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Biomedical subjects

R L Somorjai

Publications and source records attributed to R L Somorjai.

At least 19 recordsLinked to original sources
PubMed2003

Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions.

MOTIVATION: Two practical realities constrain the analysis of microarray data, mass spectra from proteomics, and biomedical infrared or magnetic resonance spectra. One is the 'curse of dimensionality': the number of features characterizing these data is in the thousands or tens of thousands. The other is the 'curse of dataset sparsity': the number of samples is limited. The consequences of these two curses are far-reaching when such data are used to classify the presence or absence of disease. RESULTS: Using very simple classifiers, we show for several publicly available microarray and proteomics datasets how these curses influence classification outcomes. In particular, even if the sample per feature ratio is increased to the recommended 5-10 by feature extraction/reduction methods, dataset sparsity can render any classification result statistically suspect. In addition, several 'optimal' feature sets are typically identifiable for sparse datasets, all producing perfect classification results, both for the training and independent validation sets. This non-uniqueness leads to interpretational difficulties and casts doubt on the biological relevance of any of these 'optimal' feature sets. We suggest an approach to assess the relative quality of apparently equally good classifiers.

Algorithms↗
PubMed2002

A novel, direct spatio-temporal approach for analyzing fMRI experiments.

We introduce a novel approach to couple temporal similarity with spatial neighborhood information. This is achieved by concatenating the K nearest, spatially contiguous neighbors of a pixel time-course (TC) of T time-instances. This produces a new TC of (K+1)T time instances. Depending on how "nearest" is defined, we have various options. Strictly spatial nearness means augmenting a given TC by its K nearest neighbors in some canonical spatial order. A more powerful and flexible option is to order the TCs to be concatenated according to their temporal similarity to the central voxel TC. For this study, we have chosen Pearson's cross-correlation coefficient as the measure of similarity. For more than a single neighbor, two concatenation options are possible. The direct ordering option requires that the TCs to be concatenated be spatially contiguous to the central pixel. The more flexible indirect option merely demands that one of a chain of temporally similar TCs be spatially connected to the central pixel. We also apply the temporal similarity criterion to the more conventional spatial (median) filtering, and show that it gives superior result to a strict spatial filtering. The method is tested and verified on a null fMRI dataset onto which we superposed two types of "activations" with known temporal behavior and spatial location. It is also applied to a real dataset containing visual activation. We also propose a strategy, based on the flexibility of the method, to determine a consensus, "core" set of activations.

Brain↗
PubMed2002

Exploring regions of interest with cluster analysis (EROICA) using a spectral peak statistic for selecting and testing the significance of fMRI activation time-series.

Much relevant information about activations and artifacts in a functional magnetic resonance imaging (fMRI) dataset can be obtained from an exploratory cluster analysis. In contrast to testing the significance of the measured experimental effect for a given model, unsupervised pattern recognition techniques, such as fuzzy clustering, often find unexpected behavior in addition to expected activations, allowing the exploitation of this element of surprise. The many artifact clusters often discovered might aid the experimenter in deciding whether the dataset is usable, whether some additional preprocessing step is required, or whether the one used has introduced spurious effects. However, clustering alone does not complete the analysis because the membership values that are generated are not indicative of the level of statistical significance with respect to the cluster activation patterns (centroids). This is of particular importance for fMRI datasets for which most time-series are "noise", with no activation patterns. We propose that an initial partition step should precede the clustering step. Only time-series that meet a certain statistical criterion (using a scaled version of Fisher's g-order statistic) are selected for clustering; this typically represents <5% of the whole brain region. The purpose of clustering is to generate a set of cluster centers that are the possible activation patterns; these are used in forming a linear model of all the time-series. The model parameter is tested for significance in both the time and frequency domains. We present a novel method of conducting these tests, which limits the number of false positives. We call the three-step process of initial partition, clustering and the two-domain significance test as exploring regions of interest with cluster analysis (EROICA).

Brain↗
PubMed2001

Magnetic resonance spectroscopy of the malignant prostate gland after radiotherapy: a histopathologic study of diagnostic validity.

PURPOSE: Accurate spatial representation of tumor clearance after conformal radiotherapy is an endpoint of clinical importance. Magnetic resonance spectroscopy (MRS) can diagnose malignancy in the untreated prostate gland through measurements of cellular metabolites. In this study we sought to describe spectral metabolic changes in prostatic tissue after radiotherapy and validate a multivariate analytic strategy (based on MRS) that could identify viable tumor. METHODS AND MATERIALS: Transrectal ultrasound-guided prostate biopsies from 35 patients were obtained 18-36 months after external beam radiotherapy. One hundred sixteen tissue specimens were subjected to 1H MRS, submitted to histopathology, and analyzed for correlation with a multivariate strategy specifically developed for biomedical spectra. RESULTS: The sensitivity and specificity of MRS in identifying a malignant biopsy were 88.9% and 92% respectively, with an overall classification accuracy of 91.4%. The diagnostic spectral regions identified by our algorithm included those due to choline, creatine, glutamine, and lipid. Citrate, an important discriminating resonance in the untreated prostate gland, was invisible in all spectra, regardless of histology. CONCLUSIONS: Although the spectral features of prostate tissue markedly change after radiotherapy, MRS combined with multivariate methods of analysis can accurately identify histologically malignant biopsies. MRS shows promise as a modality that could integrate three-dimensional measures of tumor response.

Aged↗
PubMed2001

Diagnosis and prognosis of breast cancer by magnetic resonance spectroscopy of fine-needle aspirates analysed using a statistical classification strategy.

BACKGROUND: The aim was to develop robust classifiers to analyse magnetic resonance spectroscopy (MRS) data of fine-needle aspirates taken from breast tumours. The resulting data could provide computerized, classification-based diagnosis and prognostic indicators. METHODS: Fine-needle aspirate biopsies obtained at the time of surgery for both benign and malignant breast diseases were analysed by one-dimensional proton MRS at 8.5 Tesla. Diagnostic correlation was performed between the spectra and standard pathology reports, including the presence of vascular invasion by the primary cancer and involvement of the excised axillary lymph nodes. RESULTS: Malignant tissue was distinguished from benign lesions with an overall accuracy of 93 per cent. From the same spectra, lymph node involvement was predicted with an overall accuracy of 95 per cent, and tumour vascular invasion with an overall accuracy of 94 per cent. CONCLUSION: The pathology, nodal involvement and tumour vascular invasion were predicted by computerized statistical classification of the proton MRS spectrum from a fine-needle aspirate biopsy taken from the primary breast lesion.

Adult↗
PubMed2001

The use of 1H magnetic resonance spectroscopy in inflammatory bowel diseases: distinguishing ulcerative colitis from Crohn's disease.

OBJECTIVES: The distinction between the two major forms of inflammatory bowel diseases (IBD), i.e., ulcerative colitis (UC) and Crohn's disease is sometimes difficult and may lead to a diagnosis of indeterminate colitis. We have used 1H magnetic resonance spectroscopy (MRS) combined with multivariate methods of spectral data analysis to differentiate UC from Crohn's disease and to evaluate normal-appearing mucosa in IBD. METHODS: Colon mucosal biopsies (45 UC and 31 Crohn's disease) were submitted to 1H MRS, and multivariate analysis was applied to distinguish the two diseases. A second study was performed to test endoscopically and histologically normal biopsies from IBD patients. A classifier was developed by training on 101 spectra (76 inflamed IBD tissues and 25 normal control tissues). The spectra of 38 biopsies obtained from endoscopically and histologically normal areas of the colons of patients with IBD were put into the validation test set. RESULTS: The classification accuracy between UC and Crohn's disease was 98.6%, with only one case of Crohn's disease and no cases of UC misclassified. The diagnostic spectral regions identified by our algorithm included those for taurine, lysine, and lipid. In the second study, the classification accuracy between normal controls and IBD was 97.9%. Only 47.4% of the endoscopically and histologically normal IBD tissue spectra were classified as true normals; 34.2% showed "abnormal" magnetic resonance spectral profiles, and the remaining 18.4% could not be classified unambiguously. CONCLUSIONS: There is a strong potential for MRS to be used in the accurate diagnosis of indeterminate colitis; it may also be sensitive in detecting preclinical inflammatory changes in the colon.

Adult↗
PubMed2000

A wavelet-based method for improving signal-to-noise ratio and contrast in MR images.

Magnetic resonance (MR) images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distribution only looks Gaussian at high SNR. Some recent work by Nowak employs a wavelet-based method for de-noising the square magnitude images, and explicitly takes into account the Rician nature of the noise distribution. In this article, we apply a wavelet de-noising algorithm directly to the complex image obtained as the Fourier transform of the raw k-space two-channel (real and imaginary) data. By retaining the complex image, we are able to de-noise not only magnitude images but also phase images. A multiscale (complex) wavelet-domain Wiener-type filter is derived. The algorithm preserves edges better when the Haar wavelet rather than smoother wavelets, such as those of Daubechies, are used. The algorithm was tested on a simulated image to which various levels of noise were added, on several EPI image sequences, each of different SNR, and on a pair of low SNR MR micro-images acquired using gradient echo and spin echo sequences. For the simulated data, the original image could be well recovered even for high values of noise (SNR approximately 0 dB), suggesting that the present algorithm may provide better recovery of the contrast than Nowak's method. The mean-square error, bias, and variance are computed for the simulated images. Over a range of amounts of added noise, the present method is shown to give smaller bias than when using a soft threshold, and smaller variance than a hard threshold; in general, it provides a better bias-variance balance than either hard or soft threshold methods. For the EPI (MR) images, contrast improvements of up to 8% (for SNR = 33 dB) were found. In general, the improvement in contrast was greater the lower the original SNR, for example, up to 50% contrast improvement for SNR of about 20 dB in micro-imaging. Applications of the algorithm to the segmentation of medical images, to micro-imaging and angiography (where the correct preservation of phase is important for flow encoding to be possible), as well as to de-noising time series of functional MR images, are discussed.

Algorithms↗
PubMed1999

Unilateral antegrade cerebral perfusion through the right axillary artery provides uniform flow distribution to both hemispheres of the brain: A magnetic resonance and histopathological study in pigs.

BACKGROUND: Bilateral antegrade cerebral perfusion (ACP) has decreased in popularity over the past decade because of its complexity and the risk of cerebral embolism. We used magnetic resonance (MR) perfusion imaging to assess flow distribution in both hemispheres of the brain during unilateral ACP through the right carotid artery via a cannula placed in the right axillary artery in conjunction with hypothermic circulatory arrest. METHODS AND RESULTS: Twelve pigs were randomly exposed to 120 minutes of either bilateral ACP through both carotid arteries (n=6) or unilateral ACP through the right axillary artery (n=6) at pressures of 60 to 65 mm Hg at 15 degrees C, followed by 60 minutes of cardiopulmonary bypass at 37 degrees C. MR perfusion images were acquired every 30 minutes before, during, and after ACP. The brain was perfusion fixed for histopathology. During initial normothermic cardiopulmonary bypass, MR perfusion imaging showed a uniform distribution of flow in the brain. In both the bilateral and unilateral ACP groups, the same pattern was maintained, with an increase in regional cerebral blood volume during ACP and reperfusion. The changes in regional cerebral blood volume and mean transit time were similar in both hemispheres during and after unilateral ACP. No difference was observed between the 2 groups. Histopathology showed normal morphology in all regions of the brain in both groups. CONCLUSIONS: Both bilateral ACP and unilateral ACP provide uniform blood distribution to both hemispheres of the brain and preserve normal morphology of the neurons after prolonged hypothermic circulatory arrest.

Animals↗
PubMed1999

Classification of breast tumors by grade and steroid receptor status using pattern recognition analysis of infrared spectra.

Infrared (IR) spectroscopy applied to tissue sections yields complex spectra that provide a molecular fingerprint of the tissue. We have studied a cohort of 77 breast tumors by IR spectroscopy to develop an objective method for the assignment of grade of breast tumors. Although the major variations between spectra from different tumors were in absorptions arising from triglycerides (adipose tissue) and collagen, subtle changes in spectra could be detected that were independent of cellularity and tissue composition. Using a specific multivariate pattern recognition strategy to associate these changes in spectra with different tumor grades, we then were able to accurately reclassify tumors by grade (87% accuracy; kappa = 0.835). A similar approach allowed classification of steroid receptor status (93% accuracy; kappa = 0.852). We conclude that IR spectroscopy may have clinical utility in the objective assignment of breast tumor grade.

Breast Neoplasms↗
PubMed1998

Retrograde cerebral perfusion results in flow distribution abnormalities and neuronal damage. A magnetic resonance imaging and histopathological study in pigs.

BACKGROUND: In the past few years, although significant efforts have been made to assess flow distribution during retrograde cerebral perfusion with microspheres, dye, or hydrogen clearance, flow distribution in real time is still undefined. We used MR perfusion imaging to monitor flow distribution in the brain during and after deep hypothermic circulatory arrest (DHCA) with antegrade or retrograde cerebral perfusion (ACP or RCP). METHODS AND RESULTS: Thirteen pigs were divided into 2 groups and exposed to 120 minutes of either RCP (n = 7) or ACP (n = 6) at 15 degrees C, followed by 60 minutes of cardiopulmonary bypass (CPB) at 37 degrees C. During DHCA, the brain was perfused antegradely through the common carotid artery or retrogradely through the superior vena cava at pressures of 60 to 70 mm Hg and 20 to 25 mm Hg in the ACP and RCP groups, respectively. Esophageal temperature was monitored continuously. MR perfusion images were acquired every 30 minutes before, during, and after DHCA. The brain was perfusion-fixed with formaldehyde solution for histopathology at the completion of each experiment. During initial normothermic CPB, MR perfusion imaging showed a nearly uniform distribution of flow in the brain. The same pattern was maintained with a significant increase in regional cerebral blood volume during ACP and reperfusion in the ACP group. RCP provided little or no detectable blood distribution to the brain, resulting in poor reperfusion of many areas of the brain on reflow with CPB at 37 degrees C. The total area suffering poor reperfusion was significantly higher in the RCP group than the ACP group. Histopathology showed no morphological changes in any area of the brain in the ACP group, whereas varying severity of neuronal damage was observed in different regions of the brain in the RCP group. CONCLUSIONS: ACP preserves uniform blood distribution and normal morphology of brain tissue after prolonged DHCA. RCP provides very little blood to the tissue of the brain. A 120-minute period of RCP results in abnormal flow distribution and neuronal damage during reperfusion. The damage resulting from shorter periods of RCP remains to be assessed.

Animals↗
PubMed1997

The classification of benign and malignant human prostate tissue by multivariate analysis of 1H magnetic resonance spectra.

1H magnetic resonance spectroscopy studies (360 MHz) were performed on specimens of benign (n = 66) and malignant (n = 21) human prostate tissue from 50 patients, and the spectral data were subjected to multivariate analysis, specifically linear-discriminant analysis. On the basis of histopathological assessments, an overall classification accuracy of 96.6% was achieved, with a sensitivity of 100% and a specificity of 95.5% in classifying benign prostatic hyperplasia from prostatic cancer. Resonances due to citrate, glutamate, and taurine were among the six spectral subregions identified by our algorithm as having diagnostic potential. Significantly higher levels of citrate were observed in glandular than in stromal benign prostatic hyperplasia (P < 0.05). This method shows excellent promise for the possibility of in vivo assessment of prostate tissue by magnetic resonance.

Discriminant Analysis↗
PubMed1997

Classification of 1H MR spectra of biopsies from untreated and recurrent ovarian cancer using linear discriminant analysis.

Proton (1H) magnetic resonance (MR) spectra of ex vivo biopsy samples of ovarian cancers provided biochemical information that was used to discriminate cancer from normal ovarian tissue. Possible differences present in intrinsically resistant tumors or changes in biochemistry after the induction of resistance were identified. Using multivariate techniques, in particular linear discriminant analysis (LDA), ovarian cancer was distinguished from normal ovarian tissue with a sensitivity of 100%, a specificity of 95% and an accuracy of 98%. Moreover, LDA was able to distinguish untreated ovarian cancer from recurrent ovarian cancer with a sensitivity of 92%, a specificity of 100%, and an accuracy of 97%; removal of the single "fuzzy" specimen increased the accuracy to 100%. Applications of this knowledge to in vivo measurements could lead to noninvasive diagnosis of ovarian cancer.

Biopsy↗
PubMed1997

Retrograde cerebral perfusion provides limited distribution of blood to the brain: a study in pigs.

OBJECTIVE: The objective of this study was to investigate flow distribution during retrograde and antegrade cerebral perfusion with India ink as a marker. METHODS: Ten pigs received cerebral perfusion with a solution containing 50% filtered India ink for 5 minutes either antegradely through both internal carotid arteries at a flow of 180 to 200 ml/min (n = 5) or retrogradely via the superior vena cava at a flow of 300 to 500 ml/min (n = 5). The brains were then fixed for quantitative measurement of the density of ink-filled capillaries (reported as a percentage of the total selected area). The assessment was done with the use of an in-house software program. RESULTS: In the antegrade cerebral perfusion group, the intracranial arterial and venous systems were completely filled with ink. The gray matter was colored uniformly black, and light coloring was observed in the white matter. During retrograde cerebral perfusion, the majority of ink was returned to the inferior vena cava, and only a small amount of ink was found in the innominate artery draining from the brain. Massive ink filling was observed in the sagittal sinus and other venous sinuses in all the pigs. Vessels on the surface of the brain and large vessels in the brain were also well filled with ink. However, only 10% of capillaries were filled with ink during retrograde cerebral perfusion relative to the number observed with antegrade cerebral perfusion. CONCLUSIONS: Retrograde cerebral perfusion supplies a limited amount of blood to brain tissue, which flows mainly through superficial and large deep cerebral vessels.

Animals↗
PubMed1997

An improved robust hierarchical registration algorithm.

This note describes an improvement to an accurate, robust, and fast registration algorithm (Alexander, M.E. and Somorjai, R.L., Mag. Reson. Imaging, 14:453-468, 1996). A computationally inexpensive preregistration method is proposed, consisting of simply aligning the image centroids, from which estimates of the translation shifts are derived. The method has low sensitivity to noise, and provides starting values of sufficient accuracy for the iterative registration algorithm to allow accurate registration of images that have significant levels of noise and/or large misalignments. Also, it requires a smaller computational effort than the Fourier Phase Matching (FPM) preregistration method used previously. The FPM method provides accurate preregistration for low-noise images, but fails when significant noise is present. For testing the various methods, a 256 x 256 pixel T2*-weighted image was translated, rotated, and scaled to produce large misalignments and occlusion at the image boundaries. The two situations of no noise being present in the images and in which Gaussian noise is added, were tested. After preregistration, the images were registered by applying one or several passes of the iterative algorithm at different levels of preblurring of the input images. Results of using the old and new preregistration methods, as well as no preregistration, are compared for the final accuracy of recovery of registration parameters. In addition, the performances of three robust estimators: Least Median of Squares, Least Trimmed Squares, and Least Winsorized Mean, are compared with those of the nonrobust Least Squares and Woods' methods, and found to converge to correct solutions in cases where the nonrobust methods do not.

Algorithms↗
PubMed1996

The registration of MR images using multiscale robust methods.

Acquisition of MR images involves their registration against some prechosen reference image. Motion artifacts and misregistration can seriously flaw their interpretation and analysis. This article provides a global registration method that is robust in the presence of noise and local distortions between pairs of images. It uses a two-stage approach, comprising an optional Fourier phase-matching method to carry out preregistration, followed by an iterative procedure. The iterative stage uses a prescribed set of registration points, defined on the reference image, at which a robust nonlinear regression is computed from the squared residuals at these points. The method can readily accommodate general linear or even nonlinear, registration transformations on the images. The algorithm was tested by recovering the registration transformation parameters when a 256 x 256 pixel T2*-weighted human brain image was scaled, rotated, and translated by prescribed amounts, and to which different amounts of Gaussian noise had been added. The results show subpixel accuracy of recovery when no noise is present, and graceful degradation of accuracy as noise is added. When 40% noise is added to images undergoing small shifts, the recovery errors are less than 3 pixels. The same tests applied to the Woods algorithm gave slightly inferior accuracy for these images, but failed to converge to the correct parameters in some cases of large-scale-shifted images with 10% added noise.

Algorithms↗
PubMed1995

Computerized consensus diagnosis: a classification strategy for the robust analysis of MR spectra. I. Application to 1H spectra of thyroid neoplasms.

We introduce and apply a new classification strategy we call computerized consensus diagnosis (CCD). Its purpose is to provide robust, reliable classification of biomedical data. The strategy involves the cross-validated training of several classifiers of diverse conceptual and methodological origin on the same data, and appropriately combining their outcomes. The strategy is tested on proton magnetic resonance spectra of human thyroid biopsies, which are successfully allocated to normal or carcinoma classes. We used Linear Discriminant Analysis, a Neural Net-based method, and Genetic Programming as independent classifiers on two spectral regions, and chose the median of the six classification outcomes as the consensus. This procedure yielded 100% specificity and 100% sensitivity on the training sets, and 100% specificity and 98% sensitivity on samples of known malignancy in the test sets. We discuss the necessary steps any classification approach must take to guarantee reliability, and stress the importance of fuzziness and undecidability in robust classification.

Adenocarcinoma, Follicular↗
PubMed1995

Neural network classification of infrared spectra of control and Alzheimer's diseased tissue.

Artificial neural network classification methods were applied to infrared spectra of histopathologically confirmed Alzheimer's diseased and control brain tissue. Principal component analysis was used as a preprocessing technique for some of these artificial neural networks while others were trained using the original spectra. The leave-one-out method was used for cross-validation and linear discriminant analysis was used as a performance benchmark. In the cases where principal components were used, the artificial neural networks consistently outperformed their linear discriminant counterparts; 100% versus 98% correct classifications, respectively, for the two class problem, and 90% versus 81% for a more complex five class problem. Using the original spectra, only one of the three selected artificial neural network architectures (a variation of the back-propagation algorithm using fuzzy encoding) produced results comparable to the best corresponding principal component cases: 98% and 85% correct classifications for the two and five class problems, respectively.

Alzheimer Disease↗