Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “CLASSIFICATION”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 613 records · Page 34Linked to original sources
PubMed1997

Distribution and incidence rates of lymphoid neoplasms according to the REAL classification in a single institution. A prospective study of 940 cases.

Recently, a new classification system for lymphoid neoplasms, known as the REAL classification, has been proposed. Our aim is to know the distribution of lymphoid neoplasms according to this schema and compare it with the Updated Kiel classification. We also estimate incidence rates of lymphoid neoplasms in our area. From January 1993 to November 1996, 940 patients were diagnosed of lymphoid neoplasm in our center. Histologic material was prospectively classified according to both the REAL and the Updated Kiel classifications. According to the REAL classification, distribution of all cases of lymphoid neoplasms was as follows: 73.6% B-cell neoplasm, 9.4% T-cell neoplasms, 9.6% Hodgkin's disease and 7.4% unclassifiable. Considering only non-Hodgkin's lymphomas (NHL), 87.2% of cases could be categorized according to the REAL and 77.7% with the Updated Kiel classification. These figures differed due to unrecognized categories in the Kiel schema. Annual incidence rate per 100,000 inhabitants was 20.1 for lymphoid neoplasms, and NHL alone was 9.0. In conclusion, the REAL classification allowed us to categorize more cases of NHL than did the Updated Kiel classification, fundamentally because of the inclusion of some recently described entities.

Adolescent↗
PubMed2002

A comparison of material classification techniques for ultrasound inverse imaging.

The conjugate gradient method with edge preserving regularization (CGEP) is applied to the ultrasound inverse scattering problem for the early detection of breast tumors. To accelerate image reconstruction, several different pattern classification schemes are introduced into the CGEP algorithm. These classification techniques are compared for a full-sized, two-dimensional breast model. One of these techniques uses two parameters, the sound speed and attenuation, simultaneously to perform classification based on a Bayesian classifier and is called bivariate material classification (BMC). The other two techniques, presented in earlier work, are univariate material classification (UMC) and neural network (NN) classification. BMC is an extension of UMC, the latter using attenuation alone to perform classification, and NN classification uses a neural network. Both noiseless and noisy cases are considered. For the noiseless case, numerical simulations show that the CGEP-BMC method requires 40% fewer iterations than the CGEP method, and the CGEP-NN method requires 55% fewer. The CGEP-BMC and CGEP-NN methods yield more accurate reconstructions than the CGEP method. A quantitative comparison of the CGEP-BMC, CGEP-NN, and GN-UMC methods shows that the CGEP-BMC and CGEP-NN methods are more robust to noise than the GN-UMC method, while all three are similar in computational complexity.

Breast Neoplasms↗
PubMed1998

Limited comparability of classifications of levels of neonatal care in UK units. The ECSURF (Economic Evaluation of Surfactant) Collaborative Study Group.

AIM: To assess whether different classifications of neonatal care or dependency scales are comparable when used in multicentre studies of cost effectiveness. METHODS: A survey of classifications was used in a nationally representative group of 57 units in 1990-1, with a retrospective study of 10 354 cot days using patient records from a 5% random sample of 1042 admissions. Local and national classifications were correlated with medical and nursing procedures recorded for up to 26 days after each admission. RESULTS: Classifications varied substantially. Of the 57 units in our sample, 26 used one of two national classifications, sometimes modified; 17 used the Northern Neonatal Network dependency scale; and the other 14 did not record daily levels of care. In each classification, the highest level was having respiratory support by ventilation or continuous distending pressure through an endotracheal tube, nasal prongs, facemask or negative pressure device. This level of care was consistently comparable between classifications; lower levels were not. CONCLUSIONS: Retrospective comparisons between units with different classifications can only reliably differentiate between days with and without respiratory support. There is a pressing need to develop and validate more appropriate scales for prospective multicentre studies. These should relate activity to costs and outcome.

Cost-Benefit Analysis↗
PubMed1991

Classification of findings in mammography screening--a method to minimise recall anxiety?

STUDY OBJECTIVE: The aim was to find out if it is possible, by classifying screening mammograms according to the likelihood of malignancy, to divide the recalled women to a group in which there is high suspicion of malignancy, most having breast cancers, and a group with more obscure findings. DESIGN: Screening mammograms of recalled women were classified according to the likelihood of malignancy. 0 = technically insufficient, 1 = normal, 2 = benign tumour, 3 = malignancy cannot be excluded, 4 = strongly suspicious for malignancy, 5 = malignant. SETTING: This study was a population based survey of mammography screening in Helsinki and surroundings in Finland. PATIENTS: 21,417 women (aged 50-59 years) were invited to be screened, 18,012 (84.10%) participated. Of these 579 (3.21% of those screened) were recalled for further studies; 124 of these were referred for surgical biopsy and 82 had breast cancer. MEASUREMENTS AND MAIN RESULTS: All cases classified as 5, 60% of the cases classified as 4, 6.5% of the cases classified as 3, 0% of the cases classified as 2 or 1, and 1.2% of the cases classified as 0 proved to have breast cancers. However classification 5 represented 5.9% of all recalled women and 41.5% of all screening detected breast cancers; classification 4, 6.0% of all recalled women and 25.6% of all screening detected breast cancers; classification 3, 68.9% of all recalled women and 31.7% of all screening detected breast cancers; classification 2, 11.7% and classification 1, 2.9% of all recalled women. No breast cancers were detected with these classifications. Classification 0 represented 4.5% of all recalled women and 1.2% of all screening detected breast cancers. Classifications 5 and 4 represented only 11.9% of all recalled women but 67.1% of all screening detected breast cancers. CONCLUSIONS: By classifying screening mammograms according to the likelihood of malignancy, recalled women can be divided into two groups: (1) a quite small subgroup in which everyone or almost everyone will be shown to have breast cancer; and (2) a much larger subgroup in which only a few will be proven to have breast cancer. The invitation procedure for the further studies should be improved on this basis of minimising anxiety among recalled women.

Anxiety↗
PubMed2006

Interobserver agreement for Letournel acetabular fracture classification with multidetector CT: are standard Judet radiographs necessary?

PURPOSE: To retrospectively evaluate interobserver agreement for Letournel acetabular fracture classification with radiography alone and multidetector computed tomography (CT) alone and to retrospectively assess whether standard Judet views lead to a change in the classification. MATERIALS AND METHODS: Institutional review board approval was obtained; informed consent was not required for this HIPAA-compliant study, which included 101 imaging studies performed in 99 patients (78 male, 21 female; mean age, 43 years; age range, 15-86 years) with acetabular fractures. Two musculoskeletal radiologists independently classified the fractures with radiography alone and multidetector CT alone. Multiplanar reformatted and three-dimensional (3D) CT images were reviewed at a computer workstation. Readers were shown radiographs at the end of multidetector CT image reading to see if this would change the multidetector CT-based classification. kappa Values were calculated to assess interobserver agreement. For surgically treated patients, the McNemar test was used to compare the accuracy of readers' classifications. The reference standard was a combination of preoperative radiographic and multidetector CT image findings and intraoperative findings. RESULTS: Interobserver agreement was moderate (kappa = 0.42) with radiography and substantial (kappa = 0.70) with multidetector CT. Multidetector CT classification was changed in two cases (one case for each reader) after standard Judet views were added. In 73 surgically treated patients, agreement with the surgeons' classification was higher with multidetector CT than with radiography (P < .01 for one reader, P = .06 for the other reader). CONCLUSION: There is substantial interobserver agreement for Letournel acetabular fracture classification with multiplanar reformatted and 3D multidetector CT images. Standard Judet pelvic radiographs add little information for changing the multidetector CT classification.

Acetabulum↗
PubMed2005

WHO non-Hodgkin's lymphoma classification by criterion-based report review followed by targeted pathology review: an effective strategy for epidemiology studies.

In a previous criterion-based pathology report review of 717 cases of non-Hodgkin's lymphoma in an Australian population-based epidemiologic study, a WHO category could be assigned in 91% of cases, but confidence in this classification was high in only 57.5%. Given this lack of confidence, a pathology review was done in a subset of 315 cases, with the aims of assigning a WHO classification category and the corresponding International Classification of Diseases for Oncology, Third Edition code in all cases previously unclassified or classified with low confidence and testing the accuracy of report review in assigning a confident WHO classification. After pathology review, 10 cases were ineligible (not non-Hodgkin's lymphoma, 3.2%) and 99% (301 of 305) of the remainder were assigned a WHO classification, with high confidence in 87% (261 of 301). There was 78% overall agreement between the WHO classification assigned by report review and pathology review, with 92% agreement when there was high confidence in the report review classification and 69% agreement when there was low confidence. Eighteen percent of follicular lymphomas and 23% of diffuse large B-cell lymphomas were reclassified. The pathology review increased the accuracy of WHO classification by an estimated 12.5% in the 694 cases who were still eligible in the study. Although a potential error rate of 7.5% remained, reviewing more cases, or not reviewing any cases classified with high confidence, would have produced only a small change in accuracy. Criterion-based pathology report review of all cases followed by selective pathology review in cases classified with low confidence is recommended as a cost-saving and accurate strategy for pathology review in large epidemiologic studies.

Adult↗
PubMed1987

The reliability of cytopathologists' classifications of bronchial epithelial atypias from Kodachrome slides.

The accuracy of computerized cell image analysis techniques for classification of atypical cells is determined by comparing computer-generated classifications with those made by cytopathologists. This measure of accuracy depends not only on the reliability of the computer classifications but also on the reliability of the cytopathologists' classifications. This study reports on the observed reliability of cytopathologists' classifications of squamous epithelial atypias in sputum across cytopathologists and two different classification times. Results indicated the percentage agreement among cytopathologists and computerized cell image analysis techniques. It is recommended that, in the future, all analyses of computerized classification schemes by interpreted in light of the consistency of the cytopathologists' classifications.

Diagnosis, Computer-Assisted↗
PubMed2005

WHO-EORTC classification for cutaneous lymphomas.

Primary cutaneous lymphomas are currently classified by the European Organization for Research and Treatment of Cancer (EORTC) classification or the World Health Organization (WHO) classification, but both systems have shortcomings. In particular, differences in the classification of cutaneous T-cell lymphomas other than mycosis fungoides, Sezary syndrome, and the group of primary cutaneous CD30+ lymphoproliferative disorders and the classification and terminology of different types of cutaneous B-cell lymphomas have resulted in considerable debate and confusion. During recent consensus meetings representatives of both systems reached agreement on a new classification, which is now called the WHO-EORTC classification. In this paper we describe the characteristic features of the different primary cutaneous lymphomas and other hematologic neoplasms frequently presenting in the skin, and discuss differences with the previous classification schemes. In addition, the relative frequency and survival data of 1905 patients with primary cutaneous lymphomas derived from Dutch and Austrian registries for primary cutaneous lymphomas are presented to illustrate the clinical significance of this new classification.

Humans↗
PubMed2004

Interaction profile-based protein classification of death domain.

BACKGROUND: The increasing number of protein sequences and 3D structure obtained from genomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimental and computational efforts on the creation and analysis of information derived from 3D structure. In particular, the high-throughput generation of protein-protein interaction data from a few organisms makes such an approach very important towards understanding the molecular recognition that make-up the entire protein-protein interaction network. Since the generation of sequences, and experimental protein-protein interactions increases faster than the 3D structure determination of protein complexes, there is tremendous interest in developing in silico methods that generate such structure for prediction and classification purposes. In this study we focused on classifying protein family members based on their protein-protein interaction distinctiveness. Structure-based classification of protein-protein interfaces has been described initially by Ponstingl et al. 1 and more recently by Valdar et al. 2 and Mintseris et al. 3, from complex structures that have been solved experimentally. However, little has been done on protein classification based on the prediction of protein-protein complexes obtained from homology modeling and docking simulation. RESULTS: We have developed an in silico classification system entitled HODOCO (Homology modeling, Docking and Classification Oracle), in which protein Residue Potential Interaction Profiles (RPIPS) are used to summarize protein-protein interaction characteristics. This system applied to a dataset of 64 proteins of the death domain superfamily was used to classify each member into its proper subfamily. Two classification methods were attempted, heuristic and support vector machine learning. Both methods were tested with a 5-fold cross-validation. The heuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an 89% average accuracy. CONCLUSION: We have confirmed the reliability and potential value of classifying proteins via their predicted interactions. Our results are in the same range of accuracy as other studies that classify protein-protein interactions from 3D complex structure obtained experimentally. While our classification scheme does not take directly into account sequence information our results are in agreement with functional and sequence based classification of death domain family members.

Humans↗
PubMed2006

A fast SCOP fold classification system using content-based E-Predict algorithm.

BACKGROUND: Domain experts manually construct the Structural Classification of Protein (SCOP) database to categorize and compare protein structures. Even though using the SCOP database is believed to be more reliable than classification results from other methods, it is labor intensive. To mimic human classification processes, we develop an automatic SCOP fold classification system to assign possible known SCOP folds and recognize novel folds for newly-discovered proteins. RESULTS: With a sufficient amount of ground truth data, our system is able to assign the known folds for newly-discovered proteins in the latest SCOP v1.69 release with 92.17% accuracy. Our system also recognizes the novel folds with 89.27% accuracy using 10 fold cross validation. The average response time for proteins with 500 and 1409 amino acids to complete the classification process is 4.1 and 17.4 seconds, respectively. By comparison with several structural alignment algorithms, our approach outperforms previous methods on both the classification accuracy and efficiency. CONCLUSION: In this paper, we build an advanced, non-parametric classifier to accelerate the manual classification processes of SCOP. With satisfactory ground truth data from the SCOP database, our approach identifies relevant domain knowledge and yields reasonably accurate classifications. Our system is publicly accessible at http://ProteinDBS.rnet.missouri.edu/E-Predict.php.

Algorithms↗
PubMed2006

Identifying genes that contribute most to good classification in microarrays.

BACKGROUND: The goal of most microarray studies is either the identification of genes that are most differentially expressed or the creation of a good classification rule. The disadvantage of the former is that it ignores the importance of gene interactions; the disadvantage of the latter is that it often does not provide a sufficient focus for further investigation because many genes may be included by chance. Our strategy is to search for classification rules that perform well with few genes and, if they are found, identify genes that occur relatively frequently under multiple random validation (random splits into training and test samples). RESULTS: We analyzed data from four published studies related to cancer. For classification we used a filter with a nearest centroid rule that is easy to implement and has been previously shown to perform well. To comprehensively measure classification performance we used receiver operating characteristic curves. In the three data sets with good classification performance, the classification rules for 5 genes were only slightly worse than for 20 or 50 genes and somewhat better than for 1 gene. In two of these data sets, one or two genes had relatively high frequencies not noticeable with rules involving 20 or 50 genes: desmin for classifying colon cancer versus normal tissue; and zyxin and secretory granule proteoglycan genes for classifying two types of leukemia. CONCLUSION: Using multiple random validation, investigators should look for classification rules that perform well with few genes and select, for further study, genes with relatively high frequencies of occurrence in these classification rules.

Colonic Neoplasms↗
PubMed2004

Classification between normal and tumor tissues based on the pair-wise gene expression ratio.

BACKGROUND: Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. METHOD: Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers. RESULTS: 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset - from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested that there exist some cancer-related signals in the form of pair-wise gene expression ratio. CONCLUSION: The results from this study indicated that: 1) in the case when the pair-wise expression ratio transformation achieves lower CV and higher correlation to tissue phenotypes, a better classification of tissue type will follow. 2) the comparable classification accuracy achieved after data transformation suggested that pair-wise gene expression ratio between some pairs of genes can identify reliable markers for cancer.

Colon↗
PubMed2004

Tumor taxonomy for the developmental lineage classification of neoplasms.

BACKGROUND: The new "Developmental lineage classification of neoplasms" was described in a prior publication. The classification is simple (the entire hierarchy is described with just 39 classifiers), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. A taxonomy is a list of the instances that populate a classification. The taxonomy of neoplasia attempts to list every known term for every known tumor of man. METHODS: The taxonomy provides each concept with a unique code and groups synonymous terms under the same concept. A Perl script validated successive drafts of the taxonomy ensuring that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in one and only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML (eXtensible Markup Language) document. RESULTS: The taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts. Each concept has, on average, 23 synonyms. The taxonomy populates "The developmental lineage classification of neoplasms," and is available as an XML file, currently 9+ Megabytes in length. A representation of the classification/taxonomy listing each term followed by its code, followed by its full ancestry, is available as a flat-file, 19+ Megabytes in length. The taxonomy is the largest nomenclature of neoplasms, with more than twice the number of neoplasm names found in other medical nomenclatures, including the 2004 version of the Unified Medical Language System, the Systematized Nomenclature of Medicine Clinical Terminology, the National Cancer Institute's Thesaurus, and the International Classification of Diseases Oncolology version. CONCLUSIONS: This manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and assigns each tumor to a single class within the tumor hierarchy. The entire classification and taxonomy are available as open access files (in XML and flat-file formats) with this article.

Cell Lineage↗
PubMed2003

Wavelets filtering for classification of very noisy electron microscopic single particles images--application on structure determination of VP5-VP19C recombinant.

BACKGROUND: Images of frozen hydrated [vitrified] virus particles were taken close-to-focus in an electron microscope containing structural signals at high spatial frequencies. These images had very low contrast due to the high levels of noise present in the image. The low contrast made particle selection, classification and orientation determination very difficult. The final purpose of the classification is to improve the signal-to-noise ratio of the particle representing the class, which is usually the average. In this paper, the proposed method is based on wavelet filtering and multi-resolution processing for the classification and reconstruction of this very noisy data. A multivariate statistical analysis (MSA) is used for this classification. RESULTS: The MSA classification method is noise dependent. A set of 2600 projections from a 3D map of a herpes simplex virus--to which noise was added--was classified by MSA. The classification shows the power of wavelet filtering in enhancing the quality of class averages (used in 3D reconstruction) compared to Fourier band pass filtering. A 3D reconstruction of a recombinant virus (VP5-VP19C) is presented as an application of multi-resolution processing for classification and reconstruction. CONCLUSION: The wavelet filtering and multi-resolution processing method proposed in this paper offers a new way for processing very noisy images obtained from electron cryo-microscopes. The multi-resolution and filtering improves the speed and accuracy of classification, which is vital for the 3D reconstruction of biological objects. The VP5-VP19C recombinant virus reconstruction presented here is an example, which demonstrates the power of this method. Without this processing, it is not possible to get the correct 3D map of this virus.

Capsid Proteins↗
PubMed1991

Classification of beef calves as protein-deficient or thermally stressed by discriminant analysis of blood constituents.

Linear discriminant functions hold promise for identifying either protein-deficient or cold-stressed calves based on blood constituents. For each of 2 yr 60 artificially bred Angus heifers were assigned randomly to a 2 x 2 factorial nutritional plan consisting of .32 or .96 kg/d of maternal CP and 8.7 or 12.2 Mcal/d of ME. The calves from these heifers were assigned randomly to environmental chambers set at either 0 or 21 degrees C in a repeated measures design. Linear discriminant functions were computed for 1 yr (training data) and then used to predict the classification of calves for the other year (validation data). Using the original data, the correct classifications of calves to the protein groups were 96, 80, 60, 59, 54, and 51% for blood samples obtained at 0, 12, 24, 36, 48, and 72 h of age, respectively. Using normalized data, corresponding correct classifications to protein groups were 94, 91, 80, 56, 54, and 52%. Results indicate that protein classification should use blood samples obtained within 12 h of age for reasonable success. For cold-stressed calves, correct classifications using original data were 47 (pre-exposure), 72, 54, 70, 67, and 66% for calves at 0, 12, 24, 36, 48, and 72 h of age, respectively. Corresponding correct classifications using normalized data were 54 (pre-exposure), 74, 70, 72, 69, and 77%. Cold stress could be detected after only 12 h of exposure; the time window for testing was much wider than for protein classification, but the classification generally was less discriminative.

Alkaline Phosphatase↗
PubMed1996

Diagnostic agreement in the classification of headache using Ad Hoc Committee and IHS criteria.

This study examined the level of agreement between raters in the diagnosis of headache using the Ad Hoc Committee (AHC) on the Classification of Headache and the Headache Classification Committee of the International Headache Society (IHS) classification criteria. In addition, differences in classification of headache between the AHC and IHS classification systems were considered. Analyses indicated that both the AHC and IHS classification systems were adequate in allowing clinicians to reach 100% agreement in diagnosis of headache cases (N = 36). Additionally, there was 91.7% diagnostic agreement between the two sets of criteria as applied by the raters. Differences in diagnostic classification between the two sets of criteria were believed to be the result of IHS exclusionary criteria in the classification of Tension-type headache.

Adult↗
PubMed1986

The "urge to classify" the drug user: a review of classifications by pattern of abuse.

Attempts to classify drug abusers are divided into three main categories: psychiatric classifications, psychosocial classifications and classifications by pattern of abuse. The present article focuses on pattern of abuse classifications which are divided into two subcategories: by substance of abuse and by degree of involvement. The various groups of these classifications are reviewed and their potential uses are discussed. The review indicates that classifications by substance of abuse may be useful for administrative and theoretical purposes, while their clinical uses are limited to medical emergencies. On the other hand, classifications by degree of involvement are useful for initial treatment planning and for predicting treatment outcomes. The authors conclude that for a thorough treatment planning and for better understanding of the addiction process, psychosocial classifications may be the most useful approach.

Humans↗
PubMed1994

Casemix classification and health care of the elderly.

The Australian national diagnosis-related groups (AN-DRGs) patient classification has highlighted the distinction between different categories of inpatient care and ambulatory care, and the need for an explicit definition of boundaries of associated categories. A nationally consistent definition of these patient care categories, and of episodes of care according to illness acuity, will facilitate the design of additional casemix classifications to supplement AN-DRGs. Specific features of the AN-DRGs classification will have a major impact on health care of the elderly through incentives created by funding arrangements based on this classification. The use of age as an AN-DRG classification criterion, as a surrogate for definitive secondary diagnoses, should be regarded as an interim measure pending improvement in medical record documentation, further analysis of the relationship of age partitions to these secondary diagnoses, and ongoing improvement of AN-DRG design. The complex process of development of casemix classifications for subacute and ambulatory care has commenced, and will also have a profound impact on health care of the elderly and on all specialities concerned with both acute and chronic illness, again because of financial incentives in the classification design. Funding for development and refinement of each of these casemix classifications will be required if the anticipated benefits are to occur.

Aged↗