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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...

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Autores principales: Xue, Gongbo, Liu, Hongyan, Cai, Xiaoyi, Zhang, Zhen, Zhang, Shuai, Liu, Ling, Hu, Bin, Wang, Guohua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
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.
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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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