Prediction of incident vertebral fractures in routine MDCT: Comparison of global texture features, 3D finite element parameters and volumetric BMD

Dieckmeyer, Michael; Rayudu, Nithin Manohar; Yeung, Long Yu; Löffler, Maximilian T.; Sekuboyina, Anjany; Burian, Egon; Sollmann, Nico; Kirschke, Jan S.; Baum, Thomas

doi:10.1016/j.ejrad.2021.109827

Cited by 7 publications

(18 citation statements)

References 45 publications

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“…In our paper, we reviewed 3 studies that implemented 2-dimensional (2D)-CNNs in distinct ways within their framework. Dieckmeyer et al [ 34 ] utilized a 2D-CNN to automatically segment vertebral bodies. Following this step, their approach diverged and instead utilized a combination of radiomics and a logistic regression to complete the analysis pipeline.…”

Section: Understanding Computer Visionmentioning

confidence: 99%

“…Dieckmeyer et al [ 34 ] conducted a radiomic analysis of multidetector CT scans, focusing on the thoracolumbar spine of 32 patients, evenly split between cases and controls. The L1 to L3 vertebrae were automatically segmented through a deep learning algorithm from which 24 texture features, 2 FEA features, and volumetric BMD, were derived.…”

Section: Clinical Applications In Ovf Risk Predictionmentioning

confidence: 99%

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Computer Vision in Osteoporotic Vertebral Fracture Risk Prediction: A Systematic Review

Allam,

Anand,

Flores

et al. 2023

Neurospine

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Osteoporotic vertebral fractures (OVFs) are a significant health concern linked to increased morbidity, mortality, and diminished quality of life. Traditional OVF risk assessment tools like bone mineral density (BMD) only capture a fraction of the risk profile. Artificial intelligence, specifically computer vision, has revolutionized other fields of medicine through analysis of videos, histopathology slides and radiological scans. In this review, we provide an overview of computer vision algorithms and current computer vision models used in predicting OVF risk. We highlight the clinical applications, future directions and limitations of computer vision in OVF risk prediction.

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Section: Understanding Computer Visionmentioning

confidence: 99%

Section: Clinical Applications In Ovf Risk Predictionmentioning

confidence: 99%

Computer Vision in Osteoporotic Vertebral Fracture Risk Prediction: A Systematic Review

Allam,

Anand,

Flores

et al. 2023

Neurospine

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“…On the basis of the distribution of gray-level values, texture analysis was used to characterize structural image properties of predefined regions by quantifying different texture features (37)(38)(39). Texture analysis was performed using the segmentation masks derived from CT and CSE-MRI enclosing single vertebral bodies, and different first-order statistical moments from global gray-level histograms, second-order features based on the graylevel co-occurrence matrix (GLCM), and higher-order features based on the gray-level run-length matrix (GLRLM) were extracted (Figure 1 and Table 1) (40,41,51,(53)(54)(55).…”

Section: Texture Analysismentioning

confidence: 99%

Texture Analysis Using CT and Chemical Shift Encoding-Based Water-Fat MRI Can Improve Differentiation Between Patients With and Without Osteoporotic Vertebral Fractures

et al. 2022

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PurposeOsteoporosis is a highly prevalent skeletal disease that frequently entails vertebral fractures. Areal bone mineral density (BMD) derived from dual-energy X-ray absorptiometry (DXA) is the reference standard, but has well-known limitations. Texture analysis can provide surrogate markers of tissue microstructure based on computed tomography (CT) or magnetic resonance imaging (MRI) data of the spine, thus potentially improving fracture risk estimation beyond areal BMD. However, it is largely unknown whether MRI-derived texture analysis can predict volumetric BMD (vBMD), or whether a model incorporating texture analysis based on CT and MRI may be capable of differentiating between patients with and without osteoporotic vertebral fractures.Materials and MethodsTwenty-six patients (15 females, median age: 73 years, 11 patients showing at least one osteoporotic vertebral fracture) who had CT and 3-Tesla chemical shift encoding-based water-fat MRI (CSE-MRI) available were analyzed. In total, 171 vertebral bodies of the thoracolumbar spine were segmented using an automatic convolutional neural network (CNN)-based framework, followed by extraction of integral and trabecular vBMD using CT data. For CSE-MRI, manual segmentation of vertebral bodies and consecutive extraction of the mean proton density fat fraction (PDFF) and T2* was performed. First-order, second-order, and higher-order texture features were derived from texture analysis using CT and CSE-MRI data. Stepwise multivariate linear regression models were computed using integral vBMD and fracture status as dependent variables.ResultsPatients with osteoporotic vertebral fractures showed significantly lower integral and trabecular vBMD when compared to patients without fractures (p<0.001). For the model with integral vBMD as the dependent variable, T2* combined with three PDFF-based texture features explained 40% of the variance (adjusted R2[Ra2] = 0.40; p<0.001). Furthermore, regarding the differentiation between patients with and without osteoporotic vertebral fractures, a model including texture features from CT and CSE-MRI data showed better performance than a model based on integral vBMD and PDFF only (Ra2 = 0.47 vs. Ra2 = 0.81; included texture features in the final model: integral vBMD, CT_Short-run_emphasis, CT_Varianceglobal, and PDFF_Variance).ConclusionUsing texture analysis for spine CT and CSE-MRI can facilitate the differentiation between patients with and without osteoporotic vertebral fractures, implicating that future fracture prediction in osteoporosis may be improved.

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“…Finite element (FE) patient-specific three-dimensional (3D) models derived from clinical multi-detector CT (MDCT) have been used widely for analyzing the bone qualitatively [ 16 , 17 , 18 , 19 ]. In this method, a patient-specific 3D anatomical model is segmented and reconstructed from the MDCT images.…”

Section: Introductionmentioning

confidence: 99%

Patient-Specific Finite Element Modeling of the Whole Lumbar Spine Using Clinical Routine Multi-Detector Computed Tomography (MDCT) Data—A Pilot Study

et al. 2022

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(1) Background: To study the feasibility of developing finite element (FE) models of the whole lumbar spine using clinical routine multi-detector computed tomography (MDCT) scans to predict failure load (FL) and range of motion (ROM) parameters. (2) Methods: MDCT scans of 12 subjects (6 healthy controls (HC), mean age ± standard deviation (SD): 62.16 ± 10.24 years, and 6 osteoporotic patients (OP), mean age ± SD: 65.83 ± 11.19 years) were included in the current study. Comprehensive FE models of the lumbar spine (5 vertebrae + 4 intervertebral discs (IVDs) + ligaments) were generated (L1–L5) and simulated. The coefficients of correlation (ρ) were calculated to investigate the relationship between FE-based FL and ROM parameters and bone mineral density (BMD) values of L1–L3 derived from MDCT (BMDQCT-L1-3). Finally, Mann–Whitney U tests were performed to analyze differences in FL and ROM parameters between HC and OP cohorts. (3) Results: Mean FE-based FL value of the HC cohort was significantly higher than that of the OP cohort (1471.50 ± 275.69 N (HC) vs. 763.33 ± 166.70 N (OP), p < 0.01). A strong correlation of 0.8 (p < 0.01) was observed between FE-based FL and BMDQCT-L1-L3 values. However, no significant differences were observed between ROM parameters of HC and OP cohorts (p = 0.69 for flexion; p = 0.69 for extension; p = 0.47 for lateral bending; p = 0.13 for twisting). In addition, no statistically significant correlations were observed between ROM parameters and BMDQCT- L1-3. (4) Conclusions: Clinical routine MDCT data can be used for patient-specific FE modeling of the whole lumbar spine. ROM parameters do not seem to be significantly altered between HC and OP. In contrast, FE-derived FL may help identify patients with increased osteoporotic fracture risk in the future.

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Prediction of incident vertebral fractures in routine MDCT: Comparison of global texture features, 3D finite element parameters and volumetric BMD

Cited by 7 publications

References 45 publications

Computer Vision in Osteoporotic Vertebral Fracture Risk Prediction: A Systematic Review

Computer Vision in Osteoporotic Vertebral Fracture Risk Prediction: A Systematic Review

Texture Analysis Using CT and Chemical Shift Encoding-Based Water-Fat MRI Can Improve Differentiation Between Patients With and Without Osteoporotic Vertebral Fractures

Patient-Specific Finite Element Modeling of the Whole Lumbar Spine Using Clinical Routine Multi-Detector Computed Tomography (MDCT) Data—A Pilot Study

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