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PubMed2004

Surfaced-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) differentiation of serum protein profiles of BRCA-1 and sporadic breast cancer.

BACKGROUND: BRCA-1 mutations predispose women to early onset breast cancer, but approximately 20% never develop cancer. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) profiling can differentiate protein signatures of cancer and normal subjects. Our objective was to distinguish women with BRCA-1 mutations who developed breast cancer (BRCA-1 Ca) from those who did not (Carrier), normal volunteers (NL), and women with sporadic breast cancer (SBC), using SELDI-TOF. METHODS: Baseline serum specimens were obtained from women with BRCA-1 mutations without cancer, SBC, and NL. BRCA-1 women were later divided into two cohorts, pending cancer development. The sera were spotted onto protein chips for SELDI-TOF analysis and analyzed with classification algorithm software. RESULTS: BRCA-1 Ca patients (n = 15) developed cancer within 3 years of baseline, while BRCA-1 carriers (n = 15) were cancer-free in 7 years of follow-up. SELDI-TOF analysis revealed differentially expressed proteins (P < .05) between BRCA-1 Ca, Carrier, and SBC patients (n = 16), such that 13/15 BRCA-1 Ca vs. Carrier women were correctly identified (sensitivity/specificity of 87%/87%) and 14/15 BRCA-1 Ca vs. SBC patients were correctly identified (sensitivity/specificity 94%/100%). Profiles of Carriers resembled NL profiles (n = 16). CONCLUSIONS: SELDI-TOF protein profiles from this small pilot study distinguished between women with BRCA-1 Ca, Carriers, and women with SBC. Whether BRCA-1 Ca represents earlier detection of occult cancer or other risk factors is unknown. Follow-up studies with larger numbers and longer follow-up are required to validate these findings but may allow more timely prophylactic or therapeutic strategies.

Adult↗

PubMed2006

Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis.

The Monte Carlo-based inverse model of diffuse reflectance described in part I of this pair of companion papers was applied to the diffuse reflectance spectra of a set of 17 malignant and 24 normal-benign ex vivo human breast tissue samples. This model allows extraction of physically meaningful tissue parameters, which include the concentration of absorbers and the size and density of scatterers present in tissue. It was assumed that intrinsic absorption could be attributed to oxygenated and deoxygenated hemoglobin and beta-carotene, that scattering could be modeled by spheres of a uniform size distribution, and that the refractive indices of the spheres and the surrounding medium are known. The tissue diffuse reflectance spectra were evaluated over a wavelength range of 400-600 nm. The extracted parameters that showed the statistically most significant differences between malignant and nonmalignant breast tissues were hemoglobin saturation and the mean reduced scattering coefficient. Malignant tissues showed decreased hemoglobin saturation and an increased mean reduced scattering coefficient compared with nonmalignant tissues. A support vector machine classification algorithm was then used to classify a sample as malignant or nonmalignant based on these two extracted parameters and produced a cross-validated sensitivity and specificity of 82% and 92%, respectively.

Algorithms↗

PubMed2025

A paradoxical population structure of var DBLα types in Africa.

The var multigene family encodes Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), central to host-parasite interactions. Genome structure studies have identified three major groups of var genes by specific upstream sequences (upsA, B, or C). Var with these ups groups have different chromosomal locations, transcriptional directions, and associations with disease severity. Here we explore temporal and spatial diversity of a region of var genes encoding the DBLα domain of PfEMP1 in Africa. By applying a novel ups classification algorithm (cUps) to publicly-available DBLα sequence datasets, we categorised DBLα according to association with the three ups groups, thereby avoiding the need to sequence complete genes. Data from deep sequencing of DBLα types in a local population in northern Ghana surveyed seven times from 2012 to 2017 found variants with rare-to-moderate-to-extreme frequencies, and the common variants were temporally stable in this local endemic area. Furthermore, we observed that every isolate repertoire, whether mono- or multiclonal, comprised DBLα types occurring with these frequency ranges implying a common genome structure. When comparing African countries of Ghana, Gabon, Malawi, and Uganda, we report that some DBLα types were consistently found at high frequencies in multiple African countries while others were common only at the country level. The implication of these local and pan-Africa population patterns is discussed in terms of advantage to the parasite with regards to within-host adaptation and resilience to malaria control.

Plasmodium falciparum↗

PubMed2004

Proteomic fingerprints for potential application to early diagnosis of severe acute respiratory syndrome.

BACKGROUND: Definitive early-stage diagnosis of severe acute respiratory syndrome (SARS) is important despite the number of laboratory tests that have been developed to complement clinical features and epidemiologic data in case definition. Pathologic changes in response to viral infection might be reflected in proteomic patterns in sera of SARS patients. METHODS: We developed a mass spectrometric decision tree classification algorithm using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Serum samples were grouped into acute SARS (n = 74; <7 days after onset of fever) and non-SARS [n = 1067; fever and influenza A (n = 203), pneumonia (n = 176); lung cancer (n = 29); and healthy controls (n = 659)] cohorts. Diluted samples were applied to WCX-2 ProteinChip arrays (Ciphergen), and the bound proteins were assessed on a ProteinChip Reader (Model PBS II). Bioinformatic calculations were performed with Biomarker Wizard software 3.1.1 (Ciphergen). RESULTS: The discriminatory classifier with a panel of four biomarkers determined in the training set could precisely detect 36 of 37 (sensitivity, 97.3%) acute SARS and 987 of 993 (specificity, 99.4%) non-SARS samples. More importantly, this classifier accurately distinguished acute SARS from fever and influenza with 100% specificity (187 of 187). CONCLUSIONS: This method is suitable for preliminary assessment of SARS and could potentially serve as a useful tool for early diagnosis.

Adolescent↗

PubMed2002

Simulation methods for the online extraction of ECG parameters under Matlab/Simulink.

The classification of cardiac pathologies in the human ECG greatly depends on the reliable extraction of characteristic features. This work presents a complete simulation environment for testing ECG classification algorithms under Matlab/Simulink. Evaluation of algorithm performance is undertaken in full compliance with the ANSI/AAMI standards EC38 and EC57, and ranges from beat-to-beat analysis to the comparison of episode markers (e.g., for VT/VF detection algorithms). For testing the quality of waveform boundary detection, our own testing methods have been implemented in compliance with existing literature.

Algorithms↗

PubMed2003

Native fluorescence spectroscopy of blood plasma in the characterization of oral malignancy.

Native fluorescence characteristics of blood plasma were studied in the visible spectral region, at two different excitation wavelengths, 405 and 420 nm, to discriminate patients with different stages of oral malignancy from healthy subjects. The fluorescence spectra of blood plasma of oral malignant subjects exhibit characteristic spectral differences with respect to normal subjects. Different ratios were calculated using the fluorescence intensity values at those emission wavelengths that give characteristic spectral features of each group of experimental subjects studied. These fluorescence intensity ratios were used as input variables for a multiple linear discriminant analysis across different groups. Leave-one out cross-validation was used to check the reliability of each discriminant analysis performed. The discriminant analysis performed across normal and oral cancerous subjects classified 94.7% of the original grouped cases and 93.7% of the cross-validated grouped cases. A classification algorithm was developed on the basis of the score of the discriminant functions (discriminant score) resulted in the analyses. The diagnostic potentiality of the present technique was also estimated in the discrimination of malignant subjects from normal and nonmalignant diseased subjects such as liver diseases. In the discriminant analysis performed across the three groups, normal, oral malignancy (including early and advanced stages) and liver diseases, 99% of the original grouped cases and 95.9% of the cross-validated grouped cases were correctly classified. Similar analysis performed across normal, early stage of oral malignancy, advanced oral malignancy and liver diseases correctly classified 94.9% of the original grouped cases and 91.8% of the cross-validated grouped cases.

Blood Chemical Analysis↗

PubMed2003

Predictors of substance abuse treatment outcomes in Tennessee.

In planning and implementing programs to treat substance abuse, it is important to understand which factors influence post-treatment abstinence. This article identifies and analyzes several variables important in predicting the likelihood of abstinence among substance abuse clients. The data used in this study was collected from 1,350 clients treated for alcohol or drug abuse in residential, halfway house, or outpatient facilities in Tennessee. We analyzed 22 variables as possible treatment outcome predictors by using two statistical procedures: stepwise logistic regression analysis and Quick, Unbiased, Efficient, Statistical Tree (QUEST) analysis, a tree-structured classification algorithm analysis. We found one pretreatment, five in-treatment, and three post-treatment variables to be significant predictors of treatment outcome: previous treatment history, perceived helpfulness of the treatment, simultaneous treatment for mental health, number of days in treatment,completion of treatment, special skills training during treatment, obtaining healthcare services for major physical health problem after treatment, living with someone using alcohol or drugs post treatment, and arrest record since treatment.

Adolescent↗

PubMed2006

Use of infrared spectroscopy for diagnosis of traumatic arthritis in horses.

OBJECTIVE: To evaluate use of infrared spectroscopy for diagnosis of traumatic arthritis in horses. ANIMALS: 48 horses with traumatic arthritis and 5 clinically and radiographically normal horses. PROCEDURES: Synovial fluid samples were collected from 77 joints in 48 horses with traumatic arthritis. Paired samples (affected and control joints) from 29 horses and independent samples from an affected (n = 12) or control (7) joint from 19 horses were collected for model calibration. A second set of 20 normal validation samples was collected from 5 clinically and radiographically normal horses. Fourier transform infrared spectra of synovial fluids were acquired and manipulated, and data from affected joints were compared with controls to identify spectroscopic features that differed significantly between groups. A classification model that used linear discriminant analysis was developed. Performance of the model was determined by use of the 2 validation datasets. RESULTS: A classification model based on 3 infrared regions classified spectra from the calibration dataset with overall accuracy of 97% (sensitivity, 93%; specificity, 100%). The model, with cost-adjusted prior probabilities of 0.60:0.40, yielded overall accuracy of 89% (sensitivity, 83%; specificity, 100%) for the first validation sample dataset and 100% correct classification of the second set of independent normal control joints. CONCLUSIONS AND CLINICAL RELEVANCE: The infrared spectroscopic patterns of fluid from joints with traumatic arthritis differed significantly from the corresponding patterns for controls. These alterations in absorption patterns may be used via an appropriate classification algorithm to differentiate the spectra of affected joints from those of controls.

Animals↗

PubMedDate not supplied

Identification of hearing loss using TEOAE descriptors: theoretical foundations and preliminary results.

It was hypothesized that the relationship between transiently evoked otoacoustic emission (TEOAE) signals and the functional status of the outer hair cells provides an opportunity to design a clinical procedure that can evaluate the normality of cochlear function. To discriminate normal subjects from subjects suffering from mild to moderate hearing loss (HL), it was assumed that every subject population has unique and discrete TEOAE signal descriptors. The main classification algorithm was based on a discriminant analysis of raw fast Fourier transform data. When it was applied to a sample set of TEOAE recordings (from 56 normal and 68 HL subjects) elicited from 68-dB SPL click stimuli, it correctly identified 90.2% of the normal and 87.5% of the HL subjects. The same algorithm yielded an 85.5% discrimination between TEOAE recordings from conductive and cochlear HL cases.

Acoustic Stimulation↗

PubMed1999

TEOAE-based estimation of the auditory threshold in the mid-octave frequencies.

The purpose of this study was to estimate the hearing levels, at the mid-frequencies, of 233 ears with sensorineural hearing loss by classifying the corresponding transiently evoked otoacoustic emissions (TEOAE) recordings into three threshold groups. A classification algorithm was based upon a discriminant analysis of fast Fourier transform (FFT) data, evoked by non-linear click stimuli of 80 (+/-2) dB SPL per ear. To validate the efficiency of the proposed methodology TEOAE recordings were initially grouped by mean hearing level values of the 1 kHz and 2 kHz octaves into three threshold ranges according to two strategy schemes: in the first, TEOAE data were assigned into 10-19 dB HL, 20-39 dB HL or > or = 40 dB HL groups. In the second, TEOAE data were assigned into 10-29 > dB H >, 30-39 dB HL or > or = 40 dB HL groups. The most accurate prediction estimates were obtained from the second strategy scheme with a 90.9% accuracy in the 10-29 dB HL group, 82% in the 30-39 dB HL group and 71.4% in the > or = 40 dB HL group.

Adult↗

PubMed1997

A new automated method for three-dimensional registration of MR images of the head.

A new technique for three-dimensional image registration was developed and tested using T1 and T2 weighted Magnetic Resonance image studies of the head. The method uses the fuzzy c-means classification algorithm for outlining the surface contours and then minimizes iteratively the mean squared value of the voxel per voxel weighted ratio of the two trilinearly interpolated cubic voxel volumes. A total of 200 two-dimensional and 240 three-dimensional registration experiments were performed and showed that the method is signal intensity independent, it has registration accuracy better than 1 degree for rotations and 1 voxel for translations and it is not affected by the deterioration in the imaging resolution for voxel sizes up to 1.8 mms.

Brain Diseases↗

PubMed2001

Kaiser Permanente's "metadata-driven" national clinical intranet.

This paper describes the approach taken to build Kaiser Permanente's national clinical intranet. A primary objective for the site is to facilitate resource discovery, which is enabled by the use of "metadata", or data (fields and field values) that describe the various resources available. Users can perform full text queries and/or fielded searching against the metadata. Metadata serves as the organizing principle of the site--it is used to index documents, sort search results, and structure the site's table of contents. The site's use of metadata--what it is, how it is created, how it is applied to documents, how it is indexed, how it is presented to the user in the search and the search results interface, and how it is used to construct the table of contents for the web site--will be discussed in detail. The result is that KP's national clinical intranet has coupled the power of Internet-like full text search engines with the power of MedLine-like fielded searching in order to maximize search precision and recall. Organizing content on the site in accordance with the metadata promotes overall consistency. Issues currently under investigation include how to better exploit the power of the controlled terminology within the metadata; whether the value gained is worth the cost of collecting metadata; and how automatic classification algorithms might obviate the need for manual document indexing.

Computer Communication Networks↗

PubMed2002

Using Bayesian tissue classification to improve the accuracy of vestibular schwannoma volume and growth measurement.

BACKGROUND AND PURPOSE: True 3D measurements of tumor volume are time-consuming and subject to errors that are particularly pronounced in cases of small tumors. These problems complicate the routine clinical assessment of tumor growth rates. We examined the accuracy of currently available methods of size and growth measurement of vestibular schwannomas compared with that of a novel fast partial volume tissue classification algorithm. METHODS: Sixty-three patients with unilateral sporadic vestibular schwannomas underwent imaging. Thirty-eight of these patients underwent imaging two or more times at approximately 12-month intervals. Contrast-enhanced 3D T1-weighted images were used for all measurements. An experienced radiologist performed standard size estimations, including maximal diameter, elliptical area, perimeter, manually segmented area, intensity thresholded seeding volume, and manually segmented volume. A method for calculating volume was also used, incorporating Bayesian probability statistics to estimate partial volume effects. Manually segmented volume was obtained as a baseline standard measure. A computer-generated phantom exhibiting the intensity and partial volume characteristics of brain tissue, CSF, and intracanalicular vestibular schwannoma tissue was used to measure absolute accuracy of the standard technique and Bayesian partial volume segmentation. RESULTS: The Bayesian partial volume segmentation method showed the highest correlation (R(2) = 0.994) with the standard method, whereas the commonly used method of maximal diameter measurement showed poor correlation (R(2) = 0.732). Accuracy of Bayesian segmentation was shown to be more than twice that of manual segmentation, with an absolute accuracy of 5% (cf, 13%) and a remeasurement accuracy of 70 mm(3) (cf, 150 mm(3)). For the 38 patients who underwent imaging twice, definite tumor growth was shown for 12, potential growth for seven, no growth for 17, and definite shrinkage for two. CONCLUSION: Commonly used methods such as maximal diameter measurements do not provide adequate statistical accuracy with which to monitor tumor growth in patients with small vestibular schwannomas. Bayesian partial volume segmentation provides a more accurate and rapid method of volume and growth estimation. These differences in measurement accuracy translated into a significant improvement in clinical assessment, allowing identification of tumor growth in 10 of 12 cases that appeared to be static in size when manual segmentation techniques are used. The technique is quick to perform and suitable for use in routine clinical practice.

Adult↗

PubMedDate not supplied

Multivariate vestibular testing: laterality of unilateral Ménière's disease.

This paper discusses the use of vestibular testing to discriminate between right (n = 29) and left (n = 27) Ménière's disease. We examined reduced vestibular response (RVR), directional preponderance, and spontaneous and positional nystagmus measurements from electronystagmography, as well as the asymmetry measurements from the sinusoidal harmonic acceleration test, to determine whether multivariate logistic regression could improve upon the discrimination performance of RVR alone. We found that patients with a spontaneous or positional nystagmus often had a "recovery nystagmus", beating in the opposite direction of that predicted for an acute lesion. When present, the direction and magnitude of such nystagmus could be used in the classification algorithm to increase the discriminatory power over RVR alone, but in these patients the rotation test asymmetry measurements were rendered useless. In the absence of spontaneous or positional nystagmus, asymmetry measurements significantly enhanced right/left discrimination. Directional preponderance was insignificant in determining the side of lesion.

Electronystagmography↗

PubMed2002

Neural network-based segmentation and classification system for automated grading of histologic sections of bladder carcinoma.

OBJECTIVE: To develop an image analysis system for automated nuclear segmentation and classification of histologic bladder sections employing quantitative nuclear features. STUDY DESIGN: Ninety-two cases were classified into three classes by experienced pathologists according to the WHO grading system: 18 cases as grade 1, 45 as grade 2, and 29 as grade 3. Nuclear segmentation was performed by means of an artificial neural network (ANN)-based pixel classification algorithm, and each case was represented by 36 nuclei features. Automated grading of bladder tumor histologic sections was performed by an ANN classifier implemented in a two-stage hierarchic tree. RESULTS: On average, 95% of the nuclei were correctly detected. At the first stage of the hierarchic tree, classifier performance in discriminating between cases of grade 1 and 2 and cases of grade 3 was 89%. At the second stage, 79% of grade 1 cases were correctly distinguished from grade 2 cases. CONCLUSION: The proposed image analysis system provides the means to reduce subjectivity in grading bladder tumors and may contribute to more accurate diagnosis and prognosis since it relies on nuclear features, the value of which has been confirmed.

Algorithms↗

PubMed2002

A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns.

Feature selection plays an important role in classification. We present a comparative study on six feature selection heuristics by applying them to two sets of data. The first set of data are gene expression profiles from Acute Lymphoblastic Leukemia (ALL) patients. The second set of data are proteomic patterns from ovarian cancer patients. Based on features chosen by these methods, error rates of several classification algorithms were obtained for analysis. Our results demonstrate the importance of feature selection in accurately classifying new samples.

Algorithms↗

PubMed1992

Probability estimation for biomedical classification problems.

Suppose that we wish to know the probability that an object belongs to a class. For example, we may wish to estimate the probability that a patient has a particular disease, given a set of symptoms, or we may wish to know the probability that a novel peptide binds to a receptor, given the peptide's amino-acid composition. The conventional approach is to first use a classification algorithm to find partitions in feature space and to assign each partition to a class, and then to estimate the conditional probabilities as the proportion of patients or peptides that are correctly and incorrectly classified in each partition. Unfortunately, this estimation method often gives probability estimates that are in error by 20% or more, and thus can cause incorrect decisions. We have implemented and compared alternative methods. In Monte Carlo simulations the alternative methods are substantially more accurate than is the current method.

Epidemiology↗

PubMed2004

Prostate-specific antigen and free prostate-specific antigen in the early detection of prostate cancer: do combination tests improve detection?

BACKGROUND: The combined use of free and total prostate-specific antigen (PSA) in early detection of prostate cancer has been controversial. This article systematically evaluates the discriminating capacity of a large number of combination tests. METHODS: Free and total PSA were analyzed in stored serum samples taken prior to diagnosis in 429 cases and 1,640 controls from the Physicians' Health Study. We used a classification algorithm called logic regression to search for clinically useful tests combining total and percent free PSA and receiver operating characteristic analysis and compared these tests with those based on total and complexed PSA. Data were divided into training and test subsets. For robustness, we considered 35 test-train splits of the original data and computed receiver operating characteristic curves for each test data set. RESULTS: The average area under the receiver operating characteristic curve across test data sets was 0.74 for total PSA and 0.76 for the combination tests. Combination tests with higher sensitivity and specificity than PSA > 4.0 ng/mL were identified 29 out of 35 times. All these tests extended the PSA reflex range to below 4.0 ng/mL. Receiver operating characteristic curve analysis indicated that the overall diagnostic performance as expressed by the area under the curve did not differ significantly for the different tests. CONCLUSIONS: Tests combining total and percent free PSA show modest overall improvements over total PSA. However, utilization of percent free PSA below a PSA threshold of 4 ng/mL could translate into a practically important reduction in unnecessary biopsies without sacrificing cancers detected.

Aged↗