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      Harmonizing the pixel size in retrospective computed tomography radiomics studies

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          Abstract

          Consistent pixel sizes are of fundamental importance for assessing texture features that relate intensity and spatial information in radiomics studies. To correct for the effects of variable pixel sizes, we combined image resampling with Butterworth filtering in the frequency domain and tested the correction on computed tomography (CT) scans of lung cancer patients reconstructed 5 times with pixel sizes varying from 0.59 to 0.98 mm. One hundred fifty radiomics features were calculated for each preprocessing and field-of-view combination. Intra-patient agreement and inter-patient agreement were compared using the overall concordance correlation coefficient (OCCC). To further evaluate the corrections, hierarchical clustering was used to identify patient scans before and after correction. To assess the general applicability of the corrections, they were applied to 17 CT scans of a radiomics phantom. The reduction in the inter-scanner variability relative to non–small cell lung cancer patient scans was quantified. The variation in pixel sizes caused the intra-patient variability to be large (OCCC <95%) relative to the inter-patient variability in 79% of the features. However, with the resampling and filtering corrections, the intra-patient variability was relatively large in only 10% of the features. With the filtering correction, 8 of 8 patients were correctly clustered, in contrast to only 2 of 8 without the correction. In the phantom study, resampling and filtering the images of a rubber particle cartridge substantially reduced variability in 61% of the radiomics features and substantially increased variability in only 6% of the features. Surprisingly, resampling without filtering tended to increase the variability. In conclusion, applying a correction based on resampling and Butterworth low-pass filtering in the frequency domain effectively reduced variability in CT radiomics features caused by variations in pixel size. This correction may also reduce the variability introduced by other CT scan acquisition parameters.

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          Most cited references 14

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          Textural features corresponding to textural properties

           M. Amadasun,  R. King (1989)
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            Measuring Computed Tomography Scanner Variability of Radiomics Features.

            The purpose of this study was to determine the significance of interscanner variability in CT image radiomics studies.
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              Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters.

              Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45-60 minutes post-injection of 10 mCi of [(18)F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                21 September 2017
                2017
                : 12
                : 9
                Affiliations
                [1 ] Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
                [2 ] Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, TX, United States of America
                [3 ] Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
                [4 ] Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
                Institute of Automation Chinese Academy of Sciences, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: DM AKJ CSN LC.

                • Data curation: DM CSN.

                • Formal analysis: DM LC.

                • Funding acquisition: LC CSN.

                • Investigation: DM CSN LC.

                • Methodology: DM LC.

                • Project administration: DM LC.

                • Resources: DM AKJ CSN LC.

                • Software: DM XF LZ JY.

                • Supervision: LC.

                • Validation: DM LC.

                • Visualization: DM XF LC.

                • Writing – original draft: DM.

                • Writing – review & editing: DM CSN AKJ LC.

                Article
                PONE-D-16-39442
                10.1371/journal.pone.0178524
                5608195
                28934225
                © 2017 Mackin et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 6, Tables: 2, Pages: 17
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: R03CA178495
                Award Recipient :
                This work was supported by the National Cancer Institute of the National Institutes of Health under award number R03CA178495. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
                Categories
                Research Article
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Computed Axial Tomography
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Computed Axial Tomography
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Tomography
                Computed Axial Tomography
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Tomography
                Computed Axial Tomography
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Tomography
                Computed Axial Tomography
                Engineering and Technology
                Signal Processing
                Signal Filtering
                Butterworth Filters
                Computer and Information Sciences
                Software Engineering
                Preprocessing
                Engineering and Technology
                Software Engineering
                Preprocessing
                Research and Analysis Methods
                Imaging Techniques
                Physical Sciences
                Chemistry
                Polymer Chemistry
                Macromolecules
                Polymers
                Elastomers
                Rubber
                Physical Sciences
                Materials Science
                Materials by Structure
                Polymers
                Elastomers
                Rubber
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Lung and Intrathoracic Tumors
                Biology and Life Sciences
                Plant Science
                Plant Anatomy
                Wood
                Cork
                Medicine and Health Sciences
                Health Care
                Custom metadata
                This paper uses both private health data and phantom data. Restrictions have been imposed on the private health data by the Institutional Review Board of MD Anderson. Interested researchers may contact the corresponding author with inquiries, as well as Toni Williams, the clinical protocol administrator, at towilliams@ 123456mdanderson.org . The phantom data is available at the Cancer Imaging Archive at the following link: http://doi.org/10.7937/K9/TCIA.2017.zuzrml5b.

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