Computer aided evaluation of ankylosing spondylitis using high-resolution CT

Gas Chromatography-Mass Spectrometry Reveals Stage-Specific Metabolic Signatures of Ankylosing Spondylitis

Ankylosing spondylitis (AS) is a type of chronic rheumatic immune disease, and the crucial point of AS treatment is identifying the correct stage of the disease. However, there is a lack of effective diagnostic methods for AS staging. The primary objective of this study was to perform an untargeted metabolomic approach in AS patients in an effort to reveal metabolic differences between patients in remission and acute stages. Serum samples from 40 controls and 57 AS patients were analyzed via gas chromatography-mass spectrometry (GC-MS). Twenty-four kinds of differential metabolites were identified between the healthy controls and AS patients, mainly involving valine/leucine/isoleucine biosynthesis and degradation, phenylalanine/tyrosine/tryptophan biosynthesis, glutathione metabolism, etc. Furthermore, the levels of fatty acids (linoleate, dodecanoate, hexadecanoate, and octadecanoate), amino acids (serine and pyroglutamate), 2-hydroxybutanoate, glucose, etc., were lower in patients in the acute stage than those in the remission stage, which may be associated with the aggravated inflammatory response and elevated oxidative stress in the acute stage. Multiple stage-specific metabolites were significantly correlated with inflammatory indicators (CRP and ESR). In addition, the combination of serum 2-hydroxybutanoate and hexadecanoate plays a significant role in the diagnosis of AS stages. These metabolomics-based findings provide new perspectives for AS staging, treatment, and pathogenesis studies.

Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions

CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.

Spine and Individual Vertebrae Segmentation in Computed Tomography Images Using Geometric Flows and Shape Priors

The surgical treatment of injuries to the spine often requires the placement of pedicle screws. To prevent damage to nearby blood vessels and nerves, the individual vertebrae and their surrounding tissue must be precisely localized. To aid surgical planning in this context we present a clinically applicable geometric flow based method to segment the human spinal column from computed tomography (CT) scans. We first apply anisotropic diffusion and flux computation to mitigate the effects of region inhomogeneities and partial volume effects at vertebral boundaries in such data. The first pipeline of our segmentation approach uses a region-based geometric flow, requires only a single manually identified seed point to initiate, and runs efficiently on a multi-core central processing unit (CPU). A shape-prior formulation is employed in a separate second pipeline to segment individual vertebrae, using both region and boundary based terms to augment the initial segmentation. We validate our method on four different clinical databases, each of which has a distinct intensity distribution. Our approach obviates the need for manual segmentation, significantly reduces inter- and intra-observer differences, runs in times compatible with use in a clinical workflow, achieves Dice scores that are comparable to the state of the art, and yields precise vertebral surfaces that are well within the acceptable 2 mm mark for surgical interventions.

Better Quantification of Syndesmophyte Growth in Axial Spondyloarthritis

PURPOSE OF REVIEW

We review new insights to syndesmophyte growth in axial spondyloarthritis revealed by computed tomography (CT).

RECENT FINDINGS

CT allows full reliable quantitation of syndesmophytes. About 70% of patients had detectable growth in syndesmophyte volume or height in 1 year. Syndesmophyte growth is not uniform, but can be highly heterogeneous even within the same disc space of the same patient. Syndesmophytes are not randomly distributed around the vertebral rim but have preferred locations (posterolateral and anterolateral) which vary along the spine. The frequency of syndesmophyte involvement also varies along the spine. It is highest at the thoracolumbar junction and higher in the thoracic than lumbar spine. CT syndesmophyte quantitation is a promising tool for studies of medications or biomarkers and their relations with syndesmophyte progression. The localization and growth patterns of syndesmophytes suggest importance for local factors such as mechanical stress.

Quantitation of circumferential syndesmophyte height along the vertebral rim in ankylosing spondylitis using computed tomography

OBJECTIVE

Using the 3-D imaging capability of computed tomography (CT), we developed an algorithm quantitating syndesmophyte height along the entire vertebral rim. We investigated its reliability and sensitivity to change, performed a 2-year longitudinal study, and compared it to CT measures of syndesmophyte volume.

METHODS

We performed thoracolumbar spine CT scans on 33 patients at baseline, Year 1, and Year 2, and computed syndesmophyte height in 4 intervertebral disc spaces (IDS). Height was computed every 5° (72 angular sectors) along the vertebral rim. These 72 measures were summed to form the circumferential height per IDS, and results from 4 IDS were summed to provide results per patient. To assess reliability, we compared results between 2 scans performed on the same day in 9 patients. Validity was assessed by associations with spinal flexibility.

RESULTS

Coefficient of variation for circumferential syndesmophyte height was 0.893% per patient, indicating excellent reliability. Based on the Bland-Altman analysis, an increase in circumferential height of more than 3.44% per patient represented a change greater than measurement error. At years 1 and 2, mean (SD) circumferential syndesmophyte height increases were 10.2% (11.7%) and 16.1% (14.0%), respectively. Sensitivity to change was 0.72 and 0.87 at years 1 and 2, respectively. Circumferential syndesmophyte height correlated with the Schober test (r = -0.56, p = 0.0003) and lateral thoracolumbar flexion (r = -0.73, p < 0.0001).

CONCLUSION

CT-based circumferential syndesmophyte height had excellent reliability and good sensitivity to change. It was more highly correlated with spine flexibility than syndesmophyte volume. The algorithm shows promise for longitudinal studies of syndesmophyte growth.

Quantitative syndesmophyte measurement in ankylosing spondylitis using CT: longitudinal validity and sensitivity to change over 2 years

OBJECTIVES

Accurate measurement of syndesmophyte development and growth in ankylosing spondylitis (AS) is needed for studies of biomarkers and of treatments to slow spinal fusion. We tested the longitudinal validity and sensitivity to change of quantitative measurement of syndesmophytes using CT.

METHODS

We performed lumbar spine CT scans on 33 patients with AS at baseline, 1 year and 2 years. Volumes and heights of syndesmophytes were computed in four intervertebral disk spaces. We compared the computed changes to a physician's ratings of change based on CT scan inspection. Sensitivity to change of the computed measures was compared with that of the modified Stoke AS Spinal Score (radiography) and a scoring method based on MRI.

RESULTS

At years 1 and 2, respectively 24 (73%) and 26 (79%) patients had syndesmophyte volume increases by CT. At years 1 and 2, the mean (SD) computed volume increases per patient were, respectively 87 (186) and 201 (366) mm(3). Computed volume changes were strongly associated with the physician's visual ratings of change (p<0.0002 and p<0.0001 for changes at years 1 and 2, respectively). The sensitivity to change over 1 year was higher for the CT volume measure (1.84) and the CT height measure (1.22) than either the MRI measure (0.50) or radiography (0.29).

CONCLUSIONS

CT-based syndesmophytes measurements had very good longitudinal validity and better sensitivity to change than radiography or MRI. This method shows promise for longitudinal clinical studies of syndesmophyte development and growth.

Cortical shell unwrapping for vertebral body abnormality detection on computed tomography

The vertebral body is the main axial load-bearing structure of the spinal vertebra. Assessment of acute injury and chronic deformity of the vertebral body is difficult to assess accurately and quantitatively by simple visual inspection. We propose a cortical shell unwrapping method to examine the vertebral body for injury such as fractures and degenerative osteophytes. The spine is first segmented and partitioned into vertebrae. Then the cortical shell of the vertebral body is extracted using deformable dual-surface models. The cortical shell is then unwrapped onto a 2D map and the complex 3D detection problem is effectively converted to a pattern recognition problem on a 2D plane. Characteristic features adapted for different applications are computed and sent to a committee of support vector machines for classification. The system was evaluated on two applications, one for fracture detection on trauma CT datasets and the other on degenerative osteophyte assessment on sodium fluoride PET/CT. The fracture CAD achieved 93.6% sensitivity at 3.2 false positive per patient and the degenerative osteophyte CAD achieved 82% sensitivity at 4.7 false positive per patient.

Quantitative measurement of syndesmophyte volume and height in ankylosing spondylitis using CT

OBJECTIVE

Syndesmophyte growth in ankylosing spondylitis can be difficult to measure using radiographs because of poor visualisation and semiquantitative scoring methods. We developed and tested the reliability and validity of a new computer-based method that fully quantifies syndesmophyte volumes and heights on CT scans.

METHODS

In this developmental study, we performed lumbar spine CT scans on 38 patients and used our algorithm to compute syndesmophyte volume and height in four intervertebral disk spaces. To assess reliability, we compared results between two scans performed on the same day in nine patients. To assess validity, we compared computed measures to visual ratings of syndesmophyte volume and height on both CT scans and radiographs by two physician readers.

RESULTS

Coefficients of variation for syndesmophyte volume and height, based on repeat scans, were 2.05% and 2.40%, respectively. Based on Bland-Altman analysis, an increase in syndesmophyte volume of more than 4% or in height of more than 0.20 mm represented a change greater than measurement error. Computed volumes and heights were strongly associated with physician ratings of syndesmophyte volume and height on visual examination of both the CT scans (p<0.0001) and plain radiographs (p<0.002). Syndesmophyte volumes correlated with the Schober test (r=-0.48) and lateral thoracolumbar flexion (r=-0.60).

CONCLUSIONS

This new CT-based method that fully quantifies syndesmophytes in three-dimensional space had excellent reliability and face and construct validity. Given its high precision, this method shows promise for longitudinal clinical studies of syndesmophyte development and growth.

High precision semiautomated computed tomography measurement of lumbar disk and vertebral heights

PURPOSE

Evaluation of treatments of many spine disorders requires precise measurement of the heights of vertebral bodies and disk spaces. The authors present a semiautomated computer algorithm measuring those heights from spine computed tomography (CT) scans and evaluate its precision.

METHODS

Eight patients underwent two spine CT scans in the same day. In each scan, five thoracolumbar vertebral heights and four disk heights were estimated using the algorithm. To assess precision, the authors computed the differences between the height measurements in the two scans, coefficients of variation (CV), and 95% limits of agreement. Intraoperator and interoperator precisions were evaluated. For local vertebral and disk height measurement (anterior, middle, posterior) the algorithm was compared to a manual mid-sagittal plane method.

RESULTS

The mean (standard deviation) interscan difference was as low as 0.043 (0.031) mm for disk heights and 0.044 (0.043) mm for vertebral heights. The corresponding 95% limits of agreement were [-0.085, 0.11] and [-0.10, 0.12] mm, respectively. Intraoperator and interoperator precision was high, with a maximal CV of 0.30%. For local vertebral and disk heights, the algorithm improved upon the precision of the manual mid-sagittal plane measurement by as much as a factor of 6 and 4, respectively.

CONCLUSIONS

The authors evaluated the precision of a novel computer algorithm for measuring vertebral body heights and disk heights using short term repeat CT scans of patients. The 95% limits of agreement indicate that the algorithm can detect small height changes of the order of 0.1 mm.

Compression fracture diagnosis in lumbar: a clinical CAD system

PURPOSE

Lower back pain affects 80-90 % of all people at some point during their life time, and it is considered as the second most neurological ailment after headache. It is caused by defects in the discs, vertebrae, or the soft tissues. Radiologists perform diagnosis mainly from X-ray radiographs, MRI, or CT depending on the target organ. Vertebra fracture is usually diagnosed from X-ray radiographs or CT depending on the available technology. In this paper, we propose a fully automated Computer-Aided Diagnosis System (CAD) for the diagnosis of vertebra wedge compression fracture from CT images that integrates within the clinical routine.

METHODS

We perform vertebrae localization and labeling, segment the vertebrae, and then diagnose each vertebra. We perform labeling and segmentation via coordinated system that consists of an Active Shape Model and a Gradient Vector Flow Active Contours (GVF-Snake). We propose a set of clinically motivated features that distinguish the fractured vertebra. We provide two machine learning solutions that utilize our features including a supervised learner (Neural Networks (NN)) and an unsupervised learner (K-Means).

RESULTS

We validate our method on a set of fifty (thirty abnormal) Computed Tomography (CT) cases obtained from our collaborating radiology center. Our diagnosis detection accuracy using NN is 93.2 % on average while we obtained 98 % diagnosis accuracy using K-Means. Our K-Means resulted in a specificity of 87.5 % and sensitivity over 99 %.

CONCLUSIONS

We presented a fully automated CAD system that seamlessly integrates within the clinical work flow of the radiologist. Our clinically motivated features resulted in a great performance of both the supervised and unsupervised learners that we utilize to validate our CAD system. Our CAD system results are promising to serve in clinical applications after extensive validation.

Improved precision of syndesmophyte measurement for the evaluation of ankylosing spondylitis using CT: a phantom and patient study

Ankylosing spondylitis is a disease characterized by abnormal bone formation (syndesmophyte) at the margins of inter-vertebral disc spaces. Syndesmophyte growth is currently typically monitored by the visual inspection of radiographs. The limitations inherent to the modality (2D projection of a 3D object) and rater (qualitative human judgment) may compromise sensitivity. With newly available treatments, more precise measures of syndesmophytes are needed to determine whether treatment can slow rates of syndesmophyte growth. We previously presented a computer algorithm measuring syndesmophyte volumes and heights in the 3D space of CT scans. In this study, we present improvements to the original algorithm and evaluate the gain in precision as applied to an anthropomorphic vertebral phantom and patients. Each patient was scanned twice in one day, thus providing two syndesmophyte volume and height measures. The difference between those two measures (ideally zero) determines our algorithm's precision. The technical improvements to the algorithm decreased the mean volume difference (standard deviation) between scans from 3.01% (2.83%) to 1.31% (0.95%) and the mean height difference between scans from 3.16% (2.99%) to 1.56% (1.13%). The high precision of the improved algorithm holds promise for application to longitudinal clinical studies.

Precision of syndesmophyte volume measurement for ankylosing spondylitis: a phantom study using high resolution CT

Ankylosing Spondylitis is a disease characterized by abnormal bone structures (syndesmophytes) growing at intervertebral disk spaces (IDS). The growth of syndesmophytes is typically monitored by visual inspection of radiographs. The limitations inherent to the modality (2D projection of a 3D object) and rater (qualitative human judgment) entail a possibly important loss in sensitivity. We previously presented a method designed to overcome both limitations: a computer algorithm that quantitatively measures syndesmophytes in the 3D space of a high-resolution computed tomography scan. To establish the method's usefulness for longitudinal studies, it is necessary to assess its precision (repeatability) which can be affected by the limitations of both the algorithm itself and the imaging modality. To this end, an anthropomorphic vertebral phantom with syndesmophytes in 4 IDSs was manufactured. It was scanned 22 times with varying positions and resolutions. The syndesmophyte volumes extracted by our algorithm have an average coefficient of variation of 1.6% per IDS and 0.85% for the total.

Linear measurement of polyps in CT colonography using level sets on 3D surfaces

CT colonography has emerged as a minimally invasive alternative to optical colonoscopy for the screening of polyps which are the precursors to colon cancer. Accurate polyp measurement is crucial as the size of a polyp is considered an indication of its potential for malignancy. We present a novel method for the automatic measurement of polyps. It is based on a level set algorithm capable of evolving on the surface of a 3D object represented by a triangular mesh. It is guided by curvature features and is capable of segmenting the polyp neck, that is, the ridgeline/crestline formed around the polyp by its merging to the colon wall. Our method was validated on 40 polyp surfaces obtained from real clinical data. A 3D manual measurement was used as the reference standard. A correlation of 0.825 was found between polyp measurements from our new method and the reference standard.