Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings.
Radiomics utilizes quantitative image features (QIFs) to characterize tumor phenotype. In practice, radiological images are obtained from different vendors' equipment using various imaging acquisition settings. Our objective was to assess the inter-setting agreement of QIFs computed from CT ima...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5199063?pdf=render |
| _version_ | 1818241764065017856 |
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| author | Lin Lu Ross C Ehmke Lawrence H Schwartz Binsheng Zhao |
| author_facet | Lin Lu Ross C Ehmke Lawrence H Schwartz Binsheng Zhao |
| author_sort | Lin Lu |
| collection | DOAJ |
| description | Radiomics utilizes quantitative image features (QIFs) to characterize tumor phenotype. In practice, radiological images are obtained from different vendors' equipment using various imaging acquisition settings. Our objective was to assess the inter-setting agreement of QIFs computed from CT images by varying two parameters, slice thickness and reconstruction algorithm.CT images from an IRB-approved/HIPAA-compliant study assessing thirty-two lung cancer patients were included for the analysis. Each scan's raw data were reconstructed into six imaging series using combinations of two reconstruction algorithms (Lung[L] and Standard[S]) and three slice thicknesses (1.25mm, 2.5mm and 5mm), i.e., 1.25L, 1.25S, 2.5L, 2.5S, 5L and 5S. For each imaging-setting, 89 well-defined QIFs were computed for each of the 32 tumors (one tumor per patient). The six settings led to 15 inter-setting comparisons (combinatorial pairs). To reduce QIF redundancy, hierarchical clustering was done. Concordance correlation coefficients (CCCs) were used to assess inter-setting agreement of the non-redundant feature groups. The CCC of each group was assessed by averaging CCCs of QIFs in the group.Twenty-three non-redundant feature groups were created. Across all feature groups, the best inter-setting agreements (CCCs>0.8) were 1.25S vs 2.5S, 1.25L vs 2.5L, and 2.5S vs 5S; the worst (CCCs<0.51) belonged to 1.25L vs 5S and 2.5L vs 5S. Eight of the feature groups related to size, shape, and coarse texture had an average CCC>0.8 across all imaging settings.Varying degrees of inter-setting disagreements of QIFs exist when features are computed from CT images reconstructed using different algorithms and slice thicknesses. Our findings highlight the importance of harmonizing imaging acquisition for obtaining consistent QIFs to study tumor imaging phonotype. |
| first_indexed | 2024-12-12T13:34:31Z |
| format | Article |
| id | doaj.art-643eab7d9e6c48c2bb312f258b1a1c9e |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-12T13:34:31Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj.art-643eab7d9e6c48c2bb312f258b1a1c9e2022-12-22T00:22:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-011112e016655010.1371/journal.pone.0166550Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings.Lin LuRoss C EhmkeLawrence H SchwartzBinsheng ZhaoRadiomics utilizes quantitative image features (QIFs) to characterize tumor phenotype. In practice, radiological images are obtained from different vendors' equipment using various imaging acquisition settings. Our objective was to assess the inter-setting agreement of QIFs computed from CT images by varying two parameters, slice thickness and reconstruction algorithm.CT images from an IRB-approved/HIPAA-compliant study assessing thirty-two lung cancer patients were included for the analysis. Each scan's raw data were reconstructed into six imaging series using combinations of two reconstruction algorithms (Lung[L] and Standard[S]) and three slice thicknesses (1.25mm, 2.5mm and 5mm), i.e., 1.25L, 1.25S, 2.5L, 2.5S, 5L and 5S. For each imaging-setting, 89 well-defined QIFs were computed for each of the 32 tumors (one tumor per patient). The six settings led to 15 inter-setting comparisons (combinatorial pairs). To reduce QIF redundancy, hierarchical clustering was done. Concordance correlation coefficients (CCCs) were used to assess inter-setting agreement of the non-redundant feature groups. The CCC of each group was assessed by averaging CCCs of QIFs in the group.Twenty-three non-redundant feature groups were created. Across all feature groups, the best inter-setting agreements (CCCs>0.8) were 1.25S vs 2.5S, 1.25L vs 2.5L, and 2.5S vs 5S; the worst (CCCs<0.51) belonged to 1.25L vs 5S and 2.5L vs 5S. Eight of the feature groups related to size, shape, and coarse texture had an average CCC>0.8 across all imaging settings.Varying degrees of inter-setting disagreements of QIFs exist when features are computed from CT images reconstructed using different algorithms and slice thicknesses. Our findings highlight the importance of harmonizing imaging acquisition for obtaining consistent QIFs to study tumor imaging phonotype.http://europepmc.org/articles/PMC5199063?pdf=render |
| spellingShingle | Lin Lu Ross C Ehmke Lawrence H Schwartz Binsheng Zhao Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings. PLoS ONE |
| title | Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings. |
| title_full | Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings. |
| title_fullStr | Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings. |
| title_full_unstemmed | Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings. |
| title_short | Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings. |
| title_sort | assessing agreement between radiomic features computed for multiple ct imaging settings |
| url | http://europepmc.org/articles/PMC5199063?pdf=render |
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