Analysis of the Predictive Efficiency of Lumbar Vertebral Body Quantification (VBQ) and CT Hounsfield Units (HUs) for Bone Density: Age and Gender Differences

Abstract

Study Design

Retrospective Cohort Study.

Objective

This study examines the consistency of Vertebral Bone Quality (VBQ) and Computed Tomography Hounsfield Units (CT HUs) with Dual-energy X-ray absorptiometry (DXA) as a reference standard, evaluating the diagnostic performance of these 2 imaging techniques across different age groups and genders. Particular attention is given to the applicability of VBQ in different age and gender cohorts.

Methods

We included 972 eligible patients, from which 569 patients were randomly selected and included in the analysis according to the inclusion criteria. These patients underwent lumbar Magnetic Resonance Imaging (MRI), lumbar CT, and DXA within 3 months of hospital admission. The study assessed the correlation and diagnostic efficacy of these techniques in measuring lumbar and femoral neck bone mineral density (BMD).

Results

It showed good correlation between VBQ and CT HUs with DXA in individuals under 70 years of age. However, in the population over 70 years, the correlation of VBQ with DXA significantly decreased (lumbar BMD pr = −.145 P > .05; femoral neck BMD r = −.097 P > .05), whereas CT HUs maintained high diagnostic performance. The ROC curve analysis indicated that the AUC for differentiating osteoporosis (based on lumbar spine BMD) by VBQ was .545 in males over 70 and .487 in females over 70. However, CT HUs demonstrated diagnostic performance across all groups.

Conclusion

VBQ is effective in assessing osteoporosis in patients under 70 but shows decreased efficacy in those over 70. When using VBQ to predict osteoporosis in patients on opportunistic grounds, it is still necessary to incorporate additional reference indicators, such as CT HUs.

Keywords

osteoporosis VBQ CT HUs different age ROC

Introduction

Osteoporosis is a metabolic bone disease that significantly affects the elderly population worldwide, particularly among women, where the incidence increases markedly with age.¹ In China, the prevalence of osteoporotic fractures among the elderly is 18.9%, with fracture rates increasing with age.² The central Dual-energy X-ray absorptiometry (DXA) of the hip and lumbar spine remains the recognized method for bone mass assessment.³ However, due to additional economic burdens and radiation exposure, as well as potential inaccuracies in bone mineral density (BMD) and T-scores measured by DXA due to conditions like spinal scoliosis, lumbar spondylosis, or abdominal aortic calcification,^4-7 alternative measurement methods are needed for osteoporosis screening.

Previous studies have shown that vertebral Tomography Hounsfield Units (CT HUs) obtained from existing CT scans can serve as a supplementary method for measuring bone density and have been proven to correlate positively with BMD.^8-10 The Vertebral Bone Quality (VBQ) is a method for evaluating vertebral bone quality based on Magnetic Resonance Imaging (MRI),¹¹ which considers the decline in trabecular bone quality and increase in fat infiltration in osteoporosis patients by measuring the T1 signal in lumbar MRI and normalizing it against the cerebrospinal fluid at the L3 level. Several studies have indicated that VBQ is a valuable diagnostic tool for osteoporosis and significantly correlated with BMD values.^12-14

Although previous studies have shown the potential of VBQ and CT HUs in identifying osteoporosis, these studies typically utilized smaller sample sizes or did not differentiate between age groups, thus limiting their general applicability and statistical significance. Therefore, this study aims to comprehensively assess the consistency of these imaging parameters with the DXA standard and their diagnostic efficacy in clinically diagnosing osteoporosis by broadly collecting data across different age groups, and we aim to determine whether VBQ is a reliable predictor of osteoporosis regardless of age or gender.

Methods

Subjects

This retrospective study evaluated 1735 lumbar spine surgery patients admitted to our hospital for lumbar degenerative diseases between January 2018 and June 2023. These patients underwent lumbar MRI, lumbar CT, and DXA measurements within 3 months of admission. Exclusion criteria included: (1) patients under the age of 18; (2) abnormalities at the L1-L4 level such as a history of vertebral fractures, infections, tumors, scoliosis, ankylosing spondylitis or any instrumentation in the lumbar spine; (3) patients with other metabolic bone diseases such as hyperparathyroidism, diffuse idiopathic skeletal hyperostosis; (4) history of osteoporotic fractures; (5) MRI study relaxation times (TR and TE) not within the optimal range for marrow assessment (TR = 400-850 ms, TE = 8-20 ms); (6) insufficient image quality or inability to measure lumbar VBQ. In total, 972 patients met the inclusion criteria. The patients were divided into 5 groups according to age: under 40, 40-49, 50-59, 60-69, and 70 years and older. There were fewer patients under 40 years old, totaling 90. To avoid a large disparity in the number of patients between the first group and the others, 120 patients who met the inclusion criteria were randomly selected from each of the remaining groups. (Diagram depicting the flow of patients shown in Figure 1).

Figure 1.

Diagram depicting the flow of patients included in the study.

VBQ Measurements

Patients underwent non-contrast imaging using a 1.5 Tesla MRI scanner. All images were transmitted to the computer in DICOM format and evaluated and analyzed using PACS software. T1WI non-enhanced lumbar images were used for VBQ measurement by placing a circular ROI in the medullary part of the vertebral bodies from L1 to L4 and in the CSF space at the L3 level on the midsagittal T1-weighted MRI image. If prominent venous plexuses or hemangiomas were observed on the midsagittal image, ROIs were selected on parasagittal slices, and if terminal inflammation was present, the ROI was appropriately reduced to avoid the inflamed area. When nearly all sagittal slices showed abnormalities, that segment was excluded from the study. If spinal stenosis was prominent in the L3 segment, CSF measurement was taken above or below the next possible segment (L2 or L4). The median SI of the L1-L4 vertebrae was calculated and then divided by the average signal intensity of the CSF from L1 to L3 to obtain the patient’s VBQ^11,15:

V B Q = \frac{M e d i a n ({S I}_{L 1 - 4})}{{S I}_{C S F}}

VBQ measurement is shown in Figure 2.

Figure 2.

ROIs drawn on each vertebral body and the L3-CSF to generate values for VBQ calculation.

HUs Value Measurements

HUs value was measured on lumbar CT using the ROI tool in the PACS software. When an ROI was placed at any location on the CT image, the software automatically calculated the average HUs of the selected area. It is important to note that the window setting of the CT image does not affect the average HUs. HUs value was measured at 3 independent locations on each vertebra (below the upper endplate, in the middle of the vertebrae, and above the lower endplate), expanding the ROI as large as possible but limiting it to the cancellous bone area to minimize the influence of cortical bone and avoiding areas with posterior veins and bone spurs. If terminal inflammation was present at any location, that position was excluded from the study. The HUs value for each vertebra was the mean of the HUs values at the 3 independent locations. The average lumbar HUs was the mean HUs from L1 to L4.

CT Hus measurement is shown in Figure 3.

Figure 3.

(1A)-(4C): An ROI was drawn in the cancellous region of the vertebral body, avoiding the cortical and vascular areas of the bone. Ave, average; max, maximum; min, minimum. (D): HUs were measured for the upper, middle and lower each vertebral body from L1 to L4.

Collecting T-Score by Dual Energy X-ray Absorptiometry

Dual-energy X-ray absorptiometry (DXA) measurements of bone mineral density were assessed at the lumbar spine and femoral neck. Based on World Health Organization diagnostic criteria, osteoporosis is defined as a T-score of −2.5 or lower, while osteopenia is characterized by a T-score between −1.0 and −2.5.

Statistical Analysis

The data above were obtained by professional spine surgeons through measurement. First, we included the mean values of VBQ, CT HU, and DXA for different age groups. To assess the correlation between VBQ, CT HU, and DXA values in different age groups, we used Pearson correlation analysis. To control for potential confounding effects of age, we used partial correlation analysis to evaluate the overall associations among the 3 parameters. VBQ and CT HUs were independently measured by 2 researchers, who were blinded to the clinical information of the subjects. The interclass correlation coefficient (ICC) was calculated to assess inter-observer reliability, with ICC values classified as excellent (>.90), good (.80-.90), acceptable (.70-.80), and poor (≤.70). The statistical significance level was set at P < .05.

In order to evaluate the diagnostic performance of VBQ and CT HU values for osteoporosis using DXA as the gold standard, we plotted ROC curves by age group, gender, and menopausal status.

Results

Five hundred sixty-nine patients were included (mean ± SD age was 56.05 ± 14.70 years). In the total group, the gender distribution was 335 (59%) males and 234 (41%) females. Additional basic radiological data is included in Table 1. Inter-rater reliability between the 2 independent researchers was excellent: VBQ, ICC .952 (95% CI: .944-.959), and HU value, ICC .911 (95% CI: .889-.953).

Table 1.

Basic Radiological Data of 569 Included Patients (Data Reported as Mean ± SD or n).

Groups (Years)	Male	Female	VBQ Score	HUs Value	Lumbar Spine T-Score (Mean)	Femoral Neck T-Score (Mean)
20-39	65	24	2.74 ± .48	193 ± 36	−.1 ± 1.6	−.4 ± 1.0
40-49	69	51	2.99 ± .47	171 ± 38	−.3 ± 1.6	−.6 ± 1.1
50-59	70	50	3.32 ± .61	136 ± 42	−.7 ± 1.4	−.9 ± 1.1
60-69	60	60	3.44 ± .64	104 ± 32	−.9 ± 1.7	−1.2 ± 1.0
Over 70	71	49	3.54 ± .61	96 ± 34	−1 ± 1.6	−1.6 ± 1.1
Total group	335 (59%)	234 (41%)	3.23 ± .63	137 ± 51	−.6 ± 1.6	−.9 ± 1.1

Total group for the whole study population.

VBQ, vertebral bone quality; HU, hounsfield unit.

Table 2 shows that regardless of gender, there is no significant correlation between VBQ and DXA (lumbar spine BMD pr = −.145, P > .05; femoral neck BMD r = −.097, P > .05) in individuals over 70 years old, while there is good correlation in other age groups. CT HUs correlates with both lumbar spine BMD and femoral neck BMD in different age groups. (Table 2) The partial correlation between VBQ/CT HUs and DXA T-scores remained significant after controlling for age in total group (Lumbar spine BMD: VBQ r = −.310 P < .001, CT HUs r = .58 P < .001; Femoral neck BMD: VBQ r = −.235 P < .001, CT HUs r = .484 P < .001) (Table 3).

Table 2.

Pearson Correlation Results for Lumbar Spine BMD, Femoral Neck BMD and VBQ Score, Hus Value.

Year Group		Total
Year Group		VBQ Score	Hus Value
20-39	Lumbar spine BMD	−.484**	.444**
20-39	Femoral neck BMD	−.252*	.480**
40-49	Lumbar spine BMD	−.303**	.593**
40-49	Femoral neck BMD	−.289**	.532**
50-59	Lumbar spine BMD	−.329**	.576**
50-59	Femoral neck BMD	−.270**	.475**
60-69	Lumbar spine BMD	−.433**	.512**
60-69	Femoral neck BMD	−.332**	.460**
Over 70	Lumbar spine BMD	−.145	.671**
Over 70	Femoral neck BMD	−.097	.552**
20-69	Lumbar spine BMD	−.426**	.526**
20-69	Femoral neck BMD	−.364**	.537**
Total group	Lumbar spine BMD	−.385**	.543**
Total group	Femoral neck BMD	−.357**	.583**

“**” mean P < .05.

Table 3.

Partial Correlation Results for Lumbar/Femoral Neck DXA and VBQ Score, CT HUs Value After Controlling for Age in Total Group.

Paired Variables	Partial Correlation r	P-Value
Lumbar DXA & VBQ	−.310	<.001
Lumbar DXA & CT HUs	.579	<.001
Femoral neck DXA & VBQ	−.235	<.001
Femoral neck DXA & CT HUs	.484	<.001

DXA, dual-energy X-ray absorptiometry; VBQ, vertebral bone quality; HUs, hounsfield units.

The ROC curve analysis indicated that in total groups both lumbar spine BMD and femoral neck BMD can be effectively predicted by VBQ, while can’t be predicted in the group over 70 years old (lumbar spine BMD: Youden index = .099, P > .05; femoral neck BMD: Youden index = .114, P > .05). CT HUs demonstrated diagnostic performance across all groups (Tables 4 and 5).

Table 4.

Analysis of VBQ Score and HUs Value Based on Lumbar Spine BMD as a Standard in Different Age Groups and the General Population.

	Groups (Years)	Cut-Off Point	Youden Index	Sensitivity (%)	Specificity (%)	AUC Value	Lower Bound of AUC Value	Upper Bound of ACU Value
	Age (Years)	Cut-Off Point	Youden Index	Sensitivity (%)	Specificity (%)	AUC Value	Lower Bound of AUC Value	Upper Bound of ACU Value
VBQ score	20-39	2.69	.314	72.0	59.4	.668^**	.548	.787
	40-49	2.85	.323	82.9	49.4	.698^**	.600	.796
	50-59	3.26	.654	67.3	60.7	.654^**	.553	.755
	60-69	3.53	.328	66.7	66.1	.696^**	.603	.790
	Over 70	4.27	.099	15.7	94.2	.516	.412	.619
	Total group	3.11	.272	71.0	56.2	.674^**	.630	.718
	20-69	3.11	.311	68.0	63.1	.695^**	.645	.745
HUs value	20-30	188	.647	96.0	68.7	.852^**	.771	.933
	40-49	165	.545	80.5	74.0	.781^**	.693	.869
	50-59	124	.487	75.5	73.2	.803^**	.725	.882
	60-69	91	.500	57.1	92.9	.796^**	.718	.874
	Over 70	99	.459	78.6	67.3	.796^**	.717	.875
	Total group	125	.424	69.0	73.4	.787^**	.750	.823
	20-69	164	.402	86.5	53.7	.781^**	.739	.823

“**” mean P < .05.

Total group for the whole study population.

Table 5.

Analysis of VBQ Score and HUs Value Based on Femoral Neck BMD as a Standard in Different Age Groups and the General Population.

	Groups (Years)	Cut-Off Point	Youden Index	Sensitivity (%)	Specificity (%)	AUC Value	Lower Bound of AUC Value	Upper Bound of ACU Value
	Age (Years)	Cut-Off Point	Youden Index	Sensitivity (%)	Specificity (%)	AUC Value	Lower Bound of AUC Value	Upper Bound of ACU Value
VBQ score	20-39	3.04	.276	45.5	82.1	.639^**	.505	.773
	40-49	3.11	.321	62.2	69.9	.673^**	.572	.775
	50-59	3.10	.302	76.4	53.8	.665^**	.568	.762
	60-69	3.47	.247	60.0	64.7	.637^**	.527	.748
	Over 70	3.97	.114	27.5	83.9	.536	.424	.649
	Total group	3.12	.327	70.6	62.1	.694^**	.653	.740
	20-69	3.17	.312	60.9	70.3	.703^**	.654	.751
HUs value	20-30	190	.476	86.4	66.2	.750 ^**	.642	.859
	40-49	173	.502	84.4	65.8	.789^**	.705	.873
	50-59	141	.400	80.0	60.0	.748^**	.661	.835
	60-69	91	.430	45.9	97.1	.725^**	.629	.821
	Over 70	110	.523	81.3	71.0	.801^**	.711	.892
	Total group	141	.479	77.6	70.3	.813^**	.780	.849
	20-69	150	.467	76.8	69.9	.796^**	.755	.837

“**” mean P < .05.

Total group for the whole study population.

Afterwards, we conducted further analysis on the diagnostic efficiency of VBQ and CT HUs for DXA in different genders. The VBQ was not satisfactory for the population aged over 70 regardless of gender. However, CT HUs still showed good diagnostic efficiency for BMD in both males and females in all years (Table 6). At the same time, we have generated the corresponding ROC curve for the diagnosis of osteoporosis in Figures 4 and 5.

Table 6.

Analysis of VBQ Score and HUs Value for Individuals Over 70 Years Old.

	Sex	Cut-Off Point	Youden Index	Sensitivity (%)	Specificity (%)	AUC Value	Lower Bound of AUC Value	Upper Bound of ACU Value
Base on lumbar spine BMD
VBQ score	Male	3.87	.138	43.8	70.0	.545	.408	.682
VBQ score	Female	3.69	.175	34.2	83.3	.487	.313	.661
HUs value	Male	115	.394	84.4	55.0	.749^**	.637	.861
HUs value	Female	85	.570	73.7	83.3	.800^**	.645	.956
Base on femoral neck BMD
VBQ score	Male	3.97	.194	34.8	84.6	.572^**	.438	.706
VBQ score	Female	3.59	.222	42.2	80.0	.462^**	.264	.661
HUs value	Male	116	.579	84.8	73.1	.801^**	.688	.914
HUs value	Female	85	.444	64.4	80.0	.716^**	.529	.902

“**” mean P < .05.

Figure 4.

Receiver operation characteristic curve analysis was used to evaluate VBQ and DEXA-measured BMD at the lumbar spine and femoral neck in groups. VBQ, vertebral bone quality scores; BMD, bone mineral density; DEXA, dual-energy X-ray absorptiometry; AUC, area under curve.

Figure 5.

Receiver operation characteristic curve analysis was used to evaluate CT HUs and DEXA-measured BMD at the lumbar spine and femoral neck in groups. CT HUs, CT Hounsfield units; BMD, bone mineral density; DEXA, dual-energy X-ray absorptiometry; AUC, area under curve.

Because the disruption of internal hormones before and after menopause has a greater impact on BMD among female, we grouped female into non-menopausal female and menopausal female in Table 7. For individuals aged over 70, both male and female VBQ cannot accurately assess the lumbar spine BMD (male’s AUC value = .545, P > .05; female’s AUC value = .487, P > .05). However, in postmenopausal women under 70, BMD can be accurately predicted using VBQ and CT HUs (Table 8).

Table 7.

Overall Analysis of VBQ Scores and HUs for Gender-Based Grouping.

	Sex	Cut-Off Point	Youden Index	Sensitivity (%)	Specificity (%)	AUC Value	Lower Bound of AUC Value	Upper Bound of ACU Value
Base on lumbar spine BMD
VBQ score	Male (20-69 years)	3.11	.265	64.9	61.7	.646^**	.578	.715
	Non-menopausal female	2.90	.531	82.4	70.7	.751^**	.628	.873
	Menopausal female (under 70 years old)	3.33	.299	73.4	56.5	.681^**	.581	.780
HUs value	Male (total group)	124	.326	67.4	53.9	.628^**	.567	.688
	Non-menopausal female	192	.544	94.1	60.3	.751^**	.698	.943
	Menopausal female	92	.512	57.8	93.5	.823^**	.747	.899
Base on femoral neck BMD
VBQ score	Male (20-69 years)	3.12	.262	62.6	63.6	.629^**	.561	.698
	Non-menopausal female	2.77	.478	79.2	68.6	.788^**	.682	.895
	Menopausal female (under 70 years old)	3.27	.355	73.7	61.8	.705^**	.604	.807
HUs value	Male (total group)	140	.445	73.9	70.6	.780^**	.731	.829
	Non-menopausal female	184	.478	79.2	68.6	.749^**	.630	.868
	Menopausal female	126	.500	88.2	61.8	.810^**	.722	.898

“**” mean P < .05.

Total group for the whole study population.

Table 8.

The Cut-Off Value of VBQ Score and Hus Value for Study Population.

	Age (years)	VBQ Score	Hus Value
Overall	20-39	2.69	188
	40-49	2.85	165
	50-59	3.26	124
	60-69	3.53	91
	Over 70	—	99
	20-69	3.17	—
	Total group	—	141
Divided by sex	Male (20-69)	3.11	—
	Male (total group)	—	140
	Male (over 70)	—	140
	Non-menopausal female	2.90	192
	Menopausal female	3.27	92
	Menopausal female (over 70)	—	92

Total group for the whole study population.

Taking the results mentioned above into account, Table 7 mainly includes the reference values for lumbar spine VBQ and CT HUs for individuals with normal/osteoporosis as suggested by us. For males, the most appropriate VBQ diagnostic threshold value is 3.11 (AUC value is .674, P < .05) for the population with osteoporosis, while for males over 70 years old, due to the low diagnostic efficiency of VBQ, the most appropriate CT HUs diagnostic threshold value is 140 as a diagnostic criterion for osteoporosis. Among females, for non-menopausal female, VBQ threshold value is 2.90 (AUC value is .751, P < .05) and CT HUs threshold value is 192 (AUC value also is .751, P < .05) as diagnostic criteria for osteoporosis. For menopausal female, VBQ threshold value is 3.27 (AUC value is .705, P < .05) and CT HUs threshold value is 92 (AUC value is .823, P < .05) as diagnostic criteria for osteoporosis. Similarly, for females over 70 years old, as VBQ has low diagnostic efficiency for BMD, the CT HUs for menopausal female threshold value = 92 can be an alternative prediction (Table 8).

Discussion

This study aimed to comprehensively assess whether VBQ and CT HUs correlate with the DXA standard and to evaluate the diagnostic efficacy of VBQ and CT HUs in patients diagnosed with osteoporosis by DXA. Across the general population, we found that both VBQ and lumbar T-scores and femoral neck T-scores as measured by DXA increase with age, consistent with prior research.^16-18 Ting et al also reported a significant correlation between VBQ and DXA (r = −.398 P < .05), as well as between HUs and DXA (r = .617, P < .05),^9,10,19 confirming these correlations in our study. Moreover, we found that HUs had a higher correlation with DXA than VBQ across all patients. Osteoporosis is known to increase in prevalence with age. To assess whether age influences these correlations, we adjusted for age as a potential confounding factor. Partial correlation analysis confirmed the statistically significant association between VBQ, CT HUs, and DXA, further demonstrating the robustness of their correlation.

It is worth noting that, although DXA may not be the absolute “gold standard,” it still holds significant clinical value due to its widespread use. Therefore, this study is entirely based on osteoporosis diagnoses made using DXA. Unlike previous studies, this is the first large-sample study to investigate the correlation and diagnostic efficacy of DXA with VBQ and HUs across different ages and genders. We found that for the population over 70, VBQ had no significant correlation with either lumbar DXA (r = −.145) or femoral neck DXA (r = −.097), in contrast to HUs which correlated with DXA across all age groups. Further analysis of patients over 70 showed that VBQ has very low efficacy in diagnosing osteoporosis based on lumbar DXA (male AUC value = .545; female AUC value = .487). Additionally, considering the hormonal changes in postmenopausal female patients, we divided the population into menopausal and premenopausal females under 70, and men under 70, finding that VBQ shows good diagnostic efficacy for both lumbar and femoral neck DXA in these groups. Notably, HUs maintained significant correlations with DXA and good diagnostic efficacy for osteoporosis across all age groups.

Thus, we conclude that for the population over 70, VBQ cannot effectively diagnose osteoporosis, whereas for those under 70, regardless of gender or menopausal status, VBQ performs well. Hormonal changes in postmenopausal females do not affect the effectiveness of VBQ. Osteoporosis leads to a reduction in trabecular bones and an increase in fat cells that replace the trabecular structure, weakening it overall.²⁰ As women age, the rate of marrow fat transformation is faster than in men, especially after menopause,^21,22 and the T1 signal, which is sensitive to fat, indicates higher VBQ scores with increased bone fat content. However, does vertebral fat tissue only relate to osteoporosis, and is VBQ scoring only influenced by fat tissue? Previous research has shown that bone marrow fat tissue can play a role in bone health through its paracrine and endocrine interactions with other bone components.²³ For instance, increased bone fat content in type 2 diabetes is not related to reduced bone mass.²⁴ Bredella studied 47 premenopausal women with varying BMIs and found that vertebral marrow fat increase correlates positively with visceral fat and negatively with IGF-1.²⁵ Moreover, we cannot exclude the changes in the collagen and hydroxyapatite within bones, or potential changes in water content in the lumbar spine in the elderly, might affect VBQ. Therefore, for individuals over 70, the lack of correlation between VBQ and DXA-determined bone density suggests that VBQ reflects not just bone density but bone quality, or more broadly, bone composition. Changes in organic or inorganic components of bones affect bone fragility. Ehresman found that compared to BMD obtained from DXA, VBQ is an independent predictor of thoracolumbar fragility fractures.²⁶ Aynaszyan et al’s study also showed that hyperlipidemia significantly raises VBQ, and controlling for hyperlipidemia improves the accuracy of VBQ in predicting osteopenia and osteoporosis.²⁷ Roch et al combined VBQ scores from different MRI sequences with vBMD, finding improved correlation between VBQ scores and vBMD,¹⁴ although this requires more time to calculate scores, suggesting a direction for optimizing VBQ. Given the potential changes in bone composition in the elderly, we believe it is necessary to cautiously use VBQ to test for osteoporosis in individuals over 70. Due to bone changes in menopausal and premenopausal females, we separately recommend VBQ = 3.27 and VBQ = 2.90 as diagnostic thresholds for osteoporosis, whereas previous studies based on all patients suggested a threshold of 3.^18,28

Our study found good correlation between HUs and both lumbar and femoral neck T-scores by DXA across all age groups, regardless of the menopausal status of female patients. Through ROC, we found that 141HUs was determined as the best sensitivity/specificity ratio for distinguishing between normal and abnormal BMD individuals. Pickhardt et al found a balanced HUs of 160, while Cohen et al found it to be 149.^10,29 Additionally, we discovered that after dividing the population into different age groups, genders, and menopausal statuses, the thresholds for HUs varied. Furthermore, for the population over 70, we determined male HUs balance value to be 140 and female to be 92, and since VBQ’s diagnostic efficacy is too low to distinguish between normal and abnormal BMD individuals effectively, we recommend using HUs as an alternative to DXA and not using VBQ for diagnosing abnormal bone mass.

Most patients visiting for lumbar spine diseases routinely undergo lumbar spine CT or MRI, making it very convenient and reliable to measure HUs or VBQ values using existing images, without always incurring the additional economic burden and radiation exposure of DXA. As an opportunistic preoperative tool that has emerged in recent years, VBQ has the advantage of no radiation exposure, making further research into its prospects for use necessary to determine whether VBQ can serve as an independent diagnostic tool. In our study, although VBQ performed poorly in the elderly population over 70, it showed good performance in postmenopausal females and other age groups. Meanwhile, HUs can avoid interference from bone spurs, spinal scoliosis, and aortic calcification, theoretically providing a more accurate spinal bone density assessment.³⁰

While our study focused on the correlation of lumbar and femoral BMD with CT Hounsfield Units (HUs) and VBQ for diagnosing osteoporosis, comprehensive approaches are required for osteoporosis diagnosis in clinical practice, such as incorporating fracture history and FRAX risk assessment. Nevertheless, it is noteworthy that FRAX’s requirement for detailed clinical variables (eg, smoking status, parental hip fracture history) may limit its utility in time-constrained clinical scenarios. In contrast, VBQ and HUs demonstrate particular clinical utility in opportunistic screening scenarios, particularly during outpatient evaluations where obtaining comprehensive patient histories proves challenging. These imaging-driven approaches address the growing clinical demand in resource-limited diagnostic settings.

While our study provides valuable insights into the correlation between VBQ and HUs with DEXA and their diagnostic efficacy, there are several limitations. Firstly, despite a relatively large sample size, the fact that all data were obtained from a single medical center may constrain the generalizability of the results. Secondly, this study was limited to using DEXA as the gold standard, focusing primarily on further research to assess whether opportunistic methods are consistent and comparable with DEXA. Although DEXA is the most commonly used method clinically, compared to QCT, it is not widely used in clinical practice. However, as mentioned in the introduction, DEXA can be affected by certain degenerative diseases and is only traditionally considered the gold standard for BMD. Thirdly, due to the retrospective design, our data may be subject to selection bias. Additionally, despite efforts to minimize measurement errors, technical differences between different devices and operators may affect the accuracy of the results. Furthermore, the measurement of VBQ and CT HUs relies on high-quality imaging data, and poor image quality may affect the accuracy of the measurements. Lastly, our study did not extensively explore patient populations of different races and regions, data on fragility fractures, FRAX risk assessment and factors other than age and gender were not systematically collected; future research should consider these factors to enhance the representativeness and generalizability of the study findings.

Conclusion

This study found that, although VBQ had lower diagnostic efficacy for patients over 70 years old, it showed good diagnostic accuracy for postmenopausal women under 70 and other age groups. In contrast, CT HUs demonstrated higher correlation and bone density assessment capability with DXA across all age groups. When using VBQ to predict osteoporosis in patients on opportunistic grounds, it is still necessary to incorporate additional reference indicators, such as CT HUs. Additionally, further research is needed to optimize the use of MRI as an opportunistic method for predicting osteoporosis.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Statement

ORCID iDs

Minghang Chen

Honglin Teng

Data Availability Statement

The datasets analyzed during the current study are not publicly available due to involving patients’ imaging examination results and other personal information but are available from the corresponding author on reasonable request.*

References

Salari

Darvishi

Bartina

, et al. Global prevalence of osteoporosis among the world older adults: a comprehensive systematic review and meta-analysis. J Orthop Surg Res. 2021;16(1):669. doi:10.1186/s13018-021-02821-8

Meng

Tong

, et al. The prevalence of osteoporotic fractures in the elderly in China: a systematic review and meta-analysis. J Orthop Surg Res. 2023;18(1):536. doi:10.1186/s13018-023-04030-x

Lewiecki

Gordon

Baim

, et al. International society for clinical densitometry 2007 adult and pediatric official positions. Bone. 2008;43(6):1115-1121. doi:10.1016/j.bone.2008.08.106

Kim

Song

Kim

Jeon

. Is dual-energy absorptiometry accurate in the assessment of bone status of patients with chronic kidney disease? Osteoporos Int. 2021;32(9):1859-1868. doi:10.1007/s00198-020-05670-z

Choi

Kim

Lim

. Diagnostic efficacy of Hounsfield units in spine CT for the assessment of real bone mineral density of degenerative spine: correlation study between T-scores determined by DEXA scan and hounsfield units from CT. Acta Neurochir. 2016;158(7):1421-1427. doi:10.1007/s00701-016-2821-5

Muraki

Yamamoto

Ishibashi

, et al. Impact of degenerative spinal diseases on bone mineral density of the lumbar spine in elderly women. Osteoporos Int. 2004;15(9):724-728.

Pappou

Girardi

Sandhu

, et al. Discordantly high spinal bone mineral density values in patients with adult lumbar scoliosis. Spine. 2006;31(14):1614-1620.

Zou

Deng

. The use of CT Hounsfield unit values to identify the undiagnosed spinal osteoporosis in patients with lumbar degenerative diseases. Eur Spine J. 2019;28(8):1758-1766. doi:10.1007/s00586-018-5776-9

Schreiber

Anderson

Rosas

Buchholz

. Hounsfield units for assessing bone mineral density and strength: a tool for osteoporosis management. J Bone Joint Surg Am. 2011;93(11):1057-1063. doi:10.2106/JBJS.J.00160

10.

Pickhardt

Pooler

Lauder

del Rio

Bruce

Binkley

. Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med. 2013;158(8):588-595. doi:10.7326/0003-4819-158-8-201304160-00003

11.

Ehresman

Pennington

Schilling

, et al. Novel MRI-based score for assessment of bone density in operative spine patients. Spine J. 2020;20(4):556-562. doi:10.1016/j.spinee.2019.10.018

12.

Salzmann

Okano

Jones

, et al. Preoperative MRI-based vertebral bone quality (VBQ) score assessment in patients undergoing lumbar spinal fusion. Spine J. 2022;22(8):1301-1308. doi:10.1016/j.spinee.2022.03.006

13.

Zhang

Zhou

L-P

Zhang

X-L

, et al.

Which indicator among lumbar vertebral hounsfield unit, vertebral bone quality, or dual-energy X-ray absorptiometry-measured bone mineral density is more efficacious in predicting thoracolumbar fragility fractures?

Neurospine. 2023;20(4):1193-1204. doi:10.14245/ns.2346998.499

14.

Roch

Çelik

Jäckle

, et al. Combination of vertebral bone quality scores from different magnetic resonance imaging sequences improves prognostic value for the estimation of osteoporosis. Spine J. 2023;23(2):305-311. doi:10.1016/j.spinee.2022.10.013

15.

Ehresman

Schilling

Pennington

, et al. A novel MRI-based score assessing trabecular bone quality to predict vertebral compression fractures in patients with spinal metastasis. J Neurosurg Spine. 2019;32:1-8. doi:10.3171/2019.9.SPINE19954

16.

Warming

Hassager

Christiansen

. Changes in bone mineral density with age in men and women: a longitudinal study. Osteoporos Int. 2002;13(2):105-112.

17.

Jang

Graffy

Ziemlewicz

Lee

Summers

Pickhardt

. Opportunistic osteoporosis screening at routine abdominal and thoracic CT: normative L1 trabecular attenuation values in more than 20 000 adults. Radiology. 2019;291(2):360-367. doi:10.1148/radiol.2019181648

18.

Liu

Kadri

Hernando

Binkley

Anderson

. MRI-based vertebral bone quality score: relationship with age and reproducibility. Osteoporos Int. 2023;34(12):2077-2086. doi:10.1007/s00198-023-06893-6

19.

T-T

Huang

X-Y

Jiang

Y-W

. Efficacy of two opportunistic methods for screening osteoporosis in lumbar spine surgery patients. Eur Spine J. 2023;32(11):3912-3918. doi:10.1007/s00586-023-07938-4

20.

Meunier

Aaron

Edouard

Vignon

. Osteoporosis and the replacement of cell populations of the marrow by adipose tissue. A quantitative study of 84 iliac bone biopsies. Clin Orthop Relat Res. 1971;80:147-154.

21.

Di Iorgi

Grimm

Wren

TAL

Dorey

Gilsanz

. Bone acquisition in healthy young females is reciprocally related to marrow adiposity. J Clin Endocrinol Metab. 2010;95(6):2977-2982. doi:10.1210/jc.2009-2336

22.

Baum

Rohrmeier

Syväri

, et al. Anatomical variation of age-related changes in vertebral bone marrow composition using chemical shift encoding-based water-fat magnetic resonance imaging. Front Endocrinol. 2018;9:141. doi:10.3389/fendo.2018.00141

23.

Aparisi

GMP

Ayuso Benavent

Simoni

Aparisi

Guglielmi

Bazzocchi

. Fat and bone: the multiperspective analysis of a close relationship. Quant Imag Med Surg. 2020;10(8):1614-1635. doi:10.21037/qims.2020.01.11

24.

Lecka-Czernik

. Marrow fat metabolism is linked to the systemic energy metabolism. Bone. 2012;50(2):534-539. doi:10.1016/j.bone.2011.06.032

25.

Bredella

Torriani

Ghomi

, et al. Vertebral bone marrow fat is positively associated with visceral fat and inversely associated with IGF-1 in obese women. Obesity (Silver Spring). 2011;19(1):49-53. doi:10.1038/oby.2010.106

26.

Ehresman

Schilling

Yang

, et al. Vertebral bone quality score predicts fragility fractures independently of bone mineral density. Spine J. 2021;21(1):20-27. doi:10.1016/j.spinee.2020.05.540

27.

Aynaszyan

Devia

Udoeyo

Badve

DelSole

. Patient physiology influences the MRI-based vertebral bone quality score. Spine J. 2022;22(11):1866-1874. doi:10.1016/j.spinee.2022.06.003

28.

Kadri

Binkley

Hernando

Anderson

. Opportunistic use of lumbar magnetic resonance imaging for osteoporosis screening. Osteoporos Int. 2022;33(4):861-869. doi:10.1007/s00198-021-06129-5

29.

Cohen

Foldes

Hiller

Simanovsky

Szalat

. Opportunistic screening for osteoporosis and osteopenia by routine computed tomography scan: a heterogeneous, multiethnic, middle-eastern population validation study. Eur J Radiol. 2021;136:109568. doi:10.1016/j.ejrad.2021.109568

30.

Deshpande

Hadi

Lillard

, et al. Alternatives to DEXA for the assessment of bone density: a systematic review of the literature and future recommendations. J Neurosurg Spine. 2023;38(4):436-445. doi:10.3171/2022.11.SPINE22875