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Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images.

ABSTRACT: Purpose To create and validate a computer system with which to detect, localize, and classify compression fractures and measure bone density of thoracic and lumbar vertebral bodies on computed tomographic (CT) images. Materials and Methods Institutional review board approval was obtained, and informed consent was waived in this HIPAA-compliant retrospective study. A CT study set of 150 patients (mean age, 73 years; age range, 55-96 years; 92 women, 58 men) with (n = 75) and without (n = 75) compression fractures was assembled. All case patients were age and sex matched with control subjects. A total of 210 thoracic and lumbar vertebrae showed compression fractures and were electronically marked and classified by a radiologist. Prototype fully automated spinal segmentation and fracture detection software were then used to analyze the study set. System performance was evaluated with free-response receiver operating characteristic analysis. Results Sensitivity for detection or localization of compression fractures was 95.7% (201 of 210; 95% confidence interval [CI]: 87.0%, 98.9%), with a false-positive rate of 0.29 per patient. Additionally, sensitivity was 98.7% and specificity was 77.3% at case-based receiver operating characteristic curve analysis. Accuracy for classification by Genant type (anterior, middle, or posterior height loss) was 0.95 (107 of 113; 95% CI: 0.89, 0.98), with weighted ? of 0.90 (95% CI: 0.81, 0.99). Accuracy for categorization by Genant height loss grade was 0.68 (77 of 113; 95% CI: 0.59, 0.76), with a weighted ? of 0.59 (95% CI: 0.47, 0.71). The average bone attenuation for T12-L4 vertebrae was 146 HU ± 29 (standard deviation) in case patients and 173 HU ± 42 in control patients; this difference was statistically significant (P < .001). Conclusion An automated machine learning computer system was created to detect, anatomically localize, and categorize vertebral compression fractures at high sensitivity and with a low false-positive rate, as well as to calculate vertebral bone density, on CT images. ^© RSNA, 2017 Online supplemental material is available for this article.

SUBMITTER: Burns JE

PROVIDER: S-EPMC5584647 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images.

Burns Joseph E JE Yao Jianhua J Summers Ronald M RM

Radiology 20170316 3

Purpose To create and validate a computer system with which to detect, localize, and classify compression fractures and measure bone density of thoracic and lumbar vertebral bodies on computed tomographic (CT) images. Materials and Methods Institutional review board approval was obtained, and informed consent was waived in this HIPAA-compliant retrospective study. A CT study set of 150 patients (mean age, 73 years; age range, 55-96 years; 92 women, 58 men) with (n = 75) and without (n = 75) comp ...[more]

PMID: 28301777

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Project description:PurposeTo design and validate a fully automated computer system for the detection and anatomic localization of traumatic thoracic and lumbar vertebral body fractures at computed tomography (CT).Materials and methodsThis retrospective study was HIPAA compliant. Institutional review board approval was obtained, and informed consent was waived. CT examinations in 104 patients (mean age, 34.4 years; range, 14-88 years; 32 women, 72 men), consisting of 94 examinations with positive findings for fractures (59 with vertebral body fractures) and 10 control examinations (without vertebral fractures), were performed. There were 141 thoracic and lumbar vertebral body fractures in the case set. The locations of fractures were marked and classified by a radiologist according to Denis column involvement. The CT data set was divided into training and testing subsets (37 and 67 subsets, respectively) for analysis by means of prototype software for fully automated spinal segmentation and fracture detection. Free-response receiver operating characteristic analysis was performed.ResultsTraining set sensitivity for detection and localization of fractures within each vertebra was 0.82 (28 of 34 findings; 95% confidence interval [CI]: 0.68, 0.90), with a false-positive rate of 2.5 findings per patient. The sensitivity for fracture localization to the correct vertebra was 0.88 (23 of 26 findings; 95% CI: 0.72, 0.96), with a false-positive rate of 1.3. Testing set sensitivity for the detection and localization of fractures within each vertebra was 0.81 (87 of 107 findings; 95% CI: 0.75, 0.87), with a false-positive rate of 2.7. The sensitivity for fracture localization to the correct vertebra was 0.92 (55 of 60 findings; 95% CI: 0.79, 0.94), with a false-positive rate of 1.6. The most common cause of false-positive findings was nutrient foramina (106 of 272 findings [39%]).ConclusionThe fully automated computer system detects and anatomically localizes vertebral body fractures in the thoracic and lumbar spine on CT images with a high sensitivity and a low false-positive rate.

Project description:In a population-based study, we found that computed tomography (CT)-based bone density and strength measures from the thoracic spine predicted new vertebral fracture as well as measures from the lumbar spine, suggesting that CT scans at either the thorax or abdominal regions are useful to assess vertebral fracture risk.IntroductionPrior studies have shown that computed tomography (CT)-based lumbar bone density and strength measurements predict incident vertebral fracture. This study investigated whether CT-based bone density and strength measurements from the thoracic spine predict incident vertebral fracture and compared the performance of thoracic and lumbar bone measurements to predict incident vertebral fracture.MethodsThis case-control study of community-based men and women (age 74.6 ± 6.6) included 135 cases with incident vertebral fracture at any level and 266 age- and sex-matched controls. We used baseline CT scans to measure integral and trabecular volumetric bone mineral density (vBMD) and vertebral strength (via finite element analysis, FEA) at the T8 and L2 levels. Association between these measurements and vertebral fracture was determined by using conditional logistic regression. Sensitivity and specificity for predicting incident vertebral fracture were determined for lumbar spine and thoracic bone measurements.ResultsBone measurements from T8 and L2 predicted incident vertebral fracture equally well, regardless of fracture location. Specifically, for predicting vertebral fracture at any level, the odds ratio (per 1-SD decrease) for the vBMD and strength measurements at L2 and T8 ranged from 2.0 to 2.7 (p < 0.0001) and 1.8 to 2.8 (p < 0.0001), respectively. Results were similar when predicting fracture only in the thoracic versus the thoracolumbar spine. Lumbar and thoracic spine bone measurements had similar sensitivity and specificity for predicting incident vertebral fracture.ConclusionThese findings indicated that like those from the lumbar spine, CT-based bone density and strength measurements from the thoracic spine may be useful for identifying individuals at high risk for vertebral fracture.

Project description:Rationale: Poly (methyl methacrylate) (PMMA) bone cement is one of the most commonly used biomaterials for augmenting/stabilizing osteoporosis-induced vertebral compression fractures (OVCFs), such as percutaneous vertebroplasty (PVP) and balloon kyphoplasty (BKP). However, its clinical applications are limited by its poor performance in high compressive modulus and weak bonding to bone. To address these issues, a bioactive composite bone cement was developed for the treatment of osteoporotic vertebral compression fractures, in which mineralized collagen (MC) was incorporated into the PMMA bone cement (MC-PMMA). Methods: The in vitro properties of PMMA and MC-PMMA composite bone cement were determined, including setting time, compressive modulus, adherence, proliferation, and osteogenic differentiation of rat bone mesenchymal stem cells. The in vivo properties of both cements were evaluated in an animal study (36 osteoporotic New Zealand female rabbits divided equally between the two bone cement groups; PVP at L5) and a small-scale and short-term clinical study (12 patients in each of the two bone cement groups; follow-up: 2 years). Results: In terms of value for PMMA bone cement, the handling properties of MC-PMMA bone cement were not significantly different. However, both compressive strength and compressive modulus were found to be significantly lower. In the rabbit model study, at 8 and 12 weeks post-surgery, bone regeneration was more significant in MC-PMMA bone cement (cortical bone thickness, osteoblast area, new bone area, and bone ingrowth %; each significantly higher). In the clinical study, at a follow-up of 2 years, both the Visual Analogue Score and Oswestry Disability Index were significantly reduced when MC-PMMA cement was used. Conclusions: MC-PMMA bone cement demonstrated good adaptive mechanical properties and biocompatibility and may be a promising alternative to commercial PMMA bone cements for the treatment of osteoporotic vertebral fractures in clinical settings. While the present results for MC-PMMA bone cement are encouraging, further study of this cement is needed to explore its viability as an ideal alternative for use in PVP and BKP.

Project description:Purpose:We aimed to retrospectively assess the incidence of vertebral compression fractures (VCF), examine clinicopathologic factors potentially associated with VCF, and evaluate treatment response in patients who received stereotactic body radiotherapy (SBRT) for spine metastases (spMets). Methods and Materials:We identified 78 patients with 125 spMets at baseline and subsequent assessments. Patients received SBRT doses of 16 or 18 Gy. Patients with pre-existing VCF and co-existing local progression were excluded. Spine instability neoplastic score (SINS) was used for spMets categorization. Response to SBRT and VCF were assessed according to the Positron Emission tomography Response Criteria In Solid Tumors (PERCIST) and Genant scores, respectively. Kaplan-Meier analyses were used to assess local control of disease and vertebral compression fracture-free survival (FFS). Results:We treated 103 cases with single spMets and 11 cases involving double spMets with SBRT. Progressive disease was reported in 3.2% and 8.2% of the cases in the first and last PET/CT reports, respectively. The distribution of treatment response in the remaining patients was: complete response in 30.6% of patients, partial response in 47.1% of patients, and stable disease in 22.3% of patients in the first PET/CT; complete response in 62.3% of patients, partial response in 16.7% of patients, and stable disease in 21% of patients at the last monitoring. Local failures were observed in 15 (12%) of cases. Median SINS was 5 (range: 1-13); majority of patients in our cohort (70.4%) were categorized as stable according to SINS, five (4%) patients had Grade 3 VCF at a median time of 16 months after SBRT (range: 2-22 months), and 60% of VCF occurred after an interval of at least 12 months after SBRT. No bisphosphonate usage was significantly associated with VCF (r = -0.204; p = 0.022). Median FFS was 21 months. Univariate analyses indicated that female gender (p < 0.001), bisphosphonate use (p = 0.005), >6 months of bisphosphonates use (p = 0.002), and the lowest vertebral body collapse score (p = 0.023) were associated with higher FFS. Female gender (p = 0.007), >6 months of bisphosphonates usage (p = 0.018), and the lowest vertebral body collapse score (p = 0.044) retained independent significance. Conclusions:This study demonstrated that spine SBRT with doses of 16-18 Gy promises good local control of disease with acceptable VCF rates. Lowest vertebral body collapse score, female gender, and >6 months of bisphosphonate use were significantly associated with longer FFS.

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Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images.

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Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images.

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