AI based prostate analysis system trained without human supervision to predict patient outcome from tissue samples

In order to plan the best treatment for prostate cancer patients, the aggressiveness of the tumor is graded based on visual assessment of tissue biopsies according to the Gleason scale. Recently, a number of AI models have been developed that can be trained to do this grading as well as human pathologists. But the accuracy of the AI grading will be limited by the accuracy of the subjective “ground truth” Gleason grades used for the training. We have trained an AI to predict patient outcome directly based on image analysis of a large biobank of tissue samples with known outcome without input of any human knowledge about cancer grading. The model has shown similar and in some cases better ability to predict patient outcome on an independent test-set than expert pathologists doing the conventional grading.

The novel collagen matrikine, endotrophin, is associated with mortality and cardiovascular events in patients with atherosclerosis

BACKGROUND

Rupture of atherosclerotic plaques is the major cause of acute cardiovascular events. The biomarker PRO-C6 measuring Endotrophin, a matrikine of collagen type VI, may provide valuable information detecting subjects in need of intensified strategies for secondary prevention.

OBJECTIVE

In this study, we evaluate endotrophin in human atherosclerotic plaques and circulating levels of PRO-C6 in patients with atherosclerosis, to determine the predictive potential of the biomarker.

METHODS

Sections from the stenotic human carotid plaques were stained with the PRO-C6 antibody. PRO-C6 was measured in serum of patients enrolled in the Carotid Plaque Imagining Project (CPIP) (discovery cohort, n = 577) and the innovative medicines initiative surrogate markers for micro- and macrovascular hard end-points for innovative diabetes tools (IMI-SUMMIT, validation cohort, n = 1,378). Median follow-up was 43 months. Kaplan-Meier curves and log-rank tests were performed in the discovery cohort. Cox proportional hazard regression analysis (HR with 95% CI) was used in the discovery cohort and binary logistic regression (OR with 95% CI) in the validation cohort.

RESULTS

PRO-C6 was localized in the core and shoulder of the atherosclerotic plaque. In the discovery cohort, PRO-C6 independently predicted future cardiovascular events (HR 1.089 [95% CI 1.019 -1.164], p = 0.01), cardiovascular death (HR 1.118 [95% CI 1.008 -1.241], p = 0.04) and all-cause death (HR 1.087 [95% CI 1.008 -1.172], p = 0.03). In the validation cohort, PRO-C6 predicted future cardiovascular events (OR 1.063 [95% CI 1.011 -1.117], p = 0.017).

CONCLUSION

PRO-C6 is present in the atherosclerotic plaque and associated with future cardiovascular events, cardiovascular death and all-cause mortality in two large prospective cohorts.

3046High levels of MMP-cleaved mimecan is associated to carotid plaque stability and less future cardiovascular events

Introduction

The clinical consequences of atherosclerosis, myocardial infarction and stroke are the most common causes of death globally. Mimecan, a small leucine rich-repeat proteoglycan (SLRP), is cleaved by matrix metalloproteinases (MMPs) and known to be involved in collagen fibrillogenesis and angiogenesis. Circulating levels of MMP-cleaved mimecan (cMIM) has previously been identified as a marker of extracellular matrix remodelling in ApoE−/− knockout mice. The role of mimecan and its degradation in human atherosclerotic plaques has not been explored.

Purpose

We explored whether full-length mimecan and cleaved mimecan (cMIM) are associated to plaque composition and evaluated if they can predict future cardiovascular events.

Methods

Two hundred and eighteen human atherosclerotic plaques were stained for mimecan using immunohistochemistry. cMIM was measured in 202 plaque tissue homogenates using a competitive ELISA assay. Histological components (α-actin, CD68 and glycophorin A) were assessed using immunohistochemistry, neutral lipids were measured using Oil Red O and visible areas of calcium deposits were quantified. Matrix metalloproteinases (MMP-1, -2, -3, -9, -10 and -12), tissue inhibitors of matrix metalloproteinases (TIMP-1 and -2) were analysed in plaque tissue homogenates using ELISA assays and a proximity extension assay. ECM components (glycosaminoglycans, collagen and elastin) were detected with colorimetric assays and the TGF-β1, β2 and β3 were measured by a multiplex assay. Cardiovascular events were registered using national registers, patient records and telephone calls during a follow-up period of 59 months IQR (34–73).

Results

Mimecan was expressed in human atherosclerotic plaques. The expression correlated positively with neutral lipids and intraplaque hemorrhage and inversely with α-actin. In contrast cMIM correlated with α-actin and inversely with neutral lipids. cMIM correlated also with stabilizing extracellular matrix proteins elastin, collagen as well as TGF-β1, β2 and β3. Mimecan correlated to MMP-9 and cMIM correlated to MMP-2 and TIMP-2. Patient with high levels of cMIM had a lower risk of future cardiovascular events which remained significant after adjusting for risk factors (age, gender, diabetes and symptoms) in a multivariate Cox regression

Conclusion(s)

Mimecan was associated with vulnerable plaque features, whereas cMIM was related to stable plaque features. Low levels of cMIM predicted future cardiovascular events, independently of known risk factors. Taken together this suggests a possible role for mimecan and its cleavage in atherosclerosis that needs to be further explored.

Acknowledgement/Funding

Danish Research Foundation, Swedish Research Council, Swedish Heart and Lung Foundation, Skåne University Hospital and more.

A biomarker of collagen type I degradation is associated with cardiovascular events and mortality in patients with atherosclerosis

OBJECTIVE

Atherosclerosis is characterized by accumulation of lipids, cells and extracellular matrix (ECM) proteins in the arterial wall. Collagen type I (COL1), a component of the arterial ECM, is cleaved by matrix metalloproteinases (MMPs) and known to be remodelled in atherosclerosis. We explored whether the MMP-mediated COL1 biomarker, C1M, was associated with cardiovascular events, cardiovascular mortality and all-cause mortality in a large prospective cohort of patients with known atherosclerosis.

METHODS

Serum from 787 patients who underwent a carotid endarterectomy was included. Circulating levels of C1M were measured in serum. A total of 473 patients were followed for 6 years after surgery. Associations between C1M and incidence of cardiovascular events, cardiovascular mortality and all-cause mortality were assessed by Kaplan-Meier curves and Cox regression analysis.

RESULTS

A total of 101 (21.4%) patients suffered from nonfatal cardiovascular events during the follow-up period, and 64 (13.5%) patients died. Of these, 39 (60.9%) died from cardiovascular diseases. Patients with C1M levels above the median were significantly associated with cardiovascular events, cardiovascular mortality and all-cause mortality (P < 0.001, P = 0.004 and P < 0.001, respectively). C1M was included in the final model for prediction of cardiovascular events (HR 2.15, 95% CI 1.40-3.32, P = 0.001), cardiovascular mortality (HR 2.20, 95% CI 1.07-4.51, P = 0.031) and all-cause mortality (HR 2.98 95% CI 1.67-5.33, P = < 0.001).

CONCLUSIONS

In patients with atherosclerotic carotid lesions, high levels of C1M predicted cardiovascular events, cardiovascular mortality and all-cause mortality. These findings emphasize the importance of remodelling mechanisms in atherosclerosis that are now becoming more and more explored.

Autoantibodies against aldehyde-modified collagen type IV are associated with risk of development of myocardial infarction

BACKGROUND

Oxidation of LDL particles entrapped in the extracellular matrix of the arterial wall is a key factor in the development of atherosclerosis. Lipid oxidation products, such as malondialdehyde (MDA), react with surrounding extracellular matrix proteins and cause modifications that are recognized by the immune system. MDA modification of collagen type IV is increased in carotid lesions from symptomatic patients and correlates with autoantibodies against MDA-modified collagen type IV in plasma.

OBJECTIVE

The aim of this study was to determine whether autoantibodies against MDA-modified collagen type IV predict risk of development of myocardial infarction (MI).

METHODS

Plasma levels of MDA-modified collagen type IV IgM and IgG antibodies were analysed by enzyme-linked immunosorbent assay in 385 subjects with incident MI during 13 years of follow-up and 410 age- and sex-matched controls in the Malmö Diet and Cancer study.

RESULTS

MDA-modified collagen type IV IgG levels were higher in cases with incident MI than in controls. Subjects in the highest tertile of MDA-modified collagen type IV IgG had an increased risk of MI (hazard ratio 1.56, 95% confidence interval 1.22-2.00, P for trend 0.0004). This association remained significant after adjusting for factors included in the Framingham risk score and diabetes. High levels of MDA-collagen type IV IgG were associated with increased carotid intima-media thickness and elevated plasma levels of matrix metalloproteinase 10 and 12.

CONCLUSIONS

Immune responses against MDA-modified collagen type IV are associated with more severe carotid disease and increased risk of MI. These immune responses may reflect LDL oxidation in the artery wall, but could also affect the atherosclerotic disease process.

Plasma stem cell factor levels are associated with risk of cardiovascular disease and death

OBJECTIVE

Stem cell factor (SCF) is a key growth factor for several types of stem and progenitor cells. There is experimental evidence that such cells are of importance for maintaining the integrity of the cardiovascular system. We investigated the association between circulating levels of SCF and risk for development of cardiovascular events and death.

METHODS

SCF was analysed by the proximity extension assay technique in plasma from 4742 subjects participating in the Malmö Diet and Cancer Study. Cardiovascular events and death were monitored through national registers with a mean follow-up time of 19.2 years.

RESULTS

Subjects with high baseline levels of SCF had lower cardiovascular (n = 340) and all-cause mortality (n = 1159) as well as a lower risk of heart failure (n = 177), stroke (n = 318) and myocardial infarction (n = 452). Smoking, diabetes and high alcohol consumption were associated with lower levels of SCF. Single nucleotide polymorphisms in the gene region encoding PDX1 C-terminal inhibiting factor 1 (PCIF1) and matrix metalloproteinase-9 were associated with plasma SCF levels. The highest SCF quartile remained independently associated with a lower risk of a lower risk of cardiovascular [hazard ratio and 95% confidence interval 0.59 (0.43-0.81)] and all-cause mortality [0.68 (0.57-0.81)], heart failure [0.50 (0.31-0.80)] and stroke [0.66 (0.47-0.92)], but not with MI [0.96 (0.72-1.27)] as compared with the lowest quartile when adjusting for traditional cardiovascular risk factors in Cox proportional hazard regression models.

CONCLUSIONS

This prospective population-based study demonstrates that subjects with high levels of SCF have a lower risk of cardiovascular events and death. The findings provide clinical support for a protective role of SCF in maintaining cardiovascular integrity.

Human Lipoproteins at Model Cell Membranes: Effect of Lipoprotein Class on Lipid Exchange

High and low density lipoproteins (HDL and LDL) are thought to play vital roles in the onset and development of atherosclerosis; the biggest killer in the western world. Key issues of initial lipoprotein (LP) interactions at cellular membranes need to be addressed including LP deposition and lipid exchange. Here we present a protocol for monitoring the in situ kinetics of lipoprotein deposition and lipid exchange/removal at model cellular membranes using the non-invasive, surface sensitive methods of neutron reflection and quartz crystal microbalance with dissipation. For neutron reflection, lipid exchange and lipid removal can be distinguished thanks to the combined use of hydrogenated and tail-deuterated lipids. Both HDL and LDL remove lipids from the bilayer and deposit hydrogenated material into the lipid bilayer, however, the extent of removal and exchange depends on LP type. These results support the notion of HDL acting as the ‘good’ cholesterol, removing lipid material from lipid-loaded cells, whereas LDL acts as the ‘bad’ cholesterol, depositing lipid material into the vascular wall.

Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation

Vascular segmentation plays an important role in the assessment of peripheral arterial disease. The segmentation is very challenging especially for arteries with severe stenosis or complete occlusion. We present a cascading algorithm for vascular centerline tree detection specializing in detecting centerlines in diseased peripheral arteries. It takes a three-dimensional computed tomography angiography (CTA) volume and returns a vascular centerline tree, which can be used for accelerating and facilitating the vascular segmentation. The algorithm consists of four levels, two of which detect healthy arteries of varying sizes and two that specialize in different types of vascular pathology: severe calcification and occlusion. We perform four main steps at each level: appropriate parameters for each level are selected automatically, a set of centrally located voxels is detected, these voxels are connected together based on the connection criteria, and the resulting centerline tree is corrected from spurious branches. The proposed method was tested on 25 CTA scans of the lower limbs, achieving an average overlap rate of 89% and an average detection rate of 82%. The average execution time using four CPU cores was 70 s, and the technique was successful also in detecting very distal artery branches, e.g., in the foot.

Decreased levels of stem cell factor in subjects with incident coronary events

BACKGROUND

It has been proposed that vascular progenitor cells play an important role in vascular repair, but their possible clinical importance in cardiovascular disease has not been fully characterized. Vascular endothelial growth factor A, placental growth factor and stem cell factor (SCF) are three growth factors that are important in recruiting vascular progenitor cells. In this study, we investigated the association between the plasma levels of these growth factors and incident coronary events (CEs).

METHODS

Levels of the three growth factors were measured using the proximity extension assay technique in baseline plasma samples from 384 subjects with a first CE (mean follow-up 14.0 ± 4.3 years) and 409 event-free control subjects matched by sex and age, as well as in homogenates from 201 endarterectomy specimens.

RESULTS

After controlling for known cardiovascular disease risk factors in a Cox regression model, subjects in the lowest SCF tertile had a hazard ratio of 1.70 (95% confidence interval 1.14-2.54) compared with subjects in the highest SCF tertile. Lower SCF levels were also associated with more severe carotid disease, less fibrous atherosclerotic plaques and an increased incidence of heart failure. Expression of the SCF receptor c-kit was demonstrated in the subendothelial layer and fibrous cap of human atherosclerotic plaques. Smokers and subjects with diabetes had decreased levels of SCF compared with control subjects.

CONCLUSION

To our knowledge, this is the first clinical study to provide evidence to support a key role for SCF and progenitor cells in vascular repair. We suggest that the SCF-c-kit pathway may be a promising biomarker and therapeutic target in cardiovascular disease.

Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors

The assessment of the state of the acrosome is a priority in artificial insemination centres since it is one of the main causes of function loss. In this work, boar spermatozoa present in gray scale images acquired with a phase-contrast microscope have been classified as acrosome-intact or acrosome-damaged, after using fluorescent images for creating the ground truth. Based on shape prior criteria combined with Otsu's thresholding, regional minima and watershed transform, the spermatozoa heads were segmented and registered. One of the main novelties of this proposal is that, unlike what previous works stated, the obtained results show that the contour information of the spermatozoon head is important for improving description and classification. Other of this work novelties is that it confirms that combining different texture descriptors and contour descriptors yield the best classification rates for this problem up to date. The classification was performed with a Support Vector Machine backed by a Least Squares training algorithm and a linear kernel. Using the biggest acrosome intact-damaged dataset ever created, the early fusion approach followed provides a 0.9913 F-Score, outperforming all previous related works.

Simulation of bright-field microscopy images depicting pap-smear specimen

As digital imaging is becoming a fundamental part of medical and biomedical research, the demand for computer-based evaluation using advanced image analysis is becoming an integral part of many research projects. A common problem when developing new image analysis algorithms is the need of large datasets with ground truth on which the algorithms can be tested and optimized. Generating such datasets is often tedious and introduces subjectivity and interindividual and intraindividual variations. An alternative to manually created ground-truth data is to generate synthetic images where the ground truth is known. The challenge then is to make the images sufficiently similar to the real ones to be useful in algorithm development. One of the first and most widely studied medical image analysis tasks is to automate screening for cervical cancer through Pap-smear analysis. As part of an effort to develop a new generation cervical cancer screening system, we have developed a framework for the creation of realistic synthetic bright-field microscopy images that can be used for algorithm development and benchmarking. The resulting framework has been assessed through a visual evaluation by experts with extensive experience of Pap-smear images. The results show that images produced using our described methods are realistic enough to be mistaken for real microscopy images. The developed simulation framework is very flexible and can be modified to mimic many other types of bright-field microscopy images. © 2015 The Authors. Published by Wiley Periodicals, Inc. on behalf of ISAC

Debris removal in Pap-smear images

Since its introduction in the 1940s the Pap-smear test has helped reduce the incidence of cervical cancer dramatically in countries where regular screening is standard. The automation of this procedure is an open problem that has been ongoing for over fifty years without reaching satisfactory results. Existing systems are discouragingly expensive and yet they are only able to make a correct distinction between normal and abnormal samples in a fraction of cases. Therefore, they are limited to acting as support for the cytotechnicians as they perform their manual screening. The main reason for the current limitations is that the automated systems struggle to overcome the complexity of the cell structures. Samples are covered in artefacts such as blood cells, overlapping and folded cells, and bacteria, that hamper the segmentation processes and generate large number of suspicious objects. The classifiers designed to differentiate between normal cells and pre-cancerous cells produce unpredictable results when classifying artefacts. In this paper, we propose a sequential classification scheme focused on removing unwanted objects, debris, from an initial segmentation result, intended to be run before the actual normal/abnormal classifier. The method has been evaluated using three separate datasets obtained from cervical samples prepared using both the standard Pap-smear approach as well as the more recent liquid based cytology sample preparation technique. We show success in removing more than 99% of the debris without loosing more than around one percent of the epithelial cells detected by the segmentation process.

Blind color decomposition of histological images

Cancer diagnosis is based on visual examination under a microscope of tissue sections from biopsies. But whereas pathologists rely on tissue stains to identify morphological features, automated tissue recognition using color is fraught with problems that stem from image intensity variations due to variations in tissue preparation, variations in spectral signatures of the stained tissue, spectral overlap and spatial aliasing in acquisition, and noise at image acquisition. We present a blind method for color decomposition of histological images. The method decouples intensity from color information and bases the decomposition only on the tissue absorption characteristics of each stain. By modeling the charge-coupled device sensor noise, we improve the method accuracy. We extend current linear decomposition methods to include stained tissues where one spectral signature cannot be separated from all combinations of the other tissues' spectral signatures. We demonstrate both qualitatively and quantitatively that our method results in more accurate decompositions than methods based on non-negative matrix factorization and independent component analysis. The result is one density map for each stained tissue type that classifies portions of pixels into the correct stained tissue allowing accurate identification of morphological features that may be linked to cancer.

Automated segmentation of free-lying cell nuclei in Pap smears for malignancy-associated change analysis

This paper presents an automated algorithm for robustly detecting and segmenting free-lying cell nuclei in bright-field microscope images of Pap smears. This is an essential initial step in the development of an automated screening system for cervical cancer based on malignancy associated change (MAC) analysis. The proposed segmentation algorithm makes use of gray-scale annular closings to identify free-lying nuclei-like objects together with marker-based watershed segmentation to accurately delineate the nuclear boundaries. The algorithm also employs artifact rejection based on size, shape, and granularity to ensure only the nuclei of intermediate squamous epithelial cells are retained. An evaluation of the performance of the algorithm relative to expert manual segmentation of 33 fields-of-view from 11 Pap smear slides is also presented. The results show that the sensitivity and specificity of nucleus detection is 94.71% and 85.30% respectively, and that the accuracy of segmentation, measured using the Dice coefficient, of the detected nuclei is 97.30±1.3%.

High levels of IgM against methylglyoxal-modified apolipoprotein B100 are associated with less coronary artery calcification in patients with type 2 diabetes

OBJECTIVE

  Advanced glycation end products (AGE) have been implicated in diabetic vascular complications through activation of pro-inflammatory genes. AGE-modified proteins are also targeted by the immune system resulting in the generation of AGE-specific autoantibodies, but the association of these immune responses with diabetic vasculopathy remains to be fully elucidated. The aim of this study was to determine whether antibodies against apolipoprotein B100 modified by methylglyoxal (MGO-apoB100) are associated with coronary atherosclerosis in patients with type 2 diabetes.

METHODS

  We measured antibodies against MGO-apoB100 in plasma from 497 type 2 diabetic patients without clinical signs of cardiovascular disease. Severity of coronary disease was assessed as coronary artery calcium (CAC) imaging. Immunoglobulin (Ig)M and IgG levels recognizing MGO-apoB100 were determined by enzyme-linked immunosorbent assay.

RESULTS

  Anti-MGO-apoB100 IgM antibody levels were higher in subjects with a low to moderate CAC score (≤400 Agatston units) than in subjects with a high score (>400 Agatston units; 136.8±4.4 vs. 101.6± 7.4 arbitrary units (AU), P<0.0001) and in subjects demonstrating no progression of CAC during 30 months of follow-up (136.4±5.7 vs. 113.9 ± 6.2 AU in subjects with progression, P<0.0001). Subjects with a family history of premature myocardial infarction had lower levels of anti-MGO-apoB100 IgM. Female subjects had higher levels of anti-MGO-apoB100 antibodies and lower CAC than men. Accordingly, high levels of IgM against MGO-apoB100 are associated with less severe and a lower risk of progression of coronary disease in subjects with type 2 diabetes.

CONCLUSIONS

Although conclusions regarding causal relationships based on epidemiological observations need to be made with caution, our findings suggest the possibility that anti-MGO-apoB100 IgM may be protective in diabetic vasculopathy.

Characterization and reduction of noise in dynamic PET data using masked volumewise principal component analysis

Masked volumewise principal component (PC) analysis (PCA) is used in PET to distinguish structures that display different kinetic behaviors after administration of a tracer. When masked volumewise PCA was introduced, one article proposed noise prenormalization because of temporal and spatial variations of the noise between slices. However, the noise prenormalization proposed in that article was applicable only to datasets reconstructed using filtered backprojection (FBP). The study presented in this article aimed at developing a new noise prenormalization that is applicable to datasets regardless of whether they were reconstructed with FBP or an iterative reconstruction algorithm, such as ordered-subset expectation maximization (OSEM).

METHODS

A phantom study was performed to investigate differences in the expectation values and SDs of datasets reconstructed with FBP and OSEM. A novel method, higher-order PC noise prenormalization, was suggested and evaluated against other prenormalization methods on clinical datasets.

RESULTS

Masked volumewise PCA of data reconstructed with FBP was much more dependent on an appropriate prenormalization than was analysis of data reconstructed with OSEM. Higher-order PC noise prenormalization showed an overall good performance with both FBP and OSEM reconstructions, whereas the other prenormalization methods performed well with only 1 of the 2 methods.

CONCLUSION

Higher-order PC noise prenormalization has potential for improving the results from masked volumewise PCA on dynamic PET datasets independent of the type of reconstruction algorithm.

Effects of aging and gender on the spatial organization of nuclei in single human skeletal muscle cells

The skeletal muscle fibre is a syncitium where each myonucleus regulates the gene products in a finite volume of the cytoplasm, i.e., the myonuclear domain (MND). We analysed aging- and gender-related effects on myonuclei organization and the MND size in single muscle fibres from six young (21-31 years) and nine old men (72-96 years), and from six young (24-32 years) and nine old women (65-96 years), using a novel image analysis algorithm applied to confocal images. Muscle fibres were classified according to myosin heavy chain (MyHC) isoform expression. Our image analysis algorithm was effective in determining the spatial organization of myonuclei and the distribution of individual MNDs along the single fibre segments. Significant linear relations were observed between MND size and fibre size, irrespective age, gender and MyHC isoform expression. The spatial organization of individual myonuclei, calculated as the distribution of nearest neighbour distances in 3D, and MND size were affected in old age, but changes were dependent on MyHC isoform expression. In type I muscle fibres, average NN-values were lower and showed an increased variability in old age, reflecting an aggregation of myonuclei in old age. Average MND size did not change in old age, but there was an increased MND size variability. In type IIa fibres, average NN-values and MND sizes were lower in old age, reflecting the smaller size of these muscle fibres in old age. It is suggested that these changes have a significant impact on protein synthesis and degradation during the aging process.

Signal extraction and separation in in vivo animal PET studies with masked volumewise principal-component analysis

The standardized uptake value is commonly used as a tool to supplement visual interpretation and to quantify the images acquired from static in vivo animal PET. The preferred approach for analyzing PET data is either to sum the images and calculate the standardized uptake value or to use kinetic modeling. The aim of this study was to investigate the performance of masked volumewise principal-component analysis (MVW-PCA) used in dynamic in vivo animal PET studies to extract and separate signals with different kinetic behaviors.

METHODS

PET data were acquired with a small-animal PET scanner and a fluorine tracer in a study of rats and mice. After acquisition, the data were reconstructed by use of 4 time protocols with different frame lengths. Data were analyzed by use of MVW-PCA with applied noise prenormalization and a new masking technique developed in this study.

RESULTS

The resulting principal-component images showed a clear separation of the activity in the spine into the first MVW-PCA component and the activity in the kidneys into the second MVW-PCA component. In addition, the different time protocols were shown to have little or no impact on the results obtained with MVW-PCA.

CONCLUSION

MVW-PCA can efficiently separate different kinetic behaviors into different principal-component images. Moreover, MVW-PCA is a stable technique in the sense that the time protocol chosen has only a small impact on the resulting principal-component images.

Improved methodology for identifying the teratogenic potential in early drug development of hERG channel blocking drugs

Drugs blocking the potassium current IKr of the heart (via hERG channel-inhibition) have the potential to cause hypoxia-related teratogenic effects. However, this activity may be missed in conventional teratology studies because repeat dosing may cause resorptions. The aim of the present study was to investigate an alternative protocol to reveal the teratogenic potential of IKr-blocking drugs. The IKr blocker astemizole, given as a single dose (80 mg/kg) on gestation day (GD) 13 to pregnant rats caused digital defects. In whole rat embryo culture (2h) on GD 13, astemizole caused a decrease in embryonic heart rate at 20 nM, and arrhythmias at 200-400 nM. Cetirizine, without IKr-blocking properties, did not affect the rat embryonic heart in vitro. The present study shows that single dose testing on sensitive days of development, together with whole embryo culture, can be a useful methodology to better characterize the teratogenic potential of IKr-blocking drugs.

Associations between autoantibodies against apolipoprotein B-100 peptides and vascular complications in patients with type 2 diabetes

AIMS/HYPOTHESIS

Oxidation of LDL in the arterial extracellular matrix is a key event in the development of atherosclerosis and autoantibodies against oxidised LDL antigens reflect disease severity and the risk of developing acute cardiovascular events. Since type 2 diabetes is associated with increased oxidative stress, we tested the hypothesis that autoantibodies against oxidised LDL antigens are biomarkers for vascular complications in diabetes.

METHODS

We studied 497 patients with type 2 diabetes without clinical signs of coronary heart disease. Oxidised LDL autoantibodies were determined by ELISA detecting IgG and IgM specific for native and malondialdehyde (MDA)-modified apolipoprotein B-100 peptides p45 and p210. The severity of coronary disease was assessed as the coronary artery calcium score.

RESULTS

Patients affected by retinopathy had significantly higher levels of IgG against MDA-p45 and MDA-p210. In contrast, high levels of autoantibodies against the corresponding native peptides were associated with less coronary calcification and a lower risk of progression of coronary disease.

CONCLUSIONS/INTERPRETATION

Our observations suggest that LDL oxidation is involved in the pathogenesis of diabetic retinopathy and that autoantibodies against apolipoprotein B peptides may act as biomarkers for both micro- and macrovascular complications in diabetes.

Immune responses against fibronectin modified by lipoprotein oxidation and their association with cardiovascular disease

OBJECTIVES

Accumulation and subsequent oxidation of LDL in the arterial wall are considered as key events in the development of atherosclerosis. We have investigated the possibility that LDL oxidation results in release of aldehydes that modify surrounding matrix proteins and that this may target immune responses against the plaque extracellular matrix and modulate the disease progression.

RESULTS

Using custom-made ELISAs we demonstrate that human plasma contains autoantibodies against aldehyde-modified fibronectin (FN) and to a lesser extent also other extracellular matrix proteins including collagen type I, type III, and tenascin-C. Immunohistochemistry and western blot analysis showed that aldehyde-modified FN is present in human atherosclerotic plaques and that aldehydes generated by oxidation of LDL formed adducts with FN in vitro. We also demonstrate that aldehyde-modification of FN results in a loss of its ability to promote basal secretion of cytokines and growth factors from cultured macrophages without affecting the ability of the cells to respond to stimulation with LPS. A prospective clinical study demonstrated that subjects that subsequently developed acute myocardial infarction or sudden cardiac death had lower baseline levels of autoantibodies against aldehyde-modified FN than matched controls.

CONCLUSIONS

These observations demonstrate that oxidation of LDL in the arterial wall may lead to aldehyde-modification of surrounding extracellular matrix proteins and that these modifications may affect macrophage function and activate autoimmune responses of pathophysiological importance for the development of atherosclerosis.

Performance of principal component analysis and independent component analysis with respect to signal extraction from noisy positron emission tomography data - a study on computer simulated images

Multivariate image analysis tools are used for analyzing dynamic or multidimensional Positron Emission Tomography, PET data with the aim of noise reduction, dimension reduction and signal separation. Principal Component Analysis is one of the most commonly used multivariate image analysis tools, applied on dynamic PET data. Independent Component Analysis is another multivariate image analysis tool used to extract and separate signals. Because of the presence of high and variable noise levels and correlation in the different PET images which may confound the multivariate analysis, it is essential to explore and investigate different types of pre-normalization (transformation) methods that need to be applied, prior to application of these tools. In this study, we explored the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) to extract signals and reduce noise, thereby increasing the Signal to Noise Ratio (SNR) in a dynamic sequence of PET images, where the features of the noise are different compared with some other medical imaging techniques. Applications on computer simulated PET images were explored and compared. Application of PCA generated relatively similar results, with some minor differences, on the images with different noise characteristics. However, clear differences were seen with respect to the type of pre-normalization. ICA on images normalized using two types of normalization methods also seemed to perform relatively well but did not reach the improvement in SNR as PCA. Furthermore ICA seems to have a tendency under some conditions to shift over information from IC1 to other independent components and to be more sensitive to the level of noise. PCA is a more stable technique than ICA and creates better results both qualitatively and quantitatively in the simulated PET images. PCA can extract the signals from the noise rather well and is not sensitive to type of noise, magnitude and correlation, when the input data are correctly handled by a proper pre-normalization. It is important to note that PCA as inherently a method to separate signal information into different components could still generate PC1 images with improved SNR as compared to mean images.

Levels of metrifonate and dichlorvos in plasma and erythrocytes during treatment of schistosomiasis with Bilarcil

A method for the simultaneous quantitation of metrifonate (0,0-dimethyl-(1-hydroxy-2,2,2-trichloroethyl)-phosphonate) and dichlorvos (2,2-dichlorovinyl dimethyl phosphate, DDVP) in human blood has been worked out. It is based upon multiple labelling of the compounds with deuterium and gas phase analysis using the mass spectrometer as a selective detector. The amount of DDVP in plasma is about 1% of the amount of metrifonate. In erythrocytes the corresponding amount of DDVP is 0.5% or less of metrifonate. Both compounds reach peak levels in blood within two hours after oral dosing and are detectable for at least eight hours. Cholinesterase activity in plasma reaches zero levels within 15 min. and remains inhibited for more than eight hours. Red blood cell cholinesterase is inhibited only 60-80%. According to kinetic calculations, clearance of metrifonate occurs primarily via dichlorvos. If dichlorvos is the only active component, which in all likelihood it is, it's slow release may be important in the schistosomicidal effect. Clinical data in seven metrifonate treated patients revealed that mild vertigo subsiding in a few hours was the most common side effect.

A new application of pre-normalized principal component analysis for improvement of image quality and clinical diagnosis in human brain PET studies—Clinical brain studies using [11C]-GR205171, [11C]-l-deuterium-deprenyl, [11C]-5-Hydroxy-l-Tryptophan, [11C]-l-DOPA and Pittsburgh Compound-B

Principal component analysis (PCA) is one of the most applied multivariate image analysis tool on dynamic Positron Emission Tomography (PET). Independent of used reconstruction methodologies, PET images contain correlation in-between pixels, correlations in-between frame and errors caused by the reconstruction algorithm including different corrections, which can affect the performance of the PCA. In this study, we have investigated a new approach of application of PCA on pre-normalized, dynamic human PET images. A range of different tracers have been used for this purpose to explore the performance of the new method as a way to improve detection and visualization of significant changes in tracer kinetics and to enhance the discrimination between pathological and healthy regions in the brain. We compare the new results with the results obtained using other methods. Images generated using the new approach contain more detailed anatomical information with higher quality, precision and visualization, compared with images generated using other methods.

Noise correlation in PET, CT, SPECT and PET/CT data evaluated using autocorrelation function: a phantom study on data, reconstructed using FBP and OSEM

Background

Positron Emission Tomography (PET), Computed Tomography (CT), PET/CT and Single Photon Emission Tomography (SPECT) are non-invasive imaging tools used for creating two dimensional (2D) cross section images of three dimensional (3D) objects. PET and SPECT have the potential of providing functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules, whereas CT visualizes X-ray density in tissues in the body. PET/CT provides fused images representing both functional and anatomical information with better precision in localization than PET alone.

Images generated by these types of techniques are generally noisy, thereby impairing the imaging potential and affecting the precision in quantitative values derived from the images. It is crucial to explore and understand the properties of noise in these imaging techniques. Here we used autocorrelation function (ACF) specifically to describe noise correlation and its non-isotropic behaviour in experimentally generated images of PET, CT, PET/CT and SPECT.

Methods

Experiments were performed using phantoms with different shapes. In PET and PET/CT studies, data were acquired in 2D acquisition mode and reconstructed by both analytical filter back projection (FBP) and iterative, ordered subsets expectation maximisation (OSEM) methods. In the PET/CT studies, different magnitudes of X-ray dose in the transmission were employed by using different mA settings for the X-ray tube. In the CT studies, data were acquired using different slice thickness with and without applied dose reduction function and the images were reconstructed by FBP. SPECT studies were performed in 2D, reconstructed using FBP and OSEM, using post 3D filtering. ACF images were generated from the primary images, and profiles across the ACF images were used to describe the noise correlation in different directions. The variance of noise across the images was visualised as images and with profiles across these images.

Results

The most important finding was that the pattern of noise correlation is rotation symmetric or isotropic, independent of object shape in PET and PET/CT images reconstructed using the iterative method. This is, however, not the case in FBP images when the shape of phantom is not circular. Also CT images reconstructed using FBP show the same non-isotropic pattern independent of slice thickness and utilization of care dose function. SPECT images show an isotropic correlation of the noise independent of object shape or applied reconstruction algorithm. Noise in PET/CT images was identical independent of the applied X-ray dose in the transmission part (CT), indicating that the noise from transmission with the applied doses does not propagate into the PET images showing that the noise from the emission part is dominant. The results indicate that in human studies it is possible to utilize a low dose in transmission part while maintaining the noise behaviour and the quality of the images.

Conclusion

The combined effect of noise correlation for asymmetric objects and a varying noise variance across the image field significantly complicates the interpretation of the images when statistical methods are used, such as with statistical estimates of precision in average values, use of statistical parametric mapping methods and principal component analysis. Hence it is recommended that iterative reconstruction methods are used for such applications. However, it is possible to calculate the noise analytically in images reconstructed by FBP, while it is not possible to do the same calculation in images reconstructed by iterative methods. Therefore for performing statistical methods of analysis which depend on knowing the noise, FBP would be preferred.

Non-isotropic noise correlation in PET data reconstructed by FBP but not by OSEM demonstrated using auto-correlation function

Background

Positron emission tomography (PET) is a powerful imaging technique with the potential of obtaining functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules in a biological system, both in vitro and in vivo. PET images can be used directly or after kinetic modelling to extract quantitative values of a desired physiological, biochemical or pharmacological entity. Because such images are generally noisy, it is essential to understand how noise affects the derived quantitative values. A pre-requisite for this understanding is that the properties of noise such as variance (magnitude) and texture (correlation) are known.

Methods

In this paper we explored the pattern of noise correlation in experimentally generated PET images, with emphasis on the angular dependence of correlation, using the autocorrelation function (ACF). Experimental PET data were acquired in 2D and 3D acquisition mode and reconstructed by analytical filtered back projection (FBP) and iterative ordered subsets expectation maximisation (OSEM) methods. The 3D data was rebinned to a 2D dataset using FOurier REbinning (FORE) followed by 2D reconstruction using either FBP or OSEM. In synthetic images we compared the ACF results with those from covariance matrix. The results were illustrated as 1D profiles and also visualized as 2D ACF images.

Results

We found that the autocorrelation images from PET data obtained after FBP were not fully rotationally symmetric or isotropic if the object deviated from a uniform cylindrical radioactivity distribution. In contrast, similar autocorrelation images obtained after OSEM reconstruction were isotropic even when the phantom was not circular. Simulations indicated that the noise autocorrelation is non-isotropic in images created by FBP when the level of noise in projections is angularly variable. Comparison between 1D cross profiles on autocorrelation images obtained by FBP reconstruction and covariance matrices produced almost identical results in a simulation study.

Conclusion

With asymmetric radioactivity distribution in PET, reconstruction using FBP, in contrast to OSEM, generates images in which the noise correlation is non-isotropic when the noise magnitude is angular dependent, such as in objects with asymmetric radioactivity distribution. In this respect, iterative reconstruction is superior since it creates isotropic noise correlations in the images.

Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections

We present a region-based segmentation method in which seeds representing both object and background pixels are created by combining morphological filtering of both the original image and the gradient magnitude of the image. The seeds are then used as starting points for watershed segmentation of the gradient magnitude image. The fully automatic seeding is done in a generous fashion, so that at least one seed will be set in each foreground object. If more than one seed is placed in a single object, the watershed segmentation will lead to an initial over-segmentation, i.e. a boundary is created where there is no strong edge. Thus, the result of the initial segmentation is further refined by merging based on the gradient magnitude along the boundary separating neighbouring objects. This step also makes it easy to remove objects with poor contrast. As a final step, clusters of nuclei are separated, based on the shape of the cluster. The number of input parameters to the full segmentation procedure is only five. These parameters can be set manually using a test image and thereafter be used on a large number of images created under similar imaging conditions. This automated system was verified by comparison with manual counts from the same image fields. About 90% correct segmentation was achieved for two- as well as three-dimensional images.

Image analysis for automatic segmentation of cytoplasms and classification of Rac1 activation

BACKGROUND

Rac1 is a GTP-binding molecule involved in a wide range of cellular processes. Using digital image analysis, agonist-induced translocation of green fluorescent protein (GFP) Rac1 to the cellular membrane can be estimated quantitatively for individual cells.

METHODS

A fully automatic image analysis method for cell segmentation, feature extraction, and classification of cells according to their activation, i.e., GFP-Rac1 translocation and ruffle formation at stimuli, is described. Based on training data produced by visual annotation of four image series, a statistical classifier was created.

RESULTS

The results of the automatic classification were compared with results from visual inspection of the same time sequences. The automatic classification differed from the visual classification at about the same level as visual classifications performed by two different skilled professionals differed from each other. Classification of a second image set, consisting of seven image series with different concentrations of agonist, showed that the classifier could detect an increased proportion of activated cells at increased agonist concentration.

CONCLUSIONS

Intracellular activities, such as ruffle formation, can be quantified by fully automatic image analysis, with an accuracy comparable to that achieved by visual inspection. This analysis can be done at a speed of hundreds of cells per second and without the subjectivity introduced by manual judgments.