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PubMed2026

The machine-learning classifier ALLCatchR2 identifies 20 T-ALL subtypes across cohorts and age groups.

T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expression definitions are lacking. To establish a gene-expression-anchored framework for T-ALL subtyping, we aggregated 2314 transcriptomes (15 cohorts, age: 0.8-90.8 years). An extended unsupervised approach defined 17 main clusters and 3 subclusters in samples with high blast fractions. Supervised analyses added an overarching immature T-ALL (early T cell precursor [ETP]-like) definition and resolved the LMO2 γδ-like subtype. All clusters contained samples from at least two cohorts. Characteristic genomic driver enrichments were consistent across cohorts, while gene-expression clusters did not correspond exclusively to single driver events but also reflected developmental origins. A machine-learning classifier based on ALLCatchR, our B-cell acute lymphoblastic leukemia (B-ALL) classifier, identified these 20 transcriptomic subtypes and the immature T-ALL (ETP-like) signature with 0.995-1.0 accuracy in a validation set (n = 203). Testing the classifier on a second hold-out data set (n = 265 samples) showed that 92.7% of predictions matched with corresponding driver alterations. Across all samples, 83.2% of cases received high-confidence predictions, 7.3% candidate predictions, and 9.5% remained unclassified, largely because of low blast fractions. We identified a novel gene-expression cluster markedly enriched (P < 0.001) for clonal hematopoiesis mutations (IDH2 R140Q, DNMT3A) and a stem-/progenitor cell-like gene expression. This novel clonal hematopoiesis-related T-ALL subtype was observed in six cohorts and accounted for 8.9% of adults and 39.5% of patients aged >50 years. We extended ALLCatchR into ALLCatchR2, a free R package that now enables B-/T-lineage separation, gene-expression subtyping, blast estimation, and developmental annotation to harmonize T-ALL classification across studies and clinical contexts.

Journal Article↗

PubMedDate not supplied

Quantification of white matter and gray matter volumes from T1 parametric images using fuzzy classifiers.

White matter (WM) and gray matter (GM) were accurately measured using a technique based on a single standardized fuzzy classifier (FC) for each tissue. Fuzzy classifier development was based on experts' visual assessments of WM and GM boundaries from a set of T1 parametric MR images. The fuzzy classifier method's accuracy was validated and optimized by a set of T1 phantom images that were based on hand-detailed human brain cryosection images. Nine sets of axial T1 images of varying thickness equally distributed throughout the brain were simulated. All T1 data sets were mapped to the standardized FCs and rapidly segmented into WM and GM voxel fraction images. Resulting volumes revealed that, in most cases, the difference between measured and actual volumes was less than 5%. This was consistent throughout most of the brain, and as expected, the accuracy improved to generally less than 2% for the 1-mm simulated brain slices.

Artifacts↗

PubMed2005

Frequency of malignancy in lesions classified as probably benign after dynamic contrast-enhanced breast MRI examination.

PURPOSE: To determine the chance of malignancy in lesions classified as "probably benign" by dynamic magnetic resonance imaging (MRI), in a heterogeneous population. MATERIALS AND METHODS: Reports from 473 patients, from March 1994 to March 2002, who underwent breast MRI were retrospectively reviewed. A total of 79 patients (17%) had lesions classified as probably benign after the MRI, which required further imaging follow-up. We evaluated subsequent MRI, mammographic reports, and clinical follow-up in these patients and established the frequency of malignancy in this group. RESULTS: MRI classified probably benign lesion were diagnosed in 79 women because of focal or diffuse mild enhancement and benign dynamic enhancement curves in the area of the mammographic abnormality, or because of the presence of microcalcifications on the mammogram, or because of incidental enhancing lesions. Two-year radiographic and/or clinical follow-up was available in 68 women. On follow-up, four women (6%) were diagnosed with cancer between 14 and 18 months after the initial MRI. CONCLUSION: Patients with a lesion assessed as probably benign by dynamic contrast enhanced MRI have a higher chance of malignancy than patients with probably benign lesions (Breast Imaging Reporting and Data System category 3, BI-RADS 3) seen on mammography. These patients should be informed of the increased risk of cancer and be given the option of biopsy or close follow-up.

Adult↗

PubMed2005

Texture analysis for tissue discrimination on T1-weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers.

PURPOSE: To investigate the reproducibility and transferability of texture features between MR centers, and to compare two feature selection methods and two classifiers. MATERIALS AND METHODS: Coronal T1-weighted MR images of the knees of 63 patients, divided into three groups, were included in the study. MR images were obtained at three different MR centers. Regions of interest (ROIs) were drawn in the bone marrow and fat tissue. Then texture analysis (TA) of the ROIs was performed, and the most discriminant features were identified using Fisher coefficients and POE+ACC (probability of classification error and average correlation coefficients). Based on these features, artificial neural network (ANN) and k-nearest-neighbor (k-NN) classifiers were used for tissue discrimination. RESULTS: Although the texture features differed among the MR centers, features from one center could be successfully used for tissue discrimination in texture data on MR images from other centers. The best results were achieved using the ANN classifier in combination with features selected by POE+ACC. CONCLUSION: The differences in texture features extracted from MR images from different centers seem to have only a small impact on the results of tissue discrimination.

Adipose Tissue↗

PubMed2002

Development of a non-invasive strategy to classify bladder outlet obstruction in male patients with LUTS.

To diagnose bladder outlet obstruction in male patients with lower urinary tract symptoms (LUTS), it is necessary to measure the bladder pressure via a transurethral (or suprapubic) catheter. This procedure incurs some risk of urinary tract infection and urethral trauma and is sometimes painful to the patient. We developed an external condom catheter to measure non-invasively the bladder pressure and developed a strategy to classify bladder outlet obstruction (BOO) based on this measurement. Seventy-five patients with a wide range of urological diagnoses underwent a pressure-flow study followed by a non-invasive study. We tested five different strategies to classify the patients using the provisional International Continence Society (ICS) method for definition of obstruction as the gold standard. Leakage of the external catheter occurred in eight (40%) of the first 20 tested patients. In the remaining 55 patients, only five (9%) of the measurements failed because of leakage. Of the 75 patients, 56 were successfully tested non-invasively. According to the ICS nomogram, the PFS showed that 22 of these patients were non-obstructed, 12 patients were equivocal, and 22 patients were obstructed. Ten of these 56 patients strained, and we found that the relatively high abdominal pressures in these patients were not reflected in the externally measured bladder pressure. Of the remaining 46 patients, 12 of 13 non-obstructed patients and 30 of 33 combined equivocal and obstructed patients could be correctly classified. We developed a simple, non-invasive classification strategy to identify BOO in those male patients who did not strain during voiding.

Catheterization↗

PubMed1991

Use of neural network analysis to classify electroencephalographic patterns against depth of midazolam sedation in intensive care unit patients.

The electroencephalographic (EEG) analog signal is complex and cannot easily be described by univariate variables. Clear visual changes in the EEG power spectrum can be present with little or no change in univariate variable values. A method that could produce a single value based on the total data available in the EEG power spectrum would be very useful in monitoring EEG changes. Neural network analysis is a technique that can take multiple inputs and produce a single output value using complicated processing patterns that require training to establish. We examined the usefulness of a series of neural network models to classify 63 EEG patterns against sedation level in 26 mechanically ventilated patients requiring midazolam for long-term sedation. During a stable period of sedation, a 4- to 60-minute period of EEG data was obtained concurrently with a sedation level from 1 (follows commands) to 7 (no or gag response to suctioning of the endotracheal tube). The EEG power spectrum was divided into equal frequency bands, and the log absolute powers in each of these bands were used as inputs for a series of neural network models. The output target was the sedation level associated with each set of EEG data. Networks were trained on a subset of EEG power/sedation score data pairs, and the ability to classify the remaining data pairs was tested. Using a t-test comparison with a random set of sedation levels, we found that trained neural network models classified EEG patterns against sedation level successfully (p less than 0.001).(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗

PubMed2001

Comparison of logistic and Bayesian classifiers for evaluating the risk of femoral neck fracture in osteoporotic patients.

Femoral neck fracture prediction is an important social and economic issue. The research compares two statistical methods for the classification of patients at risk for femoral neck fracture: multiple logistic regression and Bayes linear classifier. The two approaches are evaluated for their ability to separate femoral neck fractured patients from osteoporotic controls. In total, 272 Italian women are studied. Densitometric and geometric measurements are obtained from the proximal femur by dual energy X-ray absorptiometry. The performances of the two methods are evaluated by accuracy in the classification and receiver operating characteristic curves. The Bayes classifier achieves an accuracy approximately 1% higher than that of the multiple logistic regression. However, the performances of the two methods, evaluated by the area under the curves, are not statistically different. The study demonstrates that the Bayes linear classifier can be a valid alternative to multiple logistic regression in the classification of osteoporotic patients.

Aged↗

PubMed1999

[Management of primary non-classifiable anal fistulas].

UNLABELLED: In the surgery of anal fistulae, very demanding problems warranting special consideration are caused by the non-classifiable fistulae in ano. RESULTS: Of 823 patients who underwent surgery for anal fistulae between 1993 and 1996, 38 (4.5%) were, according to Parks' classification, non-classifiable; the anal canal was intact. There was no internal opening. All patients had already undergone operations, some of them multiple. In 53%, complete healing of the fistula was achieved by using a single excision. In 47% a recurrence developed. During a second revision we explored the intersphincteric space and were able to reclassify the fistulae in 50% of the cases. A continent fistulectomy led to complete healing in these patients. CONCLUSION: Non-classifiable fistulae in ano, in which an internal opening of the fistula cannot be found, can primarily be treated by a single excision of the fistula. If recurrence does occur, the patient should undergo exploration of the intersphincteric space in the region, where the cryptoglandular infection is suspected.

Adult↗

PubMed2005

Recognition of motor imagery electroencephalography using independent component analysis and machine classifiers.

Motor imagery electroencephalography (EEG), which embodies cortical potentials during mental simulation of left or right finger lifting tasks, can be used to provide neural input signals to activate a brain computer interface (BCI). The effectiveness of such an EEG-based BCI system relies on two indispensable components: distinguishable patterns of brain signals and accurate classifiers. This work aims to extract two reliable neural features, termed contralateral and ipsilateral rebound maps, by removing artifacts from motor imagery EEG based on independent component analysis (ICA), and to employ four classifiers to investigate the efficacy of rebound maps. Results demonstrate that, with the use of ICA, recognition rates for four classifiers (fisher linear discriminant (FLD), back-propagation neural network (BP-NN), radial-basis function neural network (RBF-NN), and support vector machine (SVM)) improved significantly, from 54%, 54%, 57% and 55% to 70.5%, 75.5%, 76.5% and 77.3%, respectively. In addition, the areas under the receiver operating characteristics (ROC) curve, which assess the quality of classification over a wide range of misclassification costs, also improved from .65, .60, .62, and .64 to .74, .76, .80 and .81, respectively.

Adult↗

PubMed1990

Sensitivity and specificity of current methods for classifying morbid obesity.

This study examined the sensitivity and specificity of current methods for classifying morbid obesity in females. Results suggest that current methods for classifying morbid obesity (greater than or equal to 45.5 kg over ideal weight or BMI greater than or equal to 45) do not provide acceptable specificity and sensitivity, respectively. We suggest that additional measurements such as total body fatness determined by hydrodensitometry be used to classify morbid obesity and determine eligibility for aggressive therapeutic interventions for weight loss.

Adult↗

PubMed2006

Organic anion transporting polypeptide 1B1 activity classified by SLCO1B1 genotype influences atrasentan pharmacokinetics.

OBJECTIVE: Our objective was to learn whether genetic polymorphisms of metabolic enzymes or transport proteins provide a mechanistic understanding of the in vivo disposition of atrasentan, a selective endothelin A receptor antagonist. METHODS: Atrasentan uptake was measured in HeLa cells transfected to express major alleles of organic anion transporting polypeptide 1B1 (OATP1B1). The results were used to classify individuals as extensive, intermediate, or poor OATP1B1 transporters according to their SLCO1B1 genotypes. Analysis of covariance including genotype, study, age, weight, sex, and ethnicity was used to identify factors influencing atrasentan single-dose (n = 44) and steady-state (n = 38) pharmacokinetic parameters. Genotypes for cytochrome P450 3A5, uridine diphosphate-glucuronosyltransferase (UGT) 1A1, UGT2B4, UGT2B15, adenosine triphosphate-binding cassette subfamily B (ABCB) 1, solute carrier organic anion transporter (SLCO) 1B1, and solute carrier family 22 (SLC22) A2 were each assessed. RESULTS: Single-dose atrasentan exposure (P = .0244), steady-state atrasentan exposure (P = .0108), and maximum postdose plasma concentration (P = .0002) were associated with OATP1B1 activity classified by SLCO1B1 genotype. No other tested genotypes were observed to be associated with both single-dose and steady-state atrasentan pharmacokinetics. CONCLUSIONS: OATP1B1 is a meaningful factor for atrasentan disposition. Individuals may be classified as having extensive, intermediate, or poor OATP1B1 transport phenotypes according to SLCO1B1 genotypes. Increased exposures of OATP1B1 substrates might be expected in individuals who have the poor transporter phenotype or are treated with an OATP1B1 inhibitor.

Adult↗

PubMed2005

Degeneration and height of cervical discs classified from MRI compared with precise height measurements from radiographs.

STUDY DESIGN: Descriptive study comparing MRI classifications with measurements from radiographs. OBJECTIVES: 1. Define the relationship between MRI classified cervical disc degeneration and objectively measured disc height. 2. Assess the level of inter- and intra-observer errors using MRI in defining cervical disc degeneration. SUMMARY OF BACKGROUND DATA: Cervical spine degeneration has been defined radiologically by loss of disc height, decreased disc and bone marrow signal intensity and disc protrusion/herniation on MRI. The intra- and inter-observer error using MRI in defining cervical degeneration influences data interpretation. Few previous studies have addressed this source of error. The relation and time sequence between cervical disc degeneration classified by MRI and cervical disc height decrease measured from radiographs is unclear. METHODS: The MRI classification of degeneration was based on nucleus signal, prolaps identification and bone marrow signal. Two neuro-radiologists evaluated the MR-images independently in a blinded fashion. The radiographic disc height measurements were done by a new computer-assisted method compensating for image distortion and permitting comparison with normal level-, age- and gender-appropriate disc height. RESULTS/CONCLUSIONS: 1. Progressing disc degeneration classified from MRI is on average significantly associated with a decrease of disc height as measured from radiographs. Within each MRI defined category of degeneration measured disc heights, however, scatter in a wide range. 2. The inter-observer agreement between two neuro-radiologists in both defining degeneration and disc height by MRI was only moderate. Studies addressing questions related to cervical disc degeneration should take this into consideration.

Adult↗

PubMed2005

Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS.

The transjugular intrahepatic portosystemic shunt (TIPS) is a treatment for cirrhotic patients with portal hypertension. A subgroup of patients dies in the first 6 months and another subgroup lives a long period of time. Nowadays, no risk factors have been identified in order to determine how long a patient will survive. An empirical study for predicting the survival rate within the first 6 months after TIPS placement is conducted using a clinical database with 107 cases and 77 variables. Applications of Bayesian classification models, based on Bayesian networks, to medical problems have become popular in the last years. Feature subset selection is useful due to the heterogeneity of the medical databases where not all the variables are required to perform the classification. In this paper, filter and wrapper approaches based on the feature subset selection are adapted to induce Bayesian classifiers (naive Bayes, selective naive Bayes, semi naive Bayes, tree augmented naive Bayes, and k-dependence Bayesian classifier) and are applied to distinguish between the two subgroups of cirrhotic patients. The estimated accuracies obtained tally with the results of previous studies. Moreover, the medical significance of the subset of variables selected by the classifiers along with the comprehensibility of Bayesian models is greatly appreciated by physicians.

Bayes Theorem↗

PubMed2005

A robust classifier combined with an auto-associative network for completing partly occluded images.

This paper describes an approach for constructing a classifier which is unaffected by occlusions in images. We propose a method for integrating an auto-associative network into a simple classifier. As the auto-associative network can recall the original image from a partly occluded input image, we can employ it to detect occluded regions and complete the input image by replacing those regions with recalled pixels. By iterating this reconstruction process, the integrated network is able to classify target objects with occlusions robustly. To confirm the effectiveness of this method, we performed experiments involving face image classification. It is shown that the classification performance is not decreased, even if about 30% of the face image is occluded.

Artificial Intelligence↗

PubMedDate not supplied

Incremental learning of feature space and classifier for face recognition.

We have proposed a new approach to pattern recognition in which not only a classifier but also a feature space of input variables is learned incrementally. In this paper, an extended version of Incremental Principal Component Analysis (IPCA) and Resource Allocating Network with Long-Term Memory (RAN-LTM) are effectively combined to implement this idea. Since IPCA updates a feature space incrementally by rotating the eigen-axes and increasing the dimensions, the inputs of a neural classifier must also change in their values and the number of input variables. To solve this problem, we derive an approximation of the update formula for memory items, which correspond to representative training samples stored in the long-term memory of RAN-LTM. With these memory items, RAN-LTM is efficiently reconstructed and retrained to adapt to the evolution of the feature space. This function is incorporated into our face recognition system. In the experiments, the proposed incremental learning model is evaluated over a self-compiled video clip of 24 persons. The experimental results show that the incremental learning of a feature space is very effective to enhance the generalization performance of a neural classifier in a realistic face recognition task.

Algorithms↗

PubMedDate not supplied

A hierarchical classifier using new support vector machines for automatic target recognition.

A binary hierarchical classifier is proposed for automatic target recognition. We also require rejection of non-object (non-target) inputs, which are not seen during training or validation, thus producing a very difficult problem. The SVRDM (support vector representation and discrimination machine) classifier is used at each node in the hierarchy, since it offers good generalization and rejection ability. Using this hierarchical SVRDM classifier with magnitude Fourier transform (|FT|) features, which provide shift-invariance, initial test results on infra-red (IR) data are excellent.

Algorithms↗

PubMed2005

Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data.

In the present study, we applied the Support Vector Machine (SVM) algorithm to perform multivariate classification of brain states from whole functional magnetic resonance imaging (fMRI) volumes without prior selection of spatial features. In addition, we did a comparative analysis between the SVM and the Fisher Linear Discriminant (FLD) classifier. We applied the methods to two multisubject attention experiments: a face matching and a location matching task. We demonstrate that SVM outperforms FLD in classification performance as well as in robustness of the spatial maps obtained (i.e. discriminating volumes). In addition, the SVM discrimination maps had greater overlap with the general linear model (GLM) analysis compared to the FLD. The analysis presents two phases: during the training, the classifier algorithm finds the set of regions by which the two brain states can be best distinguished from each other. In the next phase, the test phase, given an fMRI volume from a new subject, the classifier predicts the subject's instantaneous brain state.

Aged↗

PubMed1995

Classifying interval cancers.

No definitive way of classifying interval breast cancers (cancers presenting between screening rounds) has been determined, yet their number and classification forms one of the quality assurance (QA) standards for the National Health Service Breast Screening Programme (NHSBSP). This study was undertaken to identify how different screening centres undertake this process, and to compare the classifications obtained when a test set of mammograms was reviewed using three different methods. A questionnaire was sent to the 17 Regional UK breast screening QA centres. Twelve (80%) of the 15 centres completing the questionnaire had a formal method for reviewing interval cancers. Five of these (33%) attempted to simulate screening by mixing the interval cancers with other screening films. In 11 (73%) centres, a group (size range 3-14) of radiologists was involved. In a simulated film viewing exercise we assessed whether different methods of classification would alter the number of interval cancers classified as false negative (where an abnormality suspicious of malignancy can be identified on review of the original screening films). Six radiologists reviewed a set of 50 interval cancers by three different methods: independent reading of the interval cancers mixed with screening mammograms; independent reading of the interval cancers on their own; and finally a consensus opinion of the interval cancers alone using the films taken at diagnosis. No discussion or review of these cases had taken place prior to the study. The number of interval cancers classified as false negative increased by 10% when they were reviewed in isolation compared to review among 'normal' screening films. The false negative rate varied widely (4% to 56% P < 0.01) depending on the criteria required to fulfil a false negative interval cancer classification--whether only one, a majority, or all of the radiologists were required to see the abnormality on the original screening films. In order for inter-unit and regional comparisons to be useful, a standard review method of mixing the interval cancer films with normal screening films and using a consensus opinion by a minimum of three external reviewers is suggested.

Breast Neoplasms↗