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Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT

PURPOSE: To identify long-term reproducible texture features (TFs) of spinal computed tomography (CT), and characterize variations with regard to gender, age and vertebral level using our automated quantification framework. METHODS: We performed texture analysis (TA) on baseline and follow-up CT (fo...

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Autores principales: Dieckmeyer, Michael, Sollmann, Nico, El Husseini, Malek, Sekuboyina, Anjany, Löffler, Maximilian T., Zimmer, Claus, Kirschke, Jan S., Subburaj, Karupppasamy, Baum, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8828577/
https://www.ncbi.nlm.nih.gov/pubmed/35154004
http://dx.doi.org/10.3389/fendo.2021.792760
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author Dieckmeyer, Michael
Sollmann, Nico
El Husseini, Malek
Sekuboyina, Anjany
Löffler, Maximilian T.
Zimmer, Claus
Kirschke, Jan S.
Subburaj, Karupppasamy
Baum, Thomas
author_facet Dieckmeyer, Michael
Sollmann, Nico
El Husseini, Malek
Sekuboyina, Anjany
Löffler, Maximilian T.
Zimmer, Claus
Kirschke, Jan S.
Subburaj, Karupppasamy
Baum, Thomas
author_sort Dieckmeyer, Michael
collection PubMed
description PURPOSE: To identify long-term reproducible texture features (TFs) of spinal computed tomography (CT), and characterize variations with regard to gender, age and vertebral level using our automated quantification framework. METHODS: We performed texture analysis (TA) on baseline and follow-up CT (follow-up duration: 30–90 days) of 21 subjects (8 females, 13 males, age at baseline 61.2 ± 9.2 years) to determine long-term reproducibility. TFs with a long-term reproducibility error Δ(rel)<5% were further analyzed for an association with age and vertebral level in a cohort of 376 patients (129 females, 247 males, age 62.5 ± 9.2 years). Automated analysis comprised labeling and segmentation of vertebrae into subregions using a convolutional neural network, calculation of volumetric bone mineral density (vBMD) with asynchronous calibration and TF extraction. Variance(global) measures the spread of the gray-level distribution in an image while Entropy reflects the uniformity of gray-levels. Short-run emphasis (SRE), Long-run emphasis (LRE), Run-length non-uniformity (RLN) and Run percentage (RP) contain information on consecutive voxels of a particular grey-level, or grey-level range, in a particular direction. Long runs (LRE) represent coarse texture while short runs (SRE) represent fine texture. RLN reflects similarities in the length of runs while RP reflects distribution and homogeneity of runs with a specific direction. RESULTS: Six of the 24 extracted TFs had Δ(rel)<5% (Variance(global), Entropy, SRE, LRE, RLN, RP), and were analyzed further in 4716 thoracolumbar vertebrae. Five TFs (Variance(global),SRE,LRE, RLN,RP) showed a significant difference between genders (p<0.001), potentially being caused by a finer and more directional vertebral trabecular microstructure in females compared to males. Variance(global) and Entropy showed a significant increase from the thoracic to the lumbar spine (p<0.001), indicating a higher degree and earlier initiation of trabecular microstructure deterioration at lower spinal levels. The four higher-order TFs showed significant variations between spine regions without a clear directional gradient (p ≤ 0.001-0.012). No TF showed a clear age dependence. vBMD differed significantly between genders, age groups and spine regions (p ≤ 0.001–0.002). CONCLUSION: Long-term reproducible CT-based TFs of the thoracolumbar spine were established and characterized in a predominantly older adult study population. The gender-, age- and vertebral-level-specific values may serve as foundation for osteoporosis diagnostics and facilitate future studies investigating vertebral microstructure.
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spelling pubmed-88285772022-02-11 Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT Dieckmeyer, Michael Sollmann, Nico El Husseini, Malek Sekuboyina, Anjany Löffler, Maximilian T. Zimmer, Claus Kirschke, Jan S. Subburaj, Karupppasamy Baum, Thomas Front Endocrinol (Lausanne) Endocrinology PURPOSE: To identify long-term reproducible texture features (TFs) of spinal computed tomography (CT), and characterize variations with regard to gender, age and vertebral level using our automated quantification framework. METHODS: We performed texture analysis (TA) on baseline and follow-up CT (follow-up duration: 30–90 days) of 21 subjects (8 females, 13 males, age at baseline 61.2 ± 9.2 years) to determine long-term reproducibility. TFs with a long-term reproducibility error Δ(rel)<5% were further analyzed for an association with age and vertebral level in a cohort of 376 patients (129 females, 247 males, age 62.5 ± 9.2 years). Automated analysis comprised labeling and segmentation of vertebrae into subregions using a convolutional neural network, calculation of volumetric bone mineral density (vBMD) with asynchronous calibration and TF extraction. Variance(global) measures the spread of the gray-level distribution in an image while Entropy reflects the uniformity of gray-levels. Short-run emphasis (SRE), Long-run emphasis (LRE), Run-length non-uniformity (RLN) and Run percentage (RP) contain information on consecutive voxels of a particular grey-level, or grey-level range, in a particular direction. Long runs (LRE) represent coarse texture while short runs (SRE) represent fine texture. RLN reflects similarities in the length of runs while RP reflects distribution and homogeneity of runs with a specific direction. RESULTS: Six of the 24 extracted TFs had Δ(rel)<5% (Variance(global), Entropy, SRE, LRE, RLN, RP), and were analyzed further in 4716 thoracolumbar vertebrae. Five TFs (Variance(global),SRE,LRE, RLN,RP) showed a significant difference between genders (p<0.001), potentially being caused by a finer and more directional vertebral trabecular microstructure in females compared to males. Variance(global) and Entropy showed a significant increase from the thoracic to the lumbar spine (p<0.001), indicating a higher degree and earlier initiation of trabecular microstructure deterioration at lower spinal levels. The four higher-order TFs showed significant variations between spine regions without a clear directional gradient (p ≤ 0.001-0.012). No TF showed a clear age dependence. vBMD differed significantly between genders, age groups and spine regions (p ≤ 0.001–0.002). CONCLUSION: Long-term reproducible CT-based TFs of the thoracolumbar spine were established and characterized in a predominantly older adult study population. The gender-, age- and vertebral-level-specific values may serve as foundation for osteoporosis diagnostics and facilitate future studies investigating vertebral microstructure. Frontiers Media S.A. 2022-01-27 /pmc/articles/PMC8828577/ /pubmed/35154004 http://dx.doi.org/10.3389/fendo.2021.792760 Text en Copyright © 2022 Dieckmeyer, Sollmann, El Husseini, Sekuboyina, Löffler, Zimmer, Kirschke, Subburaj and Baum 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 Endocrinology
Dieckmeyer, Michael
Sollmann, Nico
El Husseini, Malek
Sekuboyina, Anjany
Löffler, Maximilian T.
Zimmer, Claus
Kirschke, Jan S.
Subburaj, Karupppasamy
Baum, Thomas
Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT
title Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT
title_full Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT
title_fullStr Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT
title_full_unstemmed Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT
title_short Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT
title_sort gender-, age- and region-specific characterization of vertebral bone microstructure through automated segmentation and 3d texture analysis of routine abdominal ct
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8828577/
https://www.ncbi.nlm.nih.gov/pubmed/35154004
http://dx.doi.org/10.3389/fendo.2021.792760
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