Texture Analysis for Postmenopausal Osteoporosis

Osteoporosis Clinical Trial

Official title:

Changes in Bone Density, Radiographic Texture Analysis and Bone Turnover During Two Years of Antiresorptive Therapy for Postmenopausal Osteoporosis

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT00145977
• Other study ID #: IRB# 11009B
• Secondary ID: NIH AR42739NIH A
• Status: Completed
• Phase: N/A
• Start date: July 2001
• Est. completion date: December 2009

Study information

• Verified date: August 2018
• Source: University of Chicago
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The purpose of this study is to determine if a new test for osteoporosis can be useful in monitoring treatment. We are studying a new method for examining the quality of bone by an experimental method of computerized analysis of radiographic images (x-ray pictures) of the heel.

Description:

The study proposed in this application is a part of a larger project entitled "Clinical utility of radiographic texture analysis in diagnosing and treating osteoporosis". The overall goal of the larger project is to determine whether computerized texture analysis of digitized high-resolution images of trabecular bone (texture analysis) improves our ability to diagnose bone fragility and follow natural history and/or response to pharmacological therapy of osteoporosis. In the study proposed here we plan to examine changes in the results of texture analysis during two years of pharmacological therapy for osteoporosis.

Role of densitometry in osteoporosis:

Measurement of bone mineral density is the principal diagnostic method used in clinical practice and in research studies, both to identify patients who have the disease and to follow their response to therapeutic agents. The technique used most widely is dual-energy X-ray absorptiometry (DXA), which has advantages of low cost and radiation exposure, and high precision and accuracy of 1-2% and 4-8%, respectively [Garner, 1996 and Melton, 1990]. Based on the association between the low BMD and increased risk of fracture, BMD-based treatment guidelines have been developed [Melton, 1993 and National Osteoporosis Foundation, 1999]. There is, however, a considerable overlap between BMD of patients who sustain fragility fractures and those who do not [Cummings, 1993; Marshall, 1996; Melton, 1989; Ross, 1990 and Wasnich, 1990]. The problem arises because the fragility is determined not only by the quantity of the bone (measured as bone density), but also by its "quality" which is believed to be related to the preservation of the normal trabecular pattern [Parfitt, 1987]. Bone quality is not specifically assessed using current diagnostic methods. Information about bone quality, however, would be of substantial clinical and scientific value, as it would complement the BMD measurement when selecting patients for therapy and when studying bone loss or assessing effects of therapeutic agents.

Texture analysis:

A novel approach to noninvasive and practical assessment of bone structure is to analyze the texture of high resolution radiographs of trabecular bone [Link, 1999]. Dr. Giger has developed a method for characterizing bone structure by computerized texture analysis of digitized high-resolution radiographs [Jiang, 1999; Caligiuri, 1993; Caligiuri, 1994; Chinander, 1999 and Chinander, 2000]. In this approach, the texture is analyzed in several ways, including Fourier based analysis, which yields root mean square (RMS) as a measure of magnitude of trabecular bone texture pattern, and the first moment of power spectrum (FMP) which characterizes the texture pattern's frequency; and Minkowski dimension fractal analysis [Caligiuri, 1993; Chinander, 1999; Chinander, 2000; Benhamou, 1994; Jiang, 1999; Majumdar, 1993 and Maragos, 1994]. Radiographic texture analysis has been studied in vivo, on lumbar spine radiographs and found to predict presence of vertebral fractures elsewhere in the spine more reliably than did the BMD of the spine [Caligiuri, 1993 and Caligiuri, 1994;]. In addition, in an in vitro study texture features as well as BMD were analyzed in femoral neck specimens obtained during surgical hip replacement. Mechanical loading (crush test) was then performed on cubes of trabecular bone machined from these specimens to determine their bone strength. It was found that the combination of BMD and texture analysis predicted bone strength better than BMD alone [Jiang, 1999; Chinander, 1999 and Chinander, 2000].

Biochemical markers of bone turnover:

In studies of osteoporosis, the bone mass is assessed by measuring BMD while the metabolic activity of the bone is assessed by measuring the biochemical markers of bone turnover [Looker, 2000]. These markers have limited utility in individual patients because they have high within-person variability (low precision), and because it is not clear which markers are useful in which clinical situation [Looker, 2000 and Bauer, 1999]. In contrast, comparing biochemical markers between groups of patients in clinical studies has been found to be useful in two settings. Firstly, it has been found that high levels of biochemical markers of bone resorption predict fractures independent of BMD [Garnero, 1996 and van Daele, 1996]. Secondly, early changes in bone markers (at 3-6 months) during anti-resorptive therapy predict later changes in BMD and fracture rates [Ravin, 1999; Greenspan, 1998; Chesnut, 1997 and Bjarnason, 1997]. The mechanisms underlying these observations have not been elucidated to date. It is speculated that increased bone resorption, which is reflected in elevation of biochemical markers of bone turnover, increases fragility by weakening trabecular structure prior to or independent of measurable BMD changes. Similarly, decreased bone resorption during pharmacological therapy is likely to improve the trabecular structure before or independent of its effects on BMD. Since the aim of our research is to (indirectly) examine the trabecular structure by performing the radiographic texture analysis, we plan to determine whether the changes in biochemical markers of bone turnover during antiresorptive therapy will correlate with changes in the results of texture analysis.

Rationale for the study:

Anti-resorptive therapy reduces bone fragility and increases bone density. It is likely that the trabecular structure of the bone also changes during treatment. Peripheral densitometry has not been used so far to monitor response to therapy. If the combination of texture analysis and peripheral BMD change reproducibly during treatment it may be possible to employ this combination to monitor therapeutic response. In so doing, one could avoid the need to use the central densitometry and biochemical markers of bone turnover since the former is cumbersome while the latter suffers from low precision.

Potential advantages of using a portable peripheral densitometer: The texture analyses described above were developed for high-resolution radiographs, which were digitized and subjected to computer analysis. The new DXA imaging systems such as GE/Lunar PIXI which will be used in our research, provide digital images with resolution sufficient for computerized texture analysis (200 micron pixels). Furthermore, PIXI can generate the image in a shorter time (seconds vs. minutes) and at a fraction of radiation dose of conventional radiographs. Finally, since this is a portable densitometer, the methodology developed in this proposal has the potential to be widely applicable to large segments of the population, including frail elderly who have limited mobility and high prevalence of osteoporosis.

STUDY PROCEDURES

The studies will be performed in the outpatient facility of the University of Chicago. Every 3 months for the first 6 months and every 6 months for the remainder of 2 years, the subjects will come in the morning in the fasting state, provide a urine sample (second morning void) and blood sample for measurement of biochemical markers of bone turnover. Height and weight will be recorded at each visit, and any change in health status, including fractures ascertained. We will also assess other factors known to influence bone turnover, such as diet and physical activity. Every 12 months, the subjects will fill out Block food frequency questionnaire from Berkley Nutrition Services. In addition, every 6 months they will fill out a calcium intake questionnaire, which will be analyzed by the nutritionist and a short physical activity questionnaire, which was used in PEPI trial for assessment of physical activity. Medication compliance will be assessed by questioning the patients and counting the number of calcium and alendronate tablets remaining from the previous visit.

After these tests are completed, the subjects will go to the densitometry suite of the Endocrinology clinic where BMD will be measured and heel images obtained for texture analysis. The left heel will be scanned twice using the PIXI densitometer (GE/Lunar corporation) for measurement of BMD of the heel and texture analysis. (If there is a deformity of the left heel, right heel will be used for all examinations.) In addition, every 6 months, BMD of the lumbar spine and proximal femur will be measured using the central densitometer Prodigy (GE/Lunar corporation). The same instrument will be used for lateral vertebral assessment (a method used for detecting vertebral deformities on images of the lateral spine from the densitometer), which will be performed every 12 months.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 36
• Est. completion date: December 2009
• Est. primary completion date: December 2009
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 59 Years and older

Eligibility

Inclusion Criteria:

• The study will enroll 40 postmenopausal women with a T score < -2 either at the lumbar spine or the femoral neck: 20 who decide to begin anti-resorptive therapy (treated group), and 20 women who decline such therapy (control group). We will attempt to match the patients and the controls for T score (within 0.3) and age (within 5 years).

• All study participants will be:

• at least 3 years past the last menstrual period,

• not on HRT, Raloxifene or calcitonin for at least 6 months.

Exclusion Criteria:

• All study participants will not be on bisphosphonates during the previous 12 months.

• Women with secondary causes of osteoporosis will be excluded.

Related Conditions & MeSH terms

• Bone Diseases, Metabolic
• Osteopenia
• Osteoporosis
• Osteoporosis, Postmenopausal

Intervention

Drug:
• Alendronate
alendronate 70 mg once weekly

Dietary Supplement:
• Calcium Citrate
600 mg of calcium citrate
• Vitamin D
500 mg of Vitamin D consumed with the evening meal.

Locations

Country	Name	City	State
United States	The University of Chicago	Chicago	Illinois

Sponsors (3)

Lead Sponsor	Collaborator
University of Chicago	National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), National Institutes of Health (NIH)

Country where clinical trial is conducted

United States, 

References & Publications (28)

Bauer DC, Sklarin PM, Stone KL, Black DM, Nevitt MC, Ensrud KE, Arnaud CD, Genant HK, Garnero P, Delmas PD, Lawaetz H, Cummings SR. Biochemical markers of bone turnover and prediction of hip bone loss in older women: the study of osteoporotic fractures. J Bone Miner Res. 1999 Aug;14(8):1404-10. — View Citation

Benhamou CL, Lespessailles E, Jacquet G, Harba R, Jennane R, Loussot T, Tourliere D, Ohley W. Fractal organization of trabecular bone images on calcaneus radiographs. J Bone Miner Res. 1994 Dec;9(12):1909-18. — View Citation

Bjarnason NH, Bjarnason K, Hassager C, Christiansen C. The response in spinal bone mass to tibolone treatment is related to bone turnover in elderly women. Bone. 1997 Feb;20(2):151-5. — View Citation

Caligiuri P, Giger ML, Favus M. Multifractal radiographic analysis of osteoporosis. Med Phys. 1994 Apr;21(4):503-8. — View Citation

Caligiuri P, Giger ML, Favus MJ, Jia H, Doi K, Dixon LB. Computerized radiographic analysis of osteoporosis: preliminary evaluation. Radiology. 1993 Feb;186(2):471-4. — View Citation

Chesnut CH 3rd, Bell NH, Clark GS, Drinkwater BL, English SC, Johnson CC Jr, Notelovitz M, Rosen C, Cain DF, Flessland KA, Mallinak NJ. Hormone replacement therapy in postmenopausal women: urinary N-telopeptide of type I collagen monitors therapeutic effect and predicts response of bone mineral density. Am J Med. 1997 Jan;102(1):29-37. — View Citation

Chinander MR, Giger ML, Martell JM, Favus MJ. Computerized analysis of radiographic bone patterns: effect of imaging conditions on performance. Med Phys. 2000 Jan;27(1):75-85. — View Citation

Chinander MR, Giger ML, Martell JM, Jiang C, Favus MJ. Computerized radiographic texture measures for characterizing bone strength: a simulated clinical setup using femoral neck specimens. Med Phys. 1999 Nov;26(11):2295-300. — View Citation

Cummings SR, Black DM, Nevitt MC, Browner W, Cauley J, Ensrud K, Genant HK, Palermo L, Scott J, Vogt TM. Bone density at various sites for prediction of hip fractures. The Study of Osteoporotic Fractures Research Group. Lancet. 1993 Jan 9;341(8837):72-5. — View Citation

Garnero P, Hausherr E, Chapuy MC, Marcelli C, Grandjean H, Muller C, Cormier C, Bréart G, Meunier PJ, Delmas PD. Markers of bone resorption predict hip fracture in elderly women: the EPIDOS Prospective Study. J Bone Miner Res. 1996 Oct;11(10):1531-8. — View Citation

Genant HK, Engelke K, Fuerst T, Glüer CC, Grampp S, Harris ST, Jergas M, Lang T, Lu Y, Majumdar S, Mathur A, Takada M. Noninvasive assessment of bone mineral and structure: state of the art. J Bone Miner Res. 1996 Jun;11(6):707-30. Review. — View Citation

Greenspan SL, Parker RA, Ferguson L, Rosen HN, Maitland-Ramsey L, Karpf DB. Early changes in biochemical markers of bone turnover predict the long-term response to alendronate therapy in representative elderly women: a randomized clinical trial. J Bone Miner Res. 1998 Sep;13(9):1431-8. — View Citation

Jiang C, Giger ML, Chinander MR, Martell JM, Kwak S, Favus MJ. Characterization of bone quality using computer-extracted radiographic features. Med Phys. 1999 Jun;26(6):872-9. — View Citation

Jiang C, Pitt RE, Bertram JE, Aneshansley DJ. Fractal-based image texture analysis of trabecular bone architecture. Med Biol Eng Comput. 1999 Jul;37(4):413-8. — View Citation

Link TM, Majumdar S, Grampp S, Guglielmi G, van Kuijk C, Imhof H, Glueer C, Adams JE. Imaging of trabecular bone structure in osteoporosis. Eur Radiol. 1999;9(9):1781-8. Review. — View Citation

Looker AC, Bauer DC, Chesnut CH 3rd, Gundberg CM, Hochberg MC, Klee G, Kleerekoper M, Watts NB, Bell NH. Clinical use of biochemical markers of bone remodeling: current status and future directions. Osteoporos Int. 2000;11(6):467-80. Review. — View Citation

Majumdar S, Weinstein RS, Prasad RR. Application of fractal geometry techniques to the study of trabecular bone. Med Phys. 1993 Nov-Dec;20(6):1611-9. Erratum in: Med Phys 1994 Mar;21(3):491. — View Citation

Maragos, P., Fractal signal analysis using mathematicl morphology. Advances in Electronics and Electron Physics, 1994. 88: p. 199-246.

Marshall D, Johnell O, Wedel H. Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ. 1996 May 18;312(7041):1254-9. — View Citation

Melton LJ 3rd, Atkinson EJ, O'Fallon WM, Wahner HW, Riggs BL. Long-term fracture prediction by bone mineral assessed at different skeletal sites. J Bone Miner Res. 1993 Oct;8(10):1227-33. — View Citation

Melton LJ 3rd, Eddy DM, Johnston CC Jr. Screening for osteoporosis. Ann Intern Med. 1990 Apr 1;112(7):516-28. Review. — View Citation

Melton LJ 3rd, Kan SH, Frye MA, Wahner HW, O'Fallon WM, Riggs BL. Epidemiology of vertebral fractures in women. Am J Epidemiol. 1989 May;129(5):1000-11. — View Citation

National Osteoporosis Foundation: Physicians guide to prevention and treatment of osteoporosis, . 1999, Exerpta Medica, Inc.: Belle Mead, New Jersey.

Parfitt AM. Trabecular bone architecture in the pathogenesis and prevention of fracture. Am J Med. 1987 Jan 26;82(1B):68-72. Review. — View Citation

Ravn P, Clemmesen B, Christiansen C. Biochemical markers can predict the response in bone mass during alendronate treatment in early postmenopausal women. Alendronate Osteoporosis Prevention Study Group. Bone. 1999 Mar;24(3):237-44. — View Citation

Ross PD, Wasnich RD, Davis JW. Fracture prediction models for osteoporosis prevention. Bone. 1990;11(5):327-31. Review. — View Citation

van Daele PL, Seibel MJ, Burger H, Hofman A, Grobbee DE, van Leeuwen JP, Birkenhäger JC, Pols HA. Case-control analysis of bone resorption markers, disability, and hip fracture risk: the Rotterdam study. BMJ. 1996 Feb 24;312(7029):482-3. — View Citation

Wasnich RD, Davis JW, Ross PD. Appropriate clinical application of bone density measurements. J Am Med Womens Assoc (1972). 1990 May-Jun;45(3):99-102. — View Citation

* Note: There are 28 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Changes in Lumbar Spine BMD +/- Treatment With Alendronate	Percent Change in lumbar spine BMD from Baseline to Month 24	Baseline to Month 24
Secondary	Changes in Peripheral Heel BMD +/- Treatment With Alendronate	Percent Change in peripheral heel BMD from Baseline to Month 24	Baseline to Month 24
Secondary	Changes in Femoral Neck BMD +/- Treatment With Alendronate	Percent Change in femoral neck BMD from Baseline to Month 24	Baseline to Month 24
Secondary	Changes in Total Hip BMD +/- Treatment With Alendronate	Percent Change in total hip BMD from Baseline to Month 24	Baseline to Month 24
Secondary	Changes in Radiographic Texture Analysis (RTA) Integrated Root Mean Square (iRMS) From Baseline to Month 24	Root Mean Square (RMS) is a measure of the variability in the radiographic texture pattern, the relative difference in the contrast between light and dark areas is expressed in a grayscale level. In practical terms, a bone image with a washed-out appearance due to loss of trabecular structure such as that seen in osteoporosis, will have a low value for RMS because there will be relatively little contrast between lighter and darker areas of the image. An image of a bone with strong trabecular structure will have a high RMS value because the contrast between the lighter and darker areas of the image will be greater.; To derive a measure of variability in the RMS in the region of interest in the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals, and RMS is calculated for each segment. The iRMS (integrated RMS) roughly corresponds to RMS averaged across all 24 angular sectors	Baseline to Month 24
Secondary	Changes in Radiographic Texture Analysis (RTA) Feature Standard Deviation of Root Mean Square (sdRMS) From Baseline to Month 24	Root Mean Square (RMS) is a measure of the variability in the radiographic texture pattern, the relative difference in the contrast between light and dark areas is expressed in a grayscale level. In practical terms, a bone image with a washed-out appearance due to loss of trabecular structure such as that seen in osteoporosis, will have a low value for RMS because there will be relatively little contrast between lighter and darker areas of the image. An image of a bone with strong trabecular structure will have a high RMS value because the contrast between the lighter and darker areas of the image will be greater.; To derive a measure of variability in the RMS in the region of interest of the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals, and RMS is calculated for each segment. We use sdRMS (standard deviation of the RMS across the segments) as a measure of the direction dependence (anisotropy) of the trabeculae in the bone image.	Baseline to Month 24
Secondary	Changes in Radiographic Texture Analysis (RTA) Feature Integrated First Moment of the Power Spectrum (iFMP) From Baseline to Month 24	To derive a measure of variability and directionality in the first moment of the power spectrum (FMP) in the region of interest of the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals, and FMP is calculated for each segment. We use iFMP (integrated FMP) as a measure of overall special frequency of the radiographic pattern. FMP characterizes spatial frequency in the radiographic pattern and the underlying trabecular structure. This corresponds to the coarseness or fineness of the radiographic texture pattern. A high level of FMP indicates thin and closely spaced trabecular structure. Low FMP indicates widely spaced dark areas usually corresponding to a strong, thick trabecular structure.	Baseline to Month 24
Secondary	Changes in Radiographic Texture Analysis (RTA) Minimum First Moment of the Power Spectrum (minFMP) From Baseline to Month 24	To derive a measure of variability and directionality in the first moment of the power spectrum (FMP) in the region of interest of the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals and FMP is calculated for each segment. We use minFMP (minimum FMP) to represent the lowest value of FMP across the 24 angular sectors corresponding to the special frequency in the most washed-out direction. FMP characterizes spatial frequency in the radiographic pattern and the underlying trabecular structure. This corresponds to the coarseness or fineness of the radiographic texture pattern. A high level of FMP indicates thin and closely spaced trabecular structure. Low FMP indicates widely spaced dark areas usually corresponding to a strong, thick trabecular structure.	Baseline to Month 24
Secondary	Changes in Radiographic Texture Analysis (RTA) Minkowski Fractal Dimension (MINK) From Baseline to Month 24	The Percent Change in Radiographic Texture Analysis (RTA) Minkowski Fractal Dimension (MINK) from Baseline to Month 24 is a description of the similarity of texture of the images at different magnifications. The Minkowski fractal dimension is calculated from the slope of the least -square fitted line relating log volume and log magnification.	Baseline to Month 24
Secondary	Changes in Radiographic Texture Analysis (RTA) Spectral Density Coefficient Beta (BETA) From Baseline to Month 24	The Percent Change in Radiographic Texture Analysis (RTA) spectral density coefficient beta (BETA) from Baseline to Month 24 is an analysis of spectral density vs. the spacial frequency on a log-log plot. BETA is the coefficient (slope) of this plot. Higher values of beta correspond to rougher (strong bone) and lower values to smoother, higher-frequency texture pattern (washed out bone).	Baseline to Month 24