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Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors
OBJECTIVE: To evaluate the impact of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) on abdominal CT radiomic features acquired in portal venous phase in liver tumor patients. METHODS: Sixty patients with liver tumors who underwent contrast-en...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113560/ https://www.ncbi.nlm.nih.gov/pubmed/37091167 http://dx.doi.org/10.3389/fonc.2023.1167745 |
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author | Xue, Gongbo Liu, Hongyan Cai, Xiaoyi Zhang, Zhen Zhang, Shuai Liu, Ling Hu, Bin Wang, Guohua |
author_facet | Xue, Gongbo Liu, Hongyan Cai, Xiaoyi Zhang, Zhen Zhang, Shuai Liu, Ling Hu, Bin Wang, Guohua |
author_sort | Xue, Gongbo |
collection | PubMed |
description | OBJECTIVE: To evaluate the impact of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) on abdominal CT radiomic features acquired in portal venous phase in liver tumor patients. METHODS: Sixty patients with liver tumors who underwent contrast-enhanced abdominal CT were retrospectively enrolled. Six groups including filtered back projection (FBP), ASIR-V (30%, 70%) and DLIR at low (DLIR-L), medium (DLIR-M and high (DLIR-H), were reconstructed using portal venous phase data. CT-based radiomic features (first-order, texture and wavelet features) were extracted from 2D and 3D liver tumors, peritumor and liver parenchyma. All features were analyzed for comparison. P < 0.05 indicated statistically different. The consistency of 3D lesion feature extraction was assessed by calculating intraclass correlation coefficient (ICC). RESULTS: Different reconstruction algorithms influenced most radiomic features. The percentages of first-order, texture and wavelet features without statistical difference among 2D and 3D lesions, peritumor and liver parenchyma for all six groups were 27.78% (5/18), 5.33% (4/75) and 5.56% (1/18), respectively (all p > 0.05), and they decreased while the level of reconstruction strengthened for both ASIR-V and DLIR. Compared with FBP, the features of ASIR-V30% and 70% without statistical difference decreased from 71.31% to 23.95%, and DLIR-L, DLIR-M, and DLIR-H decreased from 31.65% to 27.11% and 23.73%. Among texture features, unaffected features of peritumor were larger than those of lesions and liver parenchyma, and unaffected 3D lesions features were larger than those of 2D lesions. The consistency of 3D lesion first-order features was excellent, with intra- and inter-observer ICCs ranging from 0.891 to 0.999 and 0.880 to 0.998. CONCLUSIONS: Both ASIR-V and DLIR algorithms with different strengths influenced the radiomic features of abdominal CT images in portal venous phase, and the influences aggravated as reconstruction strength increased. |
format | Online Article Text |
id | pubmed-10113560 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101135602023-04-20 Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors Xue, Gongbo Liu, Hongyan Cai, Xiaoyi Zhang, Zhen Zhang, Shuai Liu, Ling Hu, Bin Wang, Guohua Front Oncol Oncology OBJECTIVE: To evaluate the impact of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) on abdominal CT radiomic features acquired in portal venous phase in liver tumor patients. METHODS: Sixty patients with liver tumors who underwent contrast-enhanced abdominal CT were retrospectively enrolled. Six groups including filtered back projection (FBP), ASIR-V (30%, 70%) and DLIR at low (DLIR-L), medium (DLIR-M and high (DLIR-H), were reconstructed using portal venous phase data. CT-based radiomic features (first-order, texture and wavelet features) were extracted from 2D and 3D liver tumors, peritumor and liver parenchyma. All features were analyzed for comparison. P < 0.05 indicated statistically different. The consistency of 3D lesion feature extraction was assessed by calculating intraclass correlation coefficient (ICC). RESULTS: Different reconstruction algorithms influenced most radiomic features. The percentages of first-order, texture and wavelet features without statistical difference among 2D and 3D lesions, peritumor and liver parenchyma for all six groups were 27.78% (5/18), 5.33% (4/75) and 5.56% (1/18), respectively (all p > 0.05), and they decreased while the level of reconstruction strengthened for both ASIR-V and DLIR. Compared with FBP, the features of ASIR-V30% and 70% without statistical difference decreased from 71.31% to 23.95%, and DLIR-L, DLIR-M, and DLIR-H decreased from 31.65% to 27.11% and 23.73%. Among texture features, unaffected features of peritumor were larger than those of lesions and liver parenchyma, and unaffected 3D lesions features were larger than those of 2D lesions. The consistency of 3D lesion first-order features was excellent, with intra- and inter-observer ICCs ranging from 0.891 to 0.999 and 0.880 to 0.998. CONCLUSIONS: Both ASIR-V and DLIR algorithms with different strengths influenced the radiomic features of abdominal CT images in portal venous phase, and the influences aggravated as reconstruction strength increased. Frontiers Media S.A. 2023-04-05 /pmc/articles/PMC10113560/ /pubmed/37091167 http://dx.doi.org/10.3389/fonc.2023.1167745 Text en Copyright © 2023 Xue, Liu, Cai, Zhang, Zhang, Liu, Hu and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Xue, Gongbo Liu, Hongyan Cai, Xiaoyi Zhang, Zhen Zhang, Shuai Liu, Ling Hu, Bin Wang, Guohua Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors |
title | Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors |
title_full | Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors |
title_fullStr | Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors |
title_full_unstemmed | Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors |
title_short | Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors |
title_sort | impact of deep learning image reconstruction algorithms on ct radiomic features in patients with liver tumors |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113560/ https://www.ncbi.nlm.nih.gov/pubmed/37091167 http://dx.doi.org/10.3389/fonc.2023.1167745 |
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