Combining diagnostic test results to increase accuracy

When multiple diagnostic tests are performed on an individual or multiple disease markers are available it may be possible to combine the information to diagnose disease. We consider how to choose linear combinations of markers in order to optimize diagnostic accuracy. The accuracy index to be maximized is the area or partial area under the receiver operating characteristic (ROC) curve. We propose a distribution-free rank-based approach for optimizing the area under the ROC curve and compare it with logistic regression and with classic linear discriminant analysis (LDA). It has been shown that the latter method optimizes the area under the ROC curve when test results have a multivariate normal distribution for diseased and non-diseased populations. Simulation studies suggest that the proposed non-parametric method is efficient when data are multivariate normal.The distribution-free method is generalized to a smooth distribution-free approach to: (i) accommodate some reasonable smoothness assumptions; (ii) incorporate covariate effects; and (iii) yield optimized partial areas under the ROC curve. This latter feature is particularly important since it allows one to focus on a region of the ROC curve which is of most relevance to clinical practice. Neither logistic regression nor LDA necessarily maximize partial areas. The approaches are illustrated on two cancer datasets, one involving serum antigen markers for pancreatic cancer and the other involving longitudinal prostate specific antigen data.     

Test Samples for Optimizing STORM Super-Resolution Microscopy

STORM is a recently developed super-resolution microscopy technique with up to 10 times better resolution than standard fluorescence microscopy techniques. However, as the image is acquired in a very different way than normal, by building up an image molecule-by-molecule, there are some significant challenges for users in trying to optimize their image acquisition. In order to aid this process and gain more insight into how STORM works we present the preparation of 3 test samples and the methodology of acquiring and processing STORM super-resolution images with typical resolutions of between 30-50 nm. By combining the test samples with the use of the freely available rainSTORM processing software it is possible to obtain a great deal of information about image quality and resolution. Using these metrics it is then possible to optimize the imaging procedure from the optics, to sample preparation, dye choice, buffer conditions, and image acquisition settings. We also show examples of some common problems that result in poor image quality, such as lateral drift, where the sample moves during image acquisition and density related problems resulting in the ''mislocalization'' phenomenon.     

Regression models for convex ROC curves

The performance of a diagnostic test is summarized by its receiver operating characteristic (ROC) curve. Under quite natural assumptions about the latent variable underlying the test, the ROC curve is convex. Empirical data on a test's performance often comes in the form of observed true positive and false positive relative frequencies under varying conditions. This paper describes a family of regression models for analyzing such data. The underlying ROC curves are specified by a quality parameter delta and a shape parameter mu and are guaranteed to be convex provided delta > 1. Both the position along the ROC curve and the quality parameter delta are modeled linearly with covariates at the level of the individual. The shape parameter mu enters the model through the link functions log(p mu) - log(1 - p mu) of a binomial regression and is estimated either by search or from an appropriate constructed variate. One simple application is to the meta-analysis of independent studies of the same diagnostic test, illustrated on some data of Moses, Shapiro, and Littenberg (1993). A second application, to so-called vigilance data, is given, where ROC curves differ across subjects and modeling of the position along the ROC curve is of primary interest.     

CT iterative reconstruction in image space: A phantom study

Although iterative reconstruction is widely applied in SPECT/PET, its introduction in clinical CT is quite recent, in the past the demand for extensive computer power and long image reconstruction times have stopped the diffusion of this technique. Recently Iterative Reconstruction in Image Space (IRIS) has been introduced on Siemens top CT scanners. This recon method works on image data area, reducing the time-consuming loops on raw data and noise removal is obtained in subsequent iterative steps with a smoothing process. We evaluated image noise, low contrast resolution, CT number linearity and accuracy, transverse and z-axis spatial resolution using some dedicated phantoms in single, dual source and cardiac mode. We reconstructed images with a traditional filtered back-projection algorithm and with IRIS. The iterative procedure preserves spatial resolution, CT number accuracy and linearity moreover decreases image noise. These preliminary results support the idea that dose reduction with preserved image quality is possible with IRIS, even if studies on patients are necessary to confirm these data.     

In silico analysis of the restriction fragments length distribution in the human genome.]

The Restriction On Computer (ROC) program (freely available at http://www.mcb.harvard.edu/gilbert/ROC) was developed and used to analyze the restriction fragment length distribution in the human genome. In contrast to other programs searching for restriction sites, ROC simultaneously analyzes several long nucleotide sequences, such as the entire genomes, and in essence simulates electrophoretic analysis of DNA restriction fragments. In addition, this program extracts and analyzes DNA repeats that account for peaks in the restriction fragment length distribution. The ROC analysis data are consistent with the experimental data obtained via in vitro restriction enzyme analysis (taxonomic printing). A difference between the in vitro and in silico results is explained by underrepresentation of tandem DNA repeats in genomic databases. The ROC analysis of individual genome fragments elucidated the nature of several DNA markers, which were earlier revealed by taxonomic printing, and showed that L1 and Alu repeats are nonrandomly distributed in various chromosomes. Another advantage is that the ROC procedure makes it possible to analyze the nonrandom character of a genomic distribution of short DNA sequences. The ROC analysis showed that a low poly(G) frequency is characteristic of the entire human genome, rather than of only coding sequences. The method was proposed for a more complex in silico analysis of the genome. For instance, it is possible to simulate DNA restriction together with blot hybridization and then to analyze the nature of markers revealed.     

In Silico Analysis of the Restriction Fragment Length Distribution in the Human Genome

The Restriction On Computer (ROC) program (freely available at http://www.mcb.harvard.edu/ gilbert/ROC) was developed and used to analyze the restriction fragment length distribution in the human genome. In contrast to other programs searching for restriction sites, ROC simultaneously analyzes several long nucleotide sequences, such as the entire genomes, and in essence simulates electrophoretic analysis of DNA restriction fragments. In addition, this program extracts and analyzes DNA repeats that account for peaks in the restriction fragment length distribution. The ROC analysis data are consistent with the experimental data obtained via in vitro restriction enzyme analysis (DNA taxonoprint). A difference between the in vitro and in silico results is explained by underrepresentation of tandem DNA repeats in genomic databases. The ROC analysis of individual genome fragments elucidated the nature of several DNA markers, which were earlier revealed by DNA taxonoprint, and showed that L1 and Alurepeats are nonrandomly distributed in various chromosomes. Another advantage is that the ROC procedure makes it possible to analyze the nonrandom character of a genomic distribution of short DNA sequences. The ROC analysis showed that a low poly(G) frequency is characteristic of the entire human genome, rather than of only coding sequences. The method was proposed for a more complex in silico analysis of the genome. For instance, it is possible to simulate DNA restriction together with blot hybridization and then to analyze the nature of markers revealed.     

Improvement of the contrast of electron microscope images by the optical shadow method

Amplitude contrast of images of weak phase and amplitude objects can be strengthened almost twice as compared to standard light-pole contrast by means of shadow method of image formation without special contrasting of the objects. In spite of the contour effects (electron-microscopic attenuation) appearing at image formation by asymmetrical shadow method, the quantitative interpretation of such image is quite possible. The symmetrical shadow image (image with cone illumination) is more complexly realized than the asymmetric one, but it has some additional advantages. Particularly efficient suppression of background noises is there possible. Several symmetrical shadow images can be synthetized by the differential method with or without colour coding into the final image with the signal/noise ratio increased by an order.     

Characterization of the cyclophilin gene family of Arabidopsis thaliana and phylogenetic analysis of known cyclophilin proteins

We have isolated four members of the Arabidopsis cyclophilin (CyP) gene family, designated ROC1 to ROC4 (rotamase CyP). Deduced peptides of ROC1, 2 and 3 are 75% to 91% identical to Brassica napus cytosolic CyP, contain no leader peptides and include a conserved seven amino-acid insertion relative to mammalian cytosolic CyPs. Two other Arabidopsis CyPs, ROC5 (43H1; ATCYP1) and ROC6 (ATCYP2), share these features. ROC1, ROC2, ROC3 and ROC5 are expressed in all tested organs of light-grown plants. ROC2 and ROC5 show elevated expression in flowers. Expression of ROC1, ROC2, and ROC3 decreases in darkness and these genes also exhibit small elevations in expression upon wounding. The five Arabidopsis genes encoding putative cytosolic CyPs (ROC1, 2, 3, 5 and 6) contain no introns. In contrast, ROC4, which encodes a chloroplast stromal CyP, is interrupted by six introns. ROC4 is not expressed in roots, and is strongly induced by light. Phylogenetic trees of all known CyPs and CyP-related proteins provide evidence of possible horizontal transfer of CyP genes between prokaryotes and eukaryotes and of a possible polyphyletic origin of these proteins within eukaryotes. These trees also show significant grouping of eukaryotic CyPs on the basis of subcellular localization and structure. Mitochondrial CyPs are closely related to cytosolic CyPs of the source organism, but endoplasmic reticulum CyPs form separate clades. Known plant CyPs fall into three clades, one including the majority of higher-plant cytosolic CyPs, one including only ROC2 and a related rice CyP, and one including only chloroplast CyPs.     

Smoothed empirical likelihood inference for ROC curve in the presence of missing biomarker values

This paper considers statistical inference for the receiver operating characteristic (ROC) curve in the presence of missing biomarker values by utilizing estimating equations (EEs) together with smoothed empirical likelihood (SEL). Three approaches are developed to estimate ROC curve and construct its SEL-based confidence intervals based on the kernel-assisted EE imputation, multiple imputation, and hybrid imputation combining the inverse probability weighted imputation and multiple imputation. Under some regularity conditions, we show asymptotic properties of the proposed maximum SEL estimators for ROC curve. Simulation studies are conducted to investigate the performance of the proposed SEL approaches. An example is illustrated by the proposed methodologies. Empirical results show that the hybrid imputation method behaves better than the kernel-assisted and multiple imputation methods, and the proposed three SEL methods outperform existing nonparametric method.     

Application of Dual Modality Contrast Agent Combined with Multi-Scale Representation in Ultrasound-Magnetic Resonance Imaging Registration Scheme

To achieve the image registration/fusion and perfect the quality of the integration, with dual modality contrast agent (DMCA), a novel multi-scale representation registration method between ultrasound imaging (US) and magnetic resonance imaging (MRI) is presented in the paper, and how DMCA influence on registration accuracy is chiefly discussed. Owing to US’s intense speckle noise, it is a tremendous challenge to register US with any other modality images. How to improve the algorithms for US processing has become the bottleneck, and in the short term it is difficult to have a breakthrough. In that case, DMCA is employed in both US and MRI to enhance the region of interest. Then, because multi-scale representation is a strategy that attempts to diminish or eliminate several possible local minima and lead to convex optimization problems to be solved quickly and more efficiently, a multi-scale representation Gaussian pyramid based affine registration (MRGP-AR) scheme is constructed to complete the US-MRI registration process. In view of the above-mentioned method, the comparison tests indicate that US-MRI registration/fusion may be a remarkable method for gaining high-quality registration image. The experiments also show that it is feasible that novel nano-materials combined with excellent algorithm are used to solve some hard tasks in medical image processing field.     

Evaluation of an automatic method for detection of defects in linear and curvilinear ultrasound transducers

PurposeThe high incidence of defective ultrasound transducers in clinical practice has been shown in several studies. Recently, a novel method using only stored images for automatic detection of defective transducers was presented. The method makes it possible to remotely monitor many transducers at the same time and send a notification when a defective transducer is found. The purpose of the present study was to evaluate the novel method and assess how well it performs when compared to an established method as reference.MethodsTo evaluate the novel method, in-air images were collected from 81 transducers in radiologic departments in nine hospitals. Two observers assessed the in-air images and marked the defects. Receiver operating characteristic (ROC)- and alternative free response receiver operating characteristic (AFROC)-curves and their figures of merit (FOM) were calculated for the novel method, using marked defects in the in-air images as reference truth.ResultsThe area under the ROC curve was 0.88 (SD 0.06), and the AFROC FOM was 0.71 (SE 0.07).ConclusionThe result shows that the novel method has a good agreement with the in-air method for detecting defects in ultrasound systems. This indicates that the novel method could be a complement to the normal quality control for early, and automatic detection of defects.     

Time-dependent ROC curves for censored survival data and a diagnostic marker

ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.     

Evaluating the effect of reduced entrance surface dose on neonatal chest imaging using subjective image quality evaluation

IntroductionAs hospitalized neonates are radiosensitive and often require numerous X-rays, institutions should investigate the optimal beam parameter combinations to deliver diagnostically acceptable quality images at the lowest possible entrance surface dose (ESD). Using a subjective approach, this study evaluated the effect of different beam parameter combinations on image quality.Methods and materialsFive rabbits simulated the neonatal chest. The ESD was reduced using a variation of voltage, tube current and filtration. Eight radiology registrars, blinded to the dose parameter information, ranked the digital X-ray images of three anatomical regions from best to worst using a variation of the multiple rank order method. T-tests compared the average values, obtained from the scores assigned by each observer to images acquired at different ESDs, for each region. The calculated intraclass correlation coefficient (ICC) assessed observer agreement.ResultsResults showed that a 64% dose reduction was achievable by altering the beam parameters. Large variation among the observers was confirmed by an ICC value <0.5. A 95% confidence interval could not conclude that different ESD values, resulting in a 50–77% dose reduction compared to current practice, would result in different overall observed image quality. This was noted for all three regions indicating that no preference existed towards an image acquired with a specific beam parameter combination.ConclusionsThe large variation in observers’ opinion of acceptable image quality emphasizes the importance of subjective image quality evaluation in the clinical environment. The rabbit phantom and multiple rank order method are considered appropriate for these evaluations.     

Structural similarity regularized and sparse coding based super-resolution for medical images

Recently the single image super-resolution reconstruction (SISR) via sparse coding has attracted increasing interests. Considering that there are obviously repetitive image structures in medical images, in this study we propose a regularized SISR method via sparse coding and structural similarity. The pixel based recovery is incorporated as a regularization term to exploit the non-local structural similarities of medical images, which is very helpful in further improving the quality of recovered medical images. An alternative variables optimization algorithm is proposed and some medical images including CT, MRI and ultrasound images are used to investigate the performance of our proposed method. The results show the superiority of our method to its counterparts.     

Application of novel lossless compression of medical images using prediction and contextual error modeling

Conduction of tele-3D-computer assisted operations as well as other telemedicine procedures often requires highest possible quality of transmitted medical images and video. Unfortunately, those data types are always associated with high telecommunication and storage costs that sometimes prevent more frequent usage of such procedures. We present a novel algorithm for lossless compression of medical images that is extremely helpful in reducing the telecommunication and storage costs. The algorithm models the image properties around the current, unknown pixel and adjusts itself to the local image region. The main contribution of this work is the enhancement of the well known approach of predictor blends through highly adaptive determination of blending context on a pixel-by-pixel basis using classification technique. We show that this approach is well suited for medical image data compression. Results obtained with the proposed compression method on medical images are very encouraging, beating several well known lossless compression methods. The predictor proposed can also be used in other image processing applications such as segmentation and extraction of image regions.     

Higuchi dimension of digital images

There exist several methods for calculating the fractal dimension of objects represented as 2D digital images. For example, Box counting, Minkowski dilation or Fourier analysis can be employed. However, there appear to be some limitations. It is not possible to calculate only the fractal dimension of an irregular region of interest in an image or to perform the calculations in a particular direction along a line on an arbitrary angle through the image. The calculations must be made for the whole image. In this paper, a new method to overcome these limitations is proposed. 2D images are appropriately prepared in order to apply 1D signal analyses, originally developed to investigate nonlinear time series. The Higuchi dimension of these 1D signals is calculated using Higuchi's algorithm, and it is shown that both regions of interests and directional dependencies can be evaluated independently of the whole picture. A thorough validation of the proposed technique and a comparison of the new method to the Fourier dimension, a common two dimensional method for digital images, are given. The main result is that Higuchi's algorithm allows a direction dependent as well as direction independent analysis. Actual values for the fractal dimensions are reliable and an effective treatment of regions of interests is possible. Moreover, the proposed method is not restricted to Higuchi's algorithm, as any 1D method of analysis, can be applied.     

Low-Order Non-Spatial Effects Dominate Second-Order Spatial Effects in the Texture Quantifier Analysis of 18F-FDG-PET Images

BackgroundThere is increasing interest in applying image texture quantifiers to assess the intra-tumor heterogeneity observed in FDG-PET images of various cancers. Use of these quantifiers as prognostic indicators of disease outcome and/or treatment response has yielded inconsistent results. We study the general applicability of some well-established texture quantifiers to the image data unique to FDG-PET.MethodsWe first created computer-simulated test images with statistical properties consistent with clinical image data for cancers of the uterine cervix. We specifically isolated second-order statistical effects from low-order effects and analyzed the resulting variation in common texture quantifiers in response to contrived image variations. We then analyzed the quantifiers computed for FIGOIIb cervical cancers via receiver operating characteristic (ROC) curves and via contingency table analysis of detrended quantifier values.ResultsWe found that image texture quantifiers depend strongly on low-effects such as tumor volume and SUV distribution. When low-order effects are controlled, the image texture quantifiers tested were not able to discern only the second-order effects. Furthermore, the results of clinical tumor heterogeneity studies might be tunable via choice of patient population analyzed.ConclusionSome image texture quantifiers are strongly affected by factors distinct from the second-order effects researchers ostensibly seek to assess via those quantifiers.     

Towards radiological diagnosis of abdominal adhesions based on motion signatures derived from sequences of cine-MRI images

This paper reports novel development and preliminary application of an image registration technique for diagnosis of abdominal adhesions imaged with cine-MRI (cMRI). Adhesions can severely compromise the movement and physiological function of the abdominal contents, and their presence is difficult to detect. The image registration approach presented here is designed to expose anomalies in movement of the abdominal organs, providing a movement signature that is indicative of underlying structural abnormalities. Validation of the technique was performed using structurally based in vitro and in silico models, supported with Receiver Operating Characteristic (ROC) methods. For the more challenging cases presented to the small cohort of 4 observers, the AUC (area under curve) improved from a mean value of 0.67 ± 0.02 (without image registration assistance) to a value of 0.87 ± 0.02 when image registration support was included. Also, in these cases, a reduction in time to diagnosis was observed, decreasing by between 20% and 50%. These results provided sufficient confidence to apply the image registration diagnostic protocol to sample magnetic resonance imaging data from healthy volunteers as well as a patient suffering from encapsulating peritoneal sclerosis (an extreme form of adhesions) where immobilization of the gut by cocooning of the small bowel is observed. The results as a whole support the hypothesis that movement analysis using image registration offers a possible method for detecting underlying structural anomalies and encourages further investigation.     

ROC curve estimation when covariates affect the verification process

A receiver operating characteristic (ROC) curve is commonly used to measure the accuracy of a medical test. It is a plot of the true positive fraction (sensitivity) against the false positive fraction (1-specificity) for increasingly stringent positivity criterion. Bias can occur in estimation of an ROC curve if only some of the tested patients are selected for disease verification and if analysis is restricted only to the verified cases. This bias is known as verification bias. In this paper, we address the problem of correcting for verification bias in estimation of an ROC curve when the verification process and efficacy of the diagnostic test depend on covariates. Our method applies the EM algorithm to ordinal regression models to derive ML estimates for ROC curves as a function of covariates, adjusted for covariates affecting the likelihood of being verified. Asymptotic variance estimates are obtained using the observed information matrix of the observed data. These estimates are derived under the missing-at-random assumption, which means that selection for disease verification depends only on the observed data, i.e., the test result and the observed covariates. We also address the issues of model selection and model checking. Finally, we illustrate the proposed method on data from a two-phase study of dementia disorders, where selection for verification depends on the screening test result and age.