Automatic analysis of trabecular bone structure from knee MRI.

We investigated the feasibility of quantifying osteoarthritis (OA) by analysis of the trabecular bone structure in low-field knee MRI. Generic texture features were extracted from the images and subsequently selected by sequential floating forward selection (SFFS), following a fully automatic, uncommitted machine-learning based framework. Six different classifiers were evaluated in cross-validation schemes and the results showed that the presence of OA can be quantified by a bone structure marker. The performance of the developed marker reached a generalization area-under-the-ROC (AUC) of 0.82, which is higher than the established cartilage markers known to relate to the OA diagnosis.

Distribution, size, shape, growth potential and extent of abdominal aortic calcified deposits predict mortality in postmenopausal women.

BACKGROUND: Aortic calcification is a major risk factor for death from cardiovascular disease. We investigated the relationship between mortality and the composite markers of number, size, morphology and distribution of calcified plaques in the lumbar aorta. METHODS: 308 postmenopausal women aged 48-76 were followed for 8.3 ± 0.3 years, with deaths related to cardiovascular disease, cancer, or other causes being recorded. From lumbar X-rays at baseline the number (NCD), size, morphology and distribution of aortic calcification lesions were scored and combined into one Morphological Atherosclerotic Calcification Distribution (MACD) index. The hazard ratio for mortality was calculated for the MACD and for three other commonly used predictors: the EU SCORE card, the Framingham Coronary Heart Disease Risk Score (Framingham score), and the gold standard Aortic Calcification Severity score (AC24) developed from the Framingham Heart Study cohorts. RESULTS: All four scoring systems showed increasing age, smoking, and raised triglyceride levels were the main predictors of mortality after adjustment for all other metabolic and physical parameters. The SCORE card and the Framingham score resulted in a mortality hazard ratio increase per standard deviation (HR/SD) of 1.8 (1.51-2.13) and 2.6 (1.87-3.71), respectively. Of the morphological x-ray based measures, NCD revealed a HR/SD >2 adjusted for SCORE/Framingham. The MACD index scoring the distribution, size, morphology and number of lesions revealed the best predictive power for identification of patients at risk of mortality, with a hazard ratio of 15.6 (p < 0.001) for the 10% at greatest risk of death. CONCLUSIONS: This study shows that it is not just the extent of aortic calcification that predicts risk of mortality, but also the distribution, shape and size of calcified lesions. The MACD index may provide a more sensitive predictor of mortality from aortic calcification than the commonly used AC24 and SCORE/Framingham point card systems.

Breast density changes associated with postmenopausal hormone therapy: post hoc radiologist- and computer-based analyses.

OBJECTIVE: The aim of this study was to assess the impact of oral hormone therapy (HT) on breast density in postmenopausal women and to compare the use of computer-based automated approaches for the assessment of breast density with reference to traditional methods. METHODS: Low-dose oral estrogen (1 mg) continuously combined with drospirenone (2 mg) was administered to postmenopausal women for up to 2 years (26 treatment cycles, 28 d/cycle) in a randomized, placebo-controlled trial. This post hoc analysis assessed the changes in breast density measured from digitized images by two radiologist-based approaches (Breast Imaging Reporting and Data System score and interactive threshold) and one computer-based technique (heterogeneity examination of radiographs). Correlations of temporal changes in breast density with changes in serum estradiol levels, biochemical markers of bone metabolism, and bone mineral density at the spine and femur were also assessed. RESULTS: Breast density assessed by the radiologist-based approaches increased significantly from baseline in the HT group (P < 0.01), with significant divergence from placebo at 2 years (P < 0.01). Heterogeneity examination of radiograph score by computer-based technique was unchanged in the HT group and decreased significantly with placebo (P < 0.001) to produce a significant group divergence (P < 0.05). Changes in mammographic markers by radiologist- and computer-based approaches correlated with each other in the HT group (P < 0.01) but not in the placebo group. CONCLUSIONS: HT for 2 years in postmenopausal women significantly increased radiologist-assessed breast density compared with placebo, in addition to significant changes in estrogen levels, markers of bone metabolism, and bone mineral density. Computer-automated techniques may be comparable with and offer advantages over traditional methods.

Identification of progressors in osteoarthritis by combining biochemical and MRI-based markers.

INTRODUCTION: At present, no disease-modifying osteoarthritis drugs (DMOADS) are approved by the FDA (US Food and Drug Administration); possibly partly due to inadequate trial design since efficacy demonstration requires disease progression in the placebo group. We investigated whether combinations of biochemical and magnetic resonance imaging (MRI)-based markers provided effective diagnostic and prognostic tools for identifying subjects with high risk of progression. Specifically, we investigated aggregate cartilage longevity markers combining markers of breakdown, quantity, and quality. METHODS: The study included healthy individuals and subjects with radiographic osteoarthritis. In total, 159 subjects (48% female, age 56.0 +/- 15.9 years, body mass index 26.1 +/- 4.2 kg/m2) were recruited. At baseline and after 21 months, biochemical (urinary collagen type II C-telopeptide fragment, CTX-II) and MRI-based markers were quantified. MRI markers included cartilage volume, thickness, area, roughness, homogeneity, and curvature in the medial tibio-femoral compartment. Joint space width was measured from radiographs and at 21 months to assess progression of joint damage. RESULTS: Cartilage roughness had the highest diagnostic accuracy quantified as the area under the receiver-operator characteristics curve (AUC) of 0.80 (95% confidence interval: 0.69 to 0.91) among the individual markers (higher than all others, P < 0.05) to distinguish subjects with radiographic osteoarthritis from healthy controls. Diagnostically, cartilage longevity scored AUC 0.84 (0.77 to 0.92, higher than roughness: P = 0.03). For prediction of longitudinal radiographic progression based on baseline marker values, the individual prognostic marker with highest AUC was homogeneity at 0.71 (0.56 to 0.81). Prognostically, cartilage longevity scored AUC 0.77 (0.62 to 0.90, borderline higher than homogeneity: P = 0.12). When comparing patients in the highest quartile for the longevity score to lowest quartile, the odds ratio of progression was 20.0 (95% confidence interval: 6.4 to 62.1). CONCLUSIONS: Combination of biochemical and MRI-based biomarkers improved diagnosis and prognosis of knee osteoarthritis and may be useful to select high-risk patients for inclusion in DMOAD clinical trials.

Low-dose transdermal estradiol induces breast density and heterogeneity changes comparable to those of raloxifene.

UNLABELLED: The aim of this study was to investigate whether transdermal low-dose estradiol treatment induces changes in mammographic density or heterogeneity compared with raloxifene, whether if these changes relate to changes in bone formation/resorption markers, and whether these findings indicate elevation of breast cancer risk by treatment. METHODS: Digitized mammograms of 2 x 135 completers of a 2-year, randomized trial formed the base of the present analysis. Active treatments were transdermal estradiol releasing 0.014 mg estradiol (E2)/week and orally administered raloxifene hydrochloride 60 mg/day, respectively. Influence of the therapies on breast density was assessed with categorical scores Breast Imaging Reporting and Data System, area percentage density, and computer-based (E2-specific) heterogeneity examination of radiographs. These where related to physical and systemic markers. RESULTS: At baseline, no mammography scoring methodology or other marker could separate the two treatment groups of transdermal estradiol and raloxifene. No treatment induced significant density changes measured by Breast Imaging Reporting and Data System. Both treatments made the area percentage density increase and the estradiol significantly. Both treatments induced significant changes in E2-specific heterogeneity scoring (E2-specific heterogeneity examination of radiograph), and the raloxifene treatment induced a significantly higher change. At baseline, the mammographic markers showed negative correlation with body mass index and positive correlation with serum type I collagen crosslinks C-telopeptide. The changes in mammographic markers did not essentially exhibit correlations to changes in bone markers in either treatment group. CONCLUSIONS: Low-dose transdermal estradiol and raloxifene induced comparable changes in breast density and heterogeneity. Baseline correlations may be explained through relations to obesity. The current study does not yield evidence against the hypothesis that "neither raloxifene nor low dose transdermal estradiol treatment increases the breast cancer risk."

Automatic quantification of local and global articular cartilage surface curvature: biomarkers for osteoarthritis?

The objective of this study was to quantitatively assess the surface curvature of the articular cartilage from low-field magnetic resonance imaging (MRI) data, and to investigate its role in populations with varying radiographic signs of osteoarthritis (OA), cross-sectionally and longitudinally. The curvature of the articular surface of the medial tibial compartment was estimated both on fine and coarse scales using two different automatic methods which are both developed from an automatic 3D segmentation algorithm. Cross-sectionally (n=288), the surface curvature for both the fine- and coarse-scale estimates were significantly higher in the OA population compared with the healthy population, with P<0.001 and P<<0.001, respectively. For the longitudinal study (n=245), there was a significant increase in fine-scale curvature for healthy and borderline OA populations (P<0.001), and in coarse-scale curvature for severe OA populations (P<0.05). Fine-scale curvature could predict progressors using the estimates of those healthy at baseline (P<0.001). The inter-scan precision was 2.2 and 6.5 (mean CV) for the fine- and coarse scale curvature measures, respectively. The results showed that quantitative curvature estimates from low-field MRI at different scales could potentially become biomarkers targeted at different stages of OA.

Quantifying calcification in the lumbar aorta on X-ray images.

In this paper we propose to use inpainting to estimate the severity of atherosclerotic plaques from X-ray projections. Inpainting allows to "remove" the plaque and estimate what the background image for an uncalcified aorta would have looked like. A measure of plaque severity can then be derived by subtracting the inpainting from the original image. In contrast to the current standard of categorical calcification scoring from X-rays, our method estimates both the size and the density of calcified areas and provides a continuous severity score, thus allowing for measurement of more subtle differences. We discuss a class of smooth inpainting methods, compare their ability to reconstruct the original images, and compare the inpainting based calcification score to the conventional categorical score in a longitudinal study on 49 patients addressing correlations of the calcification scores with hypertension, a known cardiovascular risk factor.

Osteoarthritic cartilage is more homogeneous than healthy cartilage: identification of a superior region of interest colocalized with a major risk factor for osteoarthritis.

RATIONALE AND OBJECTIVES: Cartilage loss as determined by magnetic resonance imaging (MRI) or joint space narrowing as determined by x-ray is the result of cartilage erosion. However, metabolic processes within the cartilage that later result in cartilage loss may be a more sensitive assessment method for early changes. Recently, it was shown that cartilage homogeneity visualized by MRI representing the biochemical changes undergoing in the cartilage is a potential marker for early detection of knee osteoarthritis (OA) and is also able to significantly separate groups of healthy subjects from those with OA. The purpose of this study was twofold. First, we wished to evaluate whether the results on cartilage homogeneity from the previous study can be reproduced using an independent population. Second, based on the homogeneity framework, we present an automatic technique that partitions the region of interest in the cartilage that contributes most to discrimination between healthy and OA subjects and allows for identification of the most implicated areas in early OA. These findings may allow further investigation of whether cartilage homogeneity reveals a predisposition for OA or whether it evolves as a consequence to disease and thereby can be used as a progression biomarker. MATERIALS AND METHODS: A total of 283 right and left knees from 159 subjects aged 21 to 81 years were scanned using a Turbo 3D T1 sequence on a 0.18-T MRI Esaote scanner. The medial compartment of the tibial cartilage sheet was segmented using a fully automatic voxel classification scheme based on supervised learning. From the segmented cartilage sheet, homogeneity was quantified by measuring entropy from the distribution of signal intensities inside the compartment. Each knee was examined by radiography, and the knees were categorized by the Kellgren and Lawrence (KL) Index. Next, based on a gradient descent optimization technique, the cartilage region that contributed to the maximum statistical significance of homogeneity in separating healthy subjects from the diseased was partitioned. The generalizability of the region was evaluated by testing for overfitting. Three different regularization techniques were evaluated for reducing overfitting errors. RESULTS: The P values for separating the different groups based on cartilage homogeneity were 2 x 10(-5) (KL 0 versus KL 1) and 1 x 10(-7) (KL 0 versus KL >0). Using the automatic gradient descent technique, the partitioned region was toward the peripheral part of the cartilage sheet. Using this region, the P values for separating the different groups based on homogeneity were 5 x 10(-9) (KL 0 versus KL 1) and 1 x 10(-15) (KL 0 versus KL >0). The precision of homogeneity for the partitioned region assessed as a test-retest root-mean-square coefficient of variation was 3.3%. Bootstrapping proved to be an effective regularization tool in reducing overfitting errors. CONCLUSION: The validation study supported the use of cartilage homogeneity as a tool for the early detection of knee OA and for separating groups of healthy subjects from those who have disease. Our automatic, unbiased partitioning algorithm based on a general statistical framework outlined the cartilage region of interest that best separated healthy from OA conditions on the basis of homogeneity discrimination. We have shown that OA affects certain areas of the cartilage more distinctly, and these areas are located more toward the peripheral region of the cartilage. We propose that this region corresponds anatomically to cartilage covered by the meniscus in healthy subjects. This finding may provide valuable clues in the early detection and monitoring of OA and thus may improve treatment efficacy.

Quantitative vertebral morphometry using neighbor-conditional shape models.

A novel method for vertebral fracture quantification from X-ray images is presented. Using pairwise conditional shape models trained on a set of healthy spines, the most likely normal vertebra shapes are estimated conditional on the shapes of all other vertebrae in the image. The difference between the true shape and the reconstructed normal shape is subsequently used as a measure of abnormality. In contrast with the current (semi-)quantitative grading strategies this method takes the full shape into account, it develops a patient-specific reference by combining population-based information on biological variation in vertebral shape and vertebra interrelations, and it provides a continuous measure of deformity. The method is demonstrated on 282 lateral spine radiographs with in total 93 fractures. Vertebral fracture detection is shown to be in good agreement with semi-quantitative scoring by experienced radiologists and is superior to the performance of shape models alone.

Segmenting articular cartilage automatically using a voxel classification approach.

We present a fully automatic method for articular cartilage segmentation from magnetic resonance imaging (MRI) which we use as the foundation of a quantitative cartilage assessment. We evaluate our method by comparisons to manual segmentations by a radiologist and by examining the interscan reproducibility of the volume and area estimates. Training and evaluation of the method is performed on a data set consisting of 139 scans of knees with a status ranging from healthy to severely osteoarthritic. This is, to our knowledge, the only fully automatic cartilage segmentation method that has good agreement with manual segmentations, an interscan reproducibility as good as that of a human expert, and enables the separation between healthy and osteoarthritic populations. While high-field scanners offer high-quality imaging from which the articular cartilage have been evaluated extensively using manual and automated image analysis techniques, low-field scanners on the other hand produce lower quality images but to a fraction of the cost of their high-field counterpart. For low-field MRI, there is no well-established accuracy validation for quantitative cartilage estimates, but we show that differences between healthy and osteoarthritic populations are statistically significant using our cartilage volume and surface area estimates, which suggests that low-field MRI analysis can become a useful, affordable tool in clinical studies.