Parametric response mapping of CT images provides early detection of local bone loss in a rat model of osteoporosis

Abstract Loss of bone mass due to disease, such as osteoporosis and metastatic cancer to the bone, is a leading cause of orthopedic complications and hospitalization. Onset of bone loss resulting from disease increases the risk of incurring fractures and subsequent pain, increasing medical expenses...

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Published in:Bone (New York, N.Y.) Vol. 51; no. 1; pp. 78 - 84
Main Authors: Hoff, Benjamin A, Kozloff, Kenneth M, Boes, Jennifer L, Brisset, Jean-Christophe, Galbán, Stefanie, Van Poznak, Catherine H, Jacobson, Jon A, Johnson, Timothy D, Meyer, Charles R, Rehemtulla, Alnawaz, Ross, Brian D, Galbán, Craig J
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 01-07-2012
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Abstract Abstract Loss of bone mass due to disease, such as osteoporosis and metastatic cancer to the bone, is a leading cause of orthopedic complications and hospitalization. Onset of bone loss resulting from disease increases the risk of incurring fractures and subsequent pain, increasing medical expenses while reducing quality of life. Although current standard CT-based protocols provide adequate prognostic information for assessing bone loss, many of the techniques for evaluating CT scans rely on measures based on whole-bone summary statistics. This reduces the sensitivity at identifying local regions of bone resorption, as well as formation. In this study, we evaluate the effectiveness of a voxel-based image post-processing technique, called the Parametric Response Map (PRM), for identifying local changes in bone mass in weight-bearing bones on CT scans using an established animal model of osteoporosis. Serial CT scans were evaluated weekly using PRM subsequent to ovariectomy or sham surgeries over the period of one month. For comparison, bone volume fraction and mineral density measurements were acquired and found to significantly differ between groups starting 3 weeks post-surgery. High resolution ex vivo measurements acquired four weeks post-surgery validated the extent of bone loss in the surgical groups. In contrast to standard methodologies for assessing bone loss, PRM results were capable of identifying local decreases in bone mineral by week 2, which were found to be significant between groups. This study concludes that PRM is able to detect changes in bone mineral with higher sensitivity and spatial differentiation than conventional techniques for evaluating CT scans, which may aid in clinical decision making for patients suffering from bone loss.
AbstractList Abstract Loss of bone mass due to disease, such as osteoporosis and metastatic cancer to the bone, is a leading cause of orthopedic complications and hospitalization. Onset of bone loss resulting from disease increases the risk of incurring fractures and subsequent pain, increasing medical expenses while reducing quality of life. Although current standard CT-based protocols provide adequate prognostic information for assessing bone loss, many of the techniques for evaluating CT scans rely on measures based on whole-bone summary statistics. This reduces the sensitivity at identifying local regions of bone resorption, as well as formation. In this study, we evaluate the effectiveness of a voxel-based image post-processing technique, called the Parametric Response Map (PRM), for identifying local changes in bone mass in weight-bearing bones on CT scans using an established animal model of osteoporosis. Serial CT scans were evaluated weekly using PRM subsequent to ovariectomy or sham surgeries over the period of one month. For comparison, bone volume fraction and mineral density measurements were acquired and found to significantly differ between groups starting 3 weeks post-surgery. High resolution ex vivo measurements acquired four weeks post-surgery validated the extent of bone loss in the surgical groups. In contrast to standard methodologies for assessing bone loss, PRM results were capable of identifying local decreases in bone mineral by week 2, which were found to be significant between groups. This study concludes that PRM is able to detect changes in bone mineral with higher sensitivity and spatial differentiation than conventional techniques for evaluating CT scans, which may aid in clinical decision making for patients suffering from bone loss.
Loss of bone mass due to disease, such as osteoporosis and metastatic cancer to the bone, is a leading cause of orthopedic complications and hospitalization. Onset of bone loss resulting from disease increases the risk of incurring fractures and subsequent pain, increasing medical expenses while reducing quality of life. Although current standard CT-based protocols provide adequate prognostic information for assessing bone loss, many of the techniques for evaluating CT scans rely on measures based on whole-bone summary statistics. This reduces the sensitivity at identifying local regions of bone resorption, as well as formation. In this study, we evaluate the effectiveness of a voxel-based image post-processing technique, called the Parametric Response Map (PRM), for identifying local changes in bone mass in weight-bearing bones on CT scans using an established animal model of osteoporosis. Serial CT scans were evaluated weekly using PRM subsequent to ovariectomy or sham surgeries over the period of one month. For comparison, bone volume fraction and mineral density measurements were acquired and found to significantly differ between groups starting 3 weeks post-surgery. High resolution ex vivo measurements acquired four weeks post-surgery validated the extent of bone loss in the surgical groups. In contrast to standard methodologies for assessing bone loss, PRM results were capable of identifying local decreases in bone mineral by week 2, which were found to be significant between groups. This study concludes that PRM is able to detect changes in bone mineral with higher sensitivity and spatial differentiation than conventional techniques for evaluating CT scans, which may aid in clinical decision making for patients suffering from bone loss.
Loss of bone mass due to disease, such as osteoporosis and metastatic cancer to the bone, is a leading cause of orthopedic complications and hospitalization. Onset of bone loss resulting from disease increases the risk of incurring fractures and subsequent pain, increasing medical expenses while reducing quality of life. Although current standard CT-based protocols provide adequate prognostic information for assessing bone loss, many of the techniques for evaluating CT scans rely on measures based on whole-bone summary statistics. This reduces the sensitivity at identifying local regions of bone resorption, as well as formation. In this study, we evaluate the effectiveness of a voxel-based image post-processing technique, called the Parametric Response Map (PRM), for identifying local changes in bone mass in weight-bearing bones on CT scans using an established animal model of osteoporosis. Serial CT scans were evaluated weekly using PRM subsequent to ovariectomy or sham surgeries over the period of one month. For comparison, bone volume fraction and mineral density measurements were acquired and found to significantly differ between groups starting 3weeks post-surgery. High resolution ex vivo measurements acquired four weeks post-surgery validated the extent of bone loss in the surgical groups. In contrast to standard methodologies for assessing bone loss, PRM results were capable of identifying local decreases in bone mineral by week 2, which were found to be significant between groups. This study concludes that PRM is able to detect changes in bone mineral with higher sensitivity and spatial differentiation than conventional techniques for evaluating CT scans, which may aid in clinical decision making for patients suffering from bone loss. ► PRM was an early biomarker of bone remodeling in a rat model of osteoporosis. ► Bone loss identified by PRM preceded changes observed by standard CT based metrics. ► PRM detected cancellous bone loss and cortical expansion within 2weeks. ► PRM provides a higher sensitivity and spatial context in detecting bone changes.
Loss of bone mass due to disease, such as osteoporosis and metastatic cancer to the bone, is a leading cause of orthopedic complications and hospitalization. Onset of bone loss resulting from disease increases the risk of incurring fractures and subsequent pain, increasing medical expenses while reducing quality of life. Although current standard CT-based protocols provide adequate prognostic information for assessing bone loss, many of the techniques for evaluating CT scans rely on measures based on whole-bone summary statistics. This reduces the sensitivity at identifying local regions of bone resorption, as well as formation. In this study, we evaluate the effectiveness of a voxel-based image post-processing technique, called the Parametric Response Map (PRM), for identifying local changes in bone mass in weight-bearing bones on CT scans using an established animal model of osteoporosis. Serial CT scans were evaluated weekly using PRM subsequent to ovariectomy or sham surgeries over the period of one month. For comparison, bone volume fraction and mineral density measurements were acquired and found to significantly differ between groups starting 3weeks post-surgery. High resolution ex vivo measurements acquired four weeks post-surgery validated the extent of bone loss in the surgical groups. In contrast to standard methodologies for assessing bone loss, PRM results were capable of identifying local decreases in bone mineral by week 2, which were found to be significant between groups. This study concludes that PRM is able to detect changes in bone mineral with higher sensitivity and spatial differentiation than conventional techniques for evaluating CT scans, which may aid in clinical decision making for patients suffering from bone loss.
Loss of bone mass due to disease, such as osteoporosis and metastatic cancer to the bone, is a leading cause of orthopedic complications and hospitalization. Onset of bone loss resulting from disease increases the risk of incurring fractures and subsequent pain, increasing medical expenses while reducing quality of life. Although current standard CT-based protocols provide adequate prognostic information for assessing bone loss, many of the techniques for evaluating CT scans rely on measures based on whole-bone summary statistics. This reduces the sensitivity at identifying local regions of bone resorption, as well as formation. In this study, we evaluate the effectiveness of a voxel-based image post-processing technique, called the Parametric Response Map (PRM), for identifying local changes in bone mass in weight-bearing bones on CT scans using an established animal model of osteoporosis. Serial CT scans were evaluated weekly using PRM subsequent to ovariectomy or sham surgeries over the period of one month. For comparison, bone volume fraction and mineral density measurements were acquired and found to significantly differ between groups starting 3 weeks post-surgery. High resolution ex vivo measurements acquired four weeks post-surgery validated the extent of bone loss in the surgical groups. In contrast to standard methodologies for assessing bone loss, PRM results were capable of identifying local decreases in bone mineral by week 2, which were found to be significant between groups. This study concludes that PRM is able to detect changes in bone mineral with higher sensitivity and spatial differentiation than conventional techniques for evaluating CT scans, which may aid in clinical decision making for patients suffering from bone loss.
Author Galbán, Stefanie
Johnson, Timothy D
Ross, Brian D
Van Poznak, Catherine H
Kozloff, Kenneth M
Jacobson, Jon A
Rehemtulla, Alnawaz
Hoff, Benjamin A
Boes, Jennifer L
Meyer, Charles R
Brisset, Jean-Christophe
Galbán, Craig J
AuthorAffiliation 2 Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, Michigan, 48109, USA
5 Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, 48109, USA
3 Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, 48109, USA
4 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, 48109, USA
1 Department of Radiology, University of Michigan, Ann Arbor, Michigan, 48109, USA
AuthorAffiliation_xml – name: 1 Department of Radiology, University of Michigan, Ann Arbor, Michigan, 48109, USA
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– name: 4 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, 48109, USA
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Issue 1
Keywords Response
Osteoporosis
CT
Biomarker
Ovariectomy
Imaging
Cartography
Radiodiagnosis
Rat
Diseases of the osteoarticular system
Rodentia
Biological marker
Vertebrata
Mammalia
Treatment
Surgery
Animal
Morphology
Medical imagery
Models
Computerized axial tomography
Osteopenia
Language English
License CC BY 4.0
Copyright © 2012 Elsevier Inc. All rights reserved.
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Snippet Abstract Loss of bone mass due to disease, such as osteoporosis and metastatic cancer to the bone, is a leading cause of orthopedic complications and...
Loss of bone mass due to disease, such as osteoporosis and metastatic cancer to the bone, is a leading cause of orthopedic complications and hospitalization....
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SubjectTerms Animals
Biological and medical sciences
Biomarker
Bone Density - physiology
Diseases of the osteoarticular system
Female
Fundamental and applied biological sciences. Psychology
Imaging
Medical sciences
Orthopedics
Osteoporosis
Osteoporosis - diagnosis
Osteoporosis - diagnostic imaging
Osteoporosis. Osteomalacia. Paget disease
Ovariectomy
Radiography
Rats
Rats, Sprague-Dawley
Response
Surgery (general aspects). Transplantations, organ and tissue grafts. Graft diseases
Surgery of the genital tract and mammary gland
Vertebrates: anatomy and physiology, studies on body, several organs or systems
Title Parametric response mapping of CT images provides early detection of local bone loss in a rat model of osteoporosis
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https://dx.doi.org/10.1016/j.bone.2012.04.005
https://www.ncbi.nlm.nih.gov/pubmed/22542461
https://search.proquest.com/docview/1019619958
https://search.proquest.com/docview/1635043219
https://pubmed.ncbi.nlm.nih.gov/PMC3371150
Volume 51
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