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Evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study

BACKGROUND: To investigate the influence of slice thickness on radiomic feature (RF) values in solid pulmonary nodules and evaluate the effect of a linear interpolation method in correcting the influence. METHODS: Thirty pulmonary nodules from 28 patients were selected prospectively with a thick-sli...

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Autores principales: Yang, Shouxin, Wu, Ning, Zhang, Li, Li, Meng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7944270/
https://www.ncbi.nlm.nih.gov/pubmed/33708906
http://dx.doi.org/10.21037/atm-20-2992
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author Yang, Shouxin
Wu, Ning
Zhang, Li
Li, Meng
author_facet Yang, Shouxin
Wu, Ning
Zhang, Li
Li, Meng
author_sort Yang, Shouxin
collection PubMed
description BACKGROUND: To investigate the influence of slice thickness on radiomic feature (RF) values in solid pulmonary nodules and evaluate the effect of a linear interpolation method in correcting the influence. METHODS: Thirty pulmonary nodules from 28 patients were selected prospectively with a thick-slice of 5 mm and a thin-slice of 1.25 mm on CT. A resampling method was used to normalize the voxel size of thick and thin slices CT images to 1×1×1 mm(3) by linear interpolation. Lung nodules were segmented manually. A total of 396 radiomic features (RFs) were extracted from thick-slice and thin-slice images, together with the images resampled from thick (thick-r) and thin (thin-r) slices. The differences between the RF values were evaluated using a paired t-test. A comparison between groups was made using the Chi-squared test. RESULTS: Among the 396 RFs, 305 RFs showed an intraclass correlation coefficient ≥0.75 after test-retest analysis (including 22 histogram features, 20 geometry features, and 263 texture features). In the non-resampled data, 239 RF values (78.4%, 239/305) showed significant differences between thick and thin slice CT images. Resampling of thick images revealed that 202 RF values (66.2%, 202/305) showed significant differences between thick-r and thin slice CT images, showing a significant decrease in the number of different RF values when compared to non-resampled data (P<0.01). For the RF subgroups, only texture features showed a significant reduction in the number of different RF values after resampling (P<0.01). When both thick and thin slice images were resampled, the number of significantly different RF values between thick-r and thin-r images was increased to 247 (81.0%, 247/305), showing no significant difference when compared to non-resampled data (P=0.421). CONCLUSIONS: Slice thickness demonstrated a considerable influence on RF values in solid pulmonary nodules, producing false results when CT images with different slice thicknesses were used. Linear interpolation of the resampling method was limited because of the relatively small correction effect.
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spelling pubmed-79442702021-03-10 Evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study Yang, Shouxin Wu, Ning Zhang, Li Li, Meng Ann Transl Med Original Article BACKGROUND: To investigate the influence of slice thickness on radiomic feature (RF) values in solid pulmonary nodules and evaluate the effect of a linear interpolation method in correcting the influence. METHODS: Thirty pulmonary nodules from 28 patients were selected prospectively with a thick-slice of 5 mm and a thin-slice of 1.25 mm on CT. A resampling method was used to normalize the voxel size of thick and thin slices CT images to 1×1×1 mm(3) by linear interpolation. Lung nodules were segmented manually. A total of 396 radiomic features (RFs) were extracted from thick-slice and thin-slice images, together with the images resampled from thick (thick-r) and thin (thin-r) slices. The differences between the RF values were evaluated using a paired t-test. A comparison between groups was made using the Chi-squared test. RESULTS: Among the 396 RFs, 305 RFs showed an intraclass correlation coefficient ≥0.75 after test-retest analysis (including 22 histogram features, 20 geometry features, and 263 texture features). In the non-resampled data, 239 RF values (78.4%, 239/305) showed significant differences between thick and thin slice CT images. Resampling of thick images revealed that 202 RF values (66.2%, 202/305) showed significant differences between thick-r and thin slice CT images, showing a significant decrease in the number of different RF values when compared to non-resampled data (P<0.01). For the RF subgroups, only texture features showed a significant reduction in the number of different RF values after resampling (P<0.01). When both thick and thin slice images were resampled, the number of significantly different RF values between thick-r and thin-r images was increased to 247 (81.0%, 247/305), showing no significant difference when compared to non-resampled data (P=0.421). CONCLUSIONS: Slice thickness demonstrated a considerable influence on RF values in solid pulmonary nodules, producing false results when CT images with different slice thicknesses were used. Linear interpolation of the resampling method was limited because of the relatively small correction effect. AME Publishing Company 2021-02 /pmc/articles/PMC7944270/ /pubmed/33708906 http://dx.doi.org/10.21037/atm-20-2992 Text en 2021 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Yang, Shouxin
Wu, Ning
Zhang, Li
Li, Meng
Evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study
title Evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study
title_full Evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study
title_fullStr Evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study
title_full_unstemmed Evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study
title_short Evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study
title_sort evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7944270/
https://www.ncbi.nlm.nih.gov/pubmed/33708906
http://dx.doi.org/10.21037/atm-20-2992
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