied to single-slice and multi-slice regions of interest outlining the tumour: 70 features, including statistical (first-, second- and high-order) and fractal radiomics, were generated per dataset. The effect of ASIR was calculated by means of multilevel linear regression. RESULTS: Twenty-eight CT datasets were suitable for analysis. Incremental ASIR levels determined a significant change (p < 0.001) in most statistical radiomics features, best described by a simple linear relationship. First-order statistical features, including mean, standard deviation, skewness, kurtosis, energy and entropy, underwent a relatively small change in both single-slice and multi-slice analysis (median standardised effect size B = 0.08). Second-order statistical features, including grey-level co-occurrence and difference matrices, underwent a greater change in single-slice analysis (median B = 0.36) than in multi-slice analysis (median B = 0.13). Fractal features underwent a significant change only in single-slice analysis (median B = 0.49). CONCLUSIONS: Incremental levels of ASIR affect significantly CT radiomics quantification in primary colorectal cancer. Second-order statistical and fractal features derived from single-slice analysis undergo greater change than those from multi-slice analysis. KEY POINTS: • Incremental levels of ASIR determine a significant change in most statistical (first-, second- and high-order) CT radiomics features measured in primary colorectal cancer, best described by a linear relationship. • First-order statistical features undergo a small change, both from single-slice and multi-slice radiomics analyses. • Most second-order statistical features undergo a greater change in single-slice analysis than in multi-slice analysis. Fractal features are only affected in single-slice analysis. Springer Berlin Heidelberg 2019-03-18 2019 /pmc/articles/PMC6717179/ /pubmed/30887205 http://dx.doi.org/10.1007/s00330-019-06073-3 Text en © The Author(s) 2019 OpenAccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Computed Tomography
Prezzi, Davide
Owczarczyk, Katarzyna
Bassett, Paul
Siddique, Muhammad
Breen, David J.
Cook, Gary J. R.
Goh, Vicky
Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer
title Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer
title_full Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer
title_fullStr Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer
title_full_unstemmed Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer
title_short Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer
title_sort adaptive statistical iterative reconstruction (asir) affects ct radiomics quantification in primary colorectal cancer
topic Computed Tomography
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6717179/
https://www.ncbi.nlm.nih.gov/pubmed/30887205
http://dx.doi.org/10.1007/s00330-019-06073-3
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