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PubMed1995

MR image classification algorithms for neurosurgical and stereotactic radiosurgical trajectory planning and volumetric analysis.

Neurosurgical and stereotactic radiosurgical trajectory planning involves a phase of image data acquisition followed by localization of tissues to be avoided or crossed during the procedure. Success of the procedure is often assessed during follow-up by performing volumetric analysis of tissues of interest. Both localization and volumetric analysis can be facilitated by classifying the input data into different tissue types. This study compares classification results based on four different input image data: (1) MR data obtained with one TR and TE parameter combination, (2) MR data acquired with eight combinations of TR and TE parameters, (3) input data compressed into four principal components, and (4) the input data (3) together with first- and second-order texture features. The algorithms (a) to reduce the dimensionality using principal components, (b) to generate texture features, (c) to determine feature usefulness in classification using the Wilk's lambda criterion, and (d) to classify the input set are described. Classification results are found to be poor for input data 1 and much improved (qualitatively and quantatively) for input data 2. Input data 3 produces classification results (quantitatively and qualitatively) similar to that of 2. Input data 4 is found to produce quantitative results similar to those of 2 and 3, but the qualitative results show enhanced classification of some tissues and distortion of others.

Algorithms↗
PubMed1986

Developmental classification of reading-disabled children.

The present study developed and used longitudinal cluster analysis, a multivariate classification technique, to classify a sample of 200 nonclinical normal and reading-disabled males based on their performances on a neuropsychological battery at kindergarten, second, and fifth grades. The resulting classification was examined against various internal and external validation criteria. Using a validation framework, five developmental subtypes of children, two normal and three deficit reader groups, were found. Three of these groups could best be described as partitions of a multivariate normal distribution; the other two, both containing deficit readers, showed different covariance structures, suggesting differing developmental patterns. These groups were shown to differ with respect to the domains of academic achievement, parental achievement, neurological status, birth histories, school behaviors, and neuropsychological-cognitive development. The results suggest that developmental classifications can be formed by using multivariate classification methods and the subtypes support findings from cross-sectional classification research on similar populations.

Child↗
PubMed2003

Cutaneous T-cell lymphoma: epidemiology, etiology, and classification.

The term cutaneous T-cell lymphoma (CTCL) describes a heterogeneous group of neoplasms of skin-homing T-cells that vary considerably in clinical presentation, histologic appearance, immunophenotype, and prognosis. CTCL represent approximately 75-80% of all primary cutaneous lymphomas, whereas primary cutaneous B cell lymphomas account for approximately 20-25%. For many years mycosis fungoides and Sézary's syndrome were the only known types of CTCL. In the last decade, based on a combination of clinical, histological, and immunophenotypical criteria, new types of CTCL have been defined and new classifications for this group of primary cutaneous lymphomas have been formulated. In this overview, characteristic features of the different types of CTCL recognized in the European Organization for Research and Treatment of Cancer (EORTC) classification for primary cutaneous lymphomas and the World Health Organization (WHO) classification will be reviewed. Key conclusions from this brief overview are: (1) that the term CTCL does not refer to a single disease entity, but to a group of diseases with different clinical behaviors, therapeutic requirements, and prognoses; (2) that diagnosis and classification should always be based on a combination of clinical, histologic, and immunologic criteria; and (3) that the WHO and EORTC classification schemes are broadly equivalent for almost 90% of CTCL patients. Key research priorities are to develop effective therapies for peripheral T-cell lymphoma, extranodal NK (natural killer) T-cell lymphoma and so-called blastic NK cell lymphoma, and to determine molecular profiles for all forms of CTCL.

Antigens, CD↗
PubMedDate not supplied

DSM-IV diagnostic criteria for pathological gambling: reliability, validity, and classification accuracy.

The purpose of this study was to examine the reliability, validity, and classification accuracy of the DSM-IV diagnostic criteria for pathological gambling. Given the lack of a laboratory test to diagnose pathological gambling, two groups were recruited in order to test DSM-IV diagnostic classification accuracy, one which likely had the disorder and the other which likely did not have the disorder (121 men and women clients at a gambling treatment facility) (138 men and women selected at random from the Windsor, Ontario, community who had gambled in the past twelve months). The Gambling Behavior Interview was administered to both groups. The Gambling Behavior Interview was administered to both groups. The Gambling Behavior Interview includes items that measure the ten DSM-IV diagnostic criteria for pathological gambling as well as other gambling problem severity measures and scales that served as tests of convergent validity. The ten DSM-IV diagnostic criteria were found to exhibit satisfactory reliability, validity, and classification accuracy; however, lowering the cut score to four and using item weights yielded improved classification accuracy over the standard cut score of five. Some diagnostic criteria were found to have greater discriminatory power than other criteria. The results of this study suggest that the classification accuracy of DSM-IV diagnostic criteria can be improved upon with a lower cut score or using weighted criteria.

Adolescent↗
PubMed2000

Motor neuron disease: classification and nomenclature.

The classification and nomenclature of motor neuron disease, whether sporadic or familial, is confused. For example, both the sporadic and familial motor neuron diseases are phenotypically heterogeneous and, in familial ALS, phenotypic heterogeneity correlates only weakly with different underlying mutations in the SOD1 gene. We propose a classification which is based on underlying causative mechanisms, where these are known, but which also recognizes different clinical phenotypes when the cause is unknown. This classification is flexible, and allows reattribution of clinical syndromes when their causation is understood. Currently uncertain associations--for example, a possible association of ALS with cancer--are given tentative recognition in this classification. In addition, this new classification recognizes geographical clustering and descriptions of unusual motor neuron disorder phenotypes of unknown origin in different parts of the world.

Amyotrophic Lateral Sclerosis↗
PubMed2006

Use of border information in the classification of mammographic masses.

We are developing a new method to characterize the margin of a mammographic mass lesion to improve the classification of benign and malignant masses. Towards this goal, we designed features that measure the degree of sharpness and microlobulation of mass margins. We calculated these features in a border region of the mass defined as a thin band along the mass contour. The importance of these features in the classification of benign and malignant masses was studied in relation to existing features used for mammographic mass detection. Features were divided into three groups, each representing a different mass segment: the interior region of a mass, the border and the outer area. The interior and the outer area of a mass were characterized using contrast and spiculation measures. Classification was done in two steps. First, features representing each of the three mass segments were merged into a neural network classifier resulting in a single regional classification score for each segment. Secondly, a classifier combined the three single scores into a final output to discriminate between benign and malignant lesions. We compared the classification performance of each regional classifier and the combined classifier on a data set of 1076 biopsy proved masses (590 malignant and 486 benign) from 481 women included in the Digital Database for Screening Mammography. Receiver operating characteristic (ROC) analysis was used to evaluate the accuracy of the classifiers. The area under the ROC curve (A(z)) was 0.69 for the interior mass segment, 0.76 for the border segment and 0.75 for the outer mass segment. The performance of the combined classifier was 0.81 for image-based and 0.83 for case-based evaluation. These results show that the combination of information from different mass segments is an effective approach for computer-aided characterization of mammographic masses. An advantage of this approach is that it allows the assessment of the contribution of regions rather than individual features. Results suggest that the border and the outer areas contained the most valuable information for discrimination between benign and malignant masses.

Algorithms↗
PubMed2006

Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models.

Accelerometry shows promise in providing an inexpensive but effective means of long-term ambulatory monitoring of elderly patients. The accurate classification of everyday movements should allow such a monitoring system to exhibit greater 'intelligence', improving its ability to detect and predict falls by forming a more specific picture of the activities of a person and thereby allowing more accurate tracking of the health parameters associated with those activities. With this in mind, this study aims to develop more robust and effective methods for the classification of postures and motions from data obtained using a single, waist-mounted, triaxial accelerometer; in particular, aiming to improve the flexibility and generality of the monitoring system, making it better able to detect and identify short-duration movements and more adaptable to a specific person or device. Two movement classification methods were investigated: a rule-based Heuristic system and a Gaussian mixture model (GMM)-based system. A novel time-domain feature extraction method is proposed for the GMM system to allow better detection of short-duration movements. A method for adapting the GMMs to compensate for the problem of limited user-specific training data is also proposed and investigated. Classification performance was considered in relation to data gathered in an unsupervised, directed routine conducted in a three-month field trial involving six elderly subjects. The GMM system was found to achieve a mean accuracy of 91.3%, distinguishing between three postures (sitting, standing and lying) and five movements (sit-to-stand, stand-to-sit, lie-to-stand, stand-to-lie and walking), compared to 71.1% achieved by the Heuristic system. The adaptation method was found to offer a mean accuracy of 92.2%; a relative improvement of 20.2% over tests without subject-specific data and 4.5% over tests using only a limited amount of subject-specific data. While limited to a restricted subset of possible motions and postures, these results provide a significant step in the search for a more robust and accurate ambulatory classification system.

Acceleration↗
PubMed2004

Motor imagery classification by means of source analysis for brain-computer interface applications.

We report a pilot study of performing classification of motor imagery for brain-computer interface applications, by means of source analysis of scalp-recorded EEGs. Independent component analysis (ICA) was used as a spatio-temporal filter extracting signal components relevant to left or right motor imagery (MI) tasks. Source analysis methods including equivalent dipole analysis and cortical current density imaging were applied to reconstruct equivalent neural sources corresponding to MI, and classification was performed based on the inverse solutions. The classification was considered correct if the equivalent source was found over the motor cortex in the corresponding hemisphere. A classification rate of about 80% was achieved in the human subject studied using both the equivalent dipole analysis and the cortical current density imaging analysis. The present promising results suggest that the source analysis approach could manifest a clearer picture on the cortical activity, and thus facilitate the classification of MI tasks from scalp EEGs.

Algorithms↗
PubMed2006

Different classification techniques considering brain computer interface applications.

In this work the application of different machine learning techniques for classification of mental tasks from electroencephalograph (EEG) signals is investigated. The main application for this research is the improvement of brain computer interface (BCI) systems. For this purpose, Bayesian graphical network, neural network, Bayesian quadratic, Fisher linear and hidden Markov model classifiers are applied to two known EEG datasets in the BCI field. The Bayesian network classifier is used for the first time in this work for classification of EEG signals. The Bayesian network appeared to have a significant accuracy and more consistent classification compared to the other four methods. In addition to classical correct classification accuracy criteria, the mutual information is also used to compare the classification results with other BCI groups.

Algorithms↗
PubMed2006

Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time-frequency tilings.

We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings in a brain computer interface (BCI) task. The technique is based on an adaptive time-frequency analysis of EEG signals computed using local discriminant bases (LDB) derived from local cosine packets (LCP). In an offline step, the EEG data obtained from the C(3)/C(4) electrode locations of the standard 10/20 system is adaptively segmented in time, over a non-dyadic grid by maximizing the probabilistic distances between expansion coefficients corresponding to left and right hand movement imagery. This is followed by a frequency domain clustering procedure in each adapted time segment to maximize the discrimination power of the resulting time-frequency features. Then, the most discriminant features from the resulting arbitrarily segmented time-frequency plane are sorted. A principal component analysis (PCA) step is applied to reduce the dimensionality of the feature space. This reduced feature set is finally fed to a linear discriminant for classification. The online step simply computes the reduced dimensionality features determined by the offline step and feeds them to the linear discriminant. We provide experimental data to show that the method can adapt to physio-anatomical differences, subject-specific and hemisphere-specific motor imagery patterns. The algorithm was applied to all nine subjects of the BCI Competition 2002. The classification performance of the proposed algorithm varied between 70% and 92.6% across subjects using just two electrodes. The average classification accuracy was 80.6%. For comparison, we also implemented an adaptive autoregressive model based classification procedure that achieved an average error rate of 76.3% on the same subjects, and higher error rates than the proposed approach on each individual subject.

Algorithms↗
PubMed2000

Tissue classification with gene expression profiles.

Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer-related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. In this work we examine three sets of gene expression data measured across sets of tumor(s) and normal clinical samples: The first set consists of 2,000 genes, measured in 62 epithelial colon samples (Alon et al., 1999). The second consists of approximately equal to 100,000 clones, measured in 32 ovarian samples (unpublished extension of data set described in Schummer et al. (1999)). The third set consists of approximately equal to 7,100 genes, measured in 72 bone marrow and peripheral blood samples (Golub et al, 1999). We examine the use of scoring methods, measuring separation of tissue type (e.g., tumors from normals) using individual gene expression levels. These are then coupled with high-dimensional classification methods to assess the classification power of complete expression profiles. We present results of performing leave-one-out cross validation (LOOCV) experiments on the three data sets, employing nearest neighbor classifier, SVM (Cortes and Vapnik, 1995), AdaBoost (Freund and Schapire, 1997) and a novel clustering-based classification technique. As tumor samples can differ from normal samples in their cell-type composition, we also perform LOOCV experiments using appropriately modified sets of genes, attempting to eliminate the resulting bias. We demonstrate success rate of at least 90% in tumor versus normal classification, using sets of selected genes, with, as well as without, cellular-contamination-related members. These results are insensitive to the exact selection mechanism, over a certain range.

Cluster Analysis↗
PubMed2003

Probabilistic disease classification of expression-dependent proteomic data from mass spectrometry of human serum.

We have developed an algorithm called Q5 for probabilistic classification of healthy versus disease whole serum samples using mass spectrometry. The algorithm employs principal components analysis (PCA) followed by linear discriminant analysis (LDA) on whole spectrum surface-enhanced laser desorption/ionization time of flight (SELDI-TOF) mass spectrometry (MS) data and is demonstrated on four real datasets from complete, complex SELDI spectra of human blood serum. Q5 is a closed-form, exact solution to the problem of classification of complete mass spectra of a complex protein mixture. Q5 employs a probabilistic classification algorithm built upon a dimension-reduced linear discriminant analysis. Our solution is computationally efficient; it is noniterative and computes the optimal linear discriminant using closed-form equations. The optimal discriminant is computed and verified for datasets of complete, complex SELDI spectra of human blood serum. Replicate experiments of different training/testing splits of each dataset are employed to verify robustness of the algorithm. The probabilistic classification method achieves excellent performance. We achieve sensitivity, specificity, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset. The Q5 method outperforms previous full-spectrum complex sample spectral classification techniques and can provide clues as to the molecular identities of differentially expressed proteins and peptides.

Algorithms↗
PubMed2000

MultiFun, a multifunctional classification scheme for Escherichia coli K-12 gene products.

An enriched classification system for cellular functions of gene products of Escherichia coli K-12 was developed based on the initial classification by Riley. In the new classification scheme, MultiFun, cellular functions are divided into 10 major categories: Metabolism, Information Transfer, Regulation, Transport, Cell Processes, Cell Structure, Location, Extra-chromosomal Origin, DNA Site, and Cryptic Gene. These major categories are further sub-divided into a hierarchical scheme. Two thousand nine hundred twenty-two gene products of E. coli K-12 were assigned to one or more functions depending on the role they play in the cell. Functional assignments were made to 66% of E. coli gene products, ranging from 1 to 16 assignments per gene product. The expansion of cellular function categories and the assignment to more than one category (multifunction) provides a more complete description of the gene products and their roles and hence better reflects the functional complexity of organisms. We believe this classification system will be useful in the field of genome analysis, both for annotation purposes and for comparative studies. The functional classification scheme and the cellular function assignments made to E. coli gene products can be accessed from the web at the databases GenProtEC (http://genprotec.mbl.edu) and EcoCyc (http://www.ecocyc.org).

Bacterial Proteins↗
PubMed2003

Application of a case-mix classification based on the functional autonomy of the residents for funding long-term care facilities.

INTRODUCTION: increasing public costs for the care of the elderly have created fundamental changes that are redefining the basic principles of health care funding. In the past, overall institutional funding was predominantly tied to spending. In view of the limitations of this approach to funding long-term care facilities, case-mix classification tries to take into account the characteristics of the residents as a tool for predicting costs. Recently, a new case-mix classification based on the functional autonomy profile of the residents - ISO-SMAF profile - was developed in the Province of Quebec, Canada. This classification can be used to change the funding system to base it on the functional autonomy characteristics of the residents. OBJECTIVES: the main objective of this study was to apply the ISO-SMAF classification to funding long-term care facilities in one area of the Province of Quebec and to compare the results of this new funding methodology to the formal methodology. DESIGN: this study used a cross-sectional design. METHODOLOGY: the population under study comprised all residents of all 11 long-term care facilities in the Eastern Townships area of Quebec. Each resident was assessed using the Functional Autonomy Measurement System. The theoretical budget was calculated based on the adjusted cost per year associated with each ISO-SMAF profile derived from a previous economic study. RESULTS: the theoretical budget based on the ISO-SMAF profiles may highlight the under- or over-funding of a facility when compared to the usual funding system based predominantly on the number of beds and hours of care. CONCLUSION: the results of this study show the feasibility of applying the new funding approach to long-term care facilities. However, implementation of the ISO-SMAF classification for funding must be supported by continued and computerised residents' medical files including the Functional Autonomy Measurement System.

Activities of Daily Living↗
PubMed1999

Relative frequencies and sites of presentation of lymphoid neoplasms in a community hospital according to the revised European-American classification.

Relative frequencies for common subtypes in the revised European-American classification of lymphoid neoplasms (REAL classification) have been reported. We determined the relative frequencies and sites of presentation of REAL subtypes at a 700-bed community hospital in central Illinois. A database was used to identify and prospectively catalogue all newly diagnosed lymphoid neoplasms from July 1, 1995 to March 1, 1998. The approach to diagnosis and subtyping incorporated morphologic features, immunophenotype, and clinical findings according to criteria proposed in the REAL classification. Of 347 lymphoid neoplasms diagnosed, 319 were subtyped in the REAL classification. Of these, 261 were B-cell neoplasms, 21 were T-cell neoplasms, and 37 were Hodgkin disease variants. Chronic lymphocytic leukemia/small lymphocytic lymphoma/prolymphocytic leukemia, diffuse large cell, and follicle center neoplasms were the most common B-cell subtypes. Large granular lymphocyte leukemia was the most common T-cell neoplasm. Nodular sclerosis was the most common Hodgkin disease variant. The relative frequencies in a US community hospital setting are similar to those reported in other studies. Differences are attributable to patient selection criteria, study group geographic location and racial composition, and/or referral patterns. Diverse REAL classification subtypes may be expected in US community hospitals.

Adolescent↗
PubMed2004

Use of the WHO lymphoma classification in a population-based epidemiological study.

BACKGROUND: Non-Hodgkin's lymphoma (NHL) is pathologically diverse. Epidemiological investigations into its increasing incidence and aetiology require accurate subtype classification. PATIENTS AND METHODS: Available pathology reports of 717 cases aged from 20 to 74 years in an Australian, population-based epidemiological study of NHL were reviewed by one anatomical pathologist to assign a World Health Organization (WHO) classification category. High or low confidence was assigned to the diagnosis of NHL, cell phenotype and WHO category and reasons given for low confidence. RESULTS: The most informative biopsy reports were from open tissue biopsy (79% of cases), tissue core biopsy (8%), cytology (4%) and bone marrow (9%); 8% of cases had inadequate biopsies for diagnostic purposes. Immunohistochemistry or flow cytometry reports were available for 96% of cases, gene rearrangement studies for 6% and cytogenetics for 3%. The reviewer assigned high confidence to the diagnosis of NHL in 93% of cases and also the phenotype in 88%. While a WHO classification could be assigned in 91% of cases, confidence was high in only 57.5%; insufficient immunophenotyping was the commonest reason for low confidence. CONCLUSIONS: Expert pathology review of a population-based sample of NHL can provide a WHO classification category for most cases. A high level of confidence in the classification, however, would require review of diagnostic material and additional phenotyping.

Adult↗
PubMed2005

Is the new WHO classification of neuroendocrine tumours useful for selecting an appropriate treatment?

BACKGROUND: Neuroendocrine tumours (NETs) are a rare and heterogeneous group of neoplasms. The most recent WHO classification provides clinical tools and indications to make the diagnosis and to suggest the correct treatment in different subgroups of patients. The aim of this trial was to apply the new classification criteria in clinical practice and, accordingly, to choose the most appropriate treatment. PATIENTS AND METHODS: Thirty-one evaluable patients, not previously treated, classified as advanced well differentiated NETs according to the new classification, were given long-acting release octreotide 30 mg every 28 days until evidence of disease progression. The treatment activity was evaluated according to objective, biochemical and symptomatic responses. Safety and tolerability were also assessed. RESULTS: Two partial objective tumour responses were obtained (6%), stabilization occurred in 16 patients (52%) and 95% of patients had a disease stabilisation lasting > or =6 months. However, eight patients showed rapid disease progression within 6 months of therapy and six patients after 6 months. Biochemical responses, evaluated by changes in serum chromogranine A levels were reported in 20/24 patients (83%). Symptomatic responses were observed in 6/14 patients (43%): a complete syndrome remission in one patient, partial syndrome remission in five patients, no change in four patients and progressive disease in four patients. The median overall survival was not reached, and the median time to disease progression was 18 months (range 1-49 months). The treatment was well tolerated, no severe adverse events were observed and no patient withdrew from the study because of adverse events. CONCLUSIONS: The WHO classification enables identification of low-grade NET patients who may be suitable for hormonal treatment. Octreotide LAR was seen to be effective in controlling the disease and was well tolerated. However, eight patients failed to respond to the treatment, despite histological evidence of a well differentiated tumour according to the new classification. This suggests that further histological examination should be carried out, especially in patients with visceral metastases and a short disease-free interval.

Adult↗
PubMed2026

MegaPlantTF: a machine learning framework for comprehensive identification and classification of plant transcription factors.

MOTIVATION: Understanding the role of transcription factors (TFs) in plants is essential for the study of gene regulation and various biological processes. However, both TF detection and classification remain challenging due to the great diversity and complexity of these proteins. Conventional approaches, such as BLAST, often suffer from high computational complexity and limited performance on less common TF families. RESULTS: We introduce MegaPlantTF, the first comprehensive machine learning and deep learning framework for the prediction (TF versus non-TF) and classification (family-level) of plant TFs. Our method employs k-mer-based protein representations and a two-stage architecture combining a deep feed-forward neural network with a stacking ensemble classifier. To ensure robust performance assessment, we report micro-, macro-, and weighted-average performance metrics, providing a holistic evaluation of both frequent and underrepresented TF families. Additionally, we employ threshold-based evaluation to calibrate confidence in TF detection. The results show that MegaPlantTF achieves strong accuracy and precision, particularly with a k-mer size of 3 and a classification threshold of 0.5, and maintains stable performance even under stringent thresholds. In addition to the standard cross-validation tests, a use case study on Sorghum bicolor confirms that our method performs strongly in the genome-wide analysis, making it highly suitable for large-scale TF identification and classification tasks. MegaPlantTF represents a novel contribution by integrating k-mer encoding, binary family-specific classifiers, and a two-stage stacking ensemble into a unified, reproducible framework for large-scale plant TF identification and classification. AVAILABILITY AND IMPLEMENTATION: MegaPlantTF is freely accessible through a public web server available at https://bioinformatics.um6p.ma/MegaPlantTF. The complete source code, including pretrained models and example datasets, is available at https://github.com/Bioinformatics-UM6P/MegaPlantTF.

Transcription Factors↗