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PubMed2002

Integrated decision support in a hospital cancer registry.

In this paper we present (a) a shell for integrated knowledge-based functions that is destined to support decision processes of the users of the Giessener Tumordokumentationssystem (GTDS) and (b) some results we obtained during a 6-month observation period at one of the customers of the GTDS. A special characteristic of the provided decision support is the high degree of integration in the underlying information system GTDS, i.e. the functions are triggered by events in the patient database, existing patient data is reused as input for the reasoning process and generated alerts are presented instantly to the end-user. The first routine field of application was supporting registrars to adhere to integrity constraints as defined by the International Agency of Research on Cancer (IARC) during the documentation process. This information is important for the registrars since the checks of the IARC are an accepted standard for data quality in cancer registries. The expected benefit of this application area is less effort in achieving adherence to the specification of the IARC by preventing the costly rectification at a later time. During the last 5 months of the observation period 164 alerts were displayed. About 65% of the assessed alerts were considered to be correct. Especially, the analysis of the incorrect alerts revealed some shortcomings in the knowledge behind some of the integrity constraints of the IARC. The general feedback from the end-users indicate positive user satisfaction. Currently, the shell is in use in six hospital cancer registries.

Artificial Intelligence↗

PubMed1991

Memory dysfunction in epilepsy patients as a derangement of normal physiology.

Patients with CPS often display recent memory deficits. Typically, general intelligence, perceptual skills, language, remote memory, and primary memory are all normal. However, the ability to learn new combinations of cognitively complex material is deficient. This deficit may be specific for verbal material (e.g., as a difficulty with learning to recall a response word given an unrelated cue word), for nonverbal material (e.g., as a difficulty in drawing a complex figure from memory), or for both verbal and nonverbal material. Because these characteristics are typical of memory deficits after MTL damage, it is reasonable to suspect that these deficits in patients with epilepsy also reflect MTL damage. In many cases, MTL damage is apparent from neuroimaging studies, whereas seizure semiology suggests MTL onset. In these patients, the same pathology might be the cause of both the ictus and memory deficits. In other cases, memory impairment appears to be secondary to seizures. This suggestion is supported by cases where prolonged complex partial status resulted in a permanent global amnesia. Cases with shorter-lasting memory deficits were also presented. Neuropsychological testing revealed specific recent-memory deficits that cleared 2 weeks after a flurry of CPS and 24 hr after a single seizure. Depth recordings have demonstrated that MTL electrographic seizures can occur without subjective manifestations. When these are evoked by local electrical stimulation, a profound inability to learn new material may be observed during the afterdischarge. Similarly, artificially induced MTL spike-and-wave complexes interfere with the memory for simultaneously presented complex visual scenes. Recent evidence suggests that all of the above phenomena may reflect the engagement by epileptiform processes of the association-cortex (AC)-MTL circuits used in normal human memory. In recent memory tasks, cognitive evoked-potential components N4 and P3 are generated in the MTL and to a lesser degree in related AC regions. The N4/P3 are strongly modulated by familiarity in recent memory. This modulation is eliminated by anterior temporal lobectomy. The typical slow wave following spontaneous MTL interictal spikes has the same MTL voltage topography, and thus probably similar synaptic generators, as the cognitive P3 potential. Furthermore, MTL spike-and-wave complexes can be evoked in recent memory tasks at a fixed latency equal to that of the N4.(ABSTRACT TRUNCATED AT 400 WORDS)

Adolescent↗

PubMed2026

The next-generation biomarkers in early-stage triple negative breast cancer.

PURPOSE OF REVIEW: Despite maximal neoadjuvant chemoimmunotherapy, nearly 40% of early-stage triple-negative breast cancer (TNBC) patients fail to achieve pathological complete response, underscoring an urgent need for biomarkers capable of guiding treatment modulation. This review summarizes recent advances in tumor-infiltrating lymphocytes (TILs), circulating tumor DNA (ctDNA), and genomic signatures, exploring their potential integration into clinical decision-making. RECENT FINDINGS: TILs remain the most validated, cost-effective prognostic biomarker, although standardized scoring is still needed to overcome interobserver variability. ctDNA has emerged as a dynamic, real-time prognostic tool, with postneoadjuvant detection strongly predicting relapse and worse outcomes. Nonetheless, optimal sampling timing remains undefined. Genomic signatures, particularly TNBC-DX, provide standardized, reproducible prognostic information by integrating immune and proliferative gene expression. Emerging data suggest that combining these biomarkers may offer a complementary and synergistic effect. SUMMARY: Multibiomarker integration, supported by prospective validation and automated models, represents a promising approach to personalize treatment algorithms in early-stage TNBC, balancing efficacy and toxicity while guiding escalation and de-escalation strategies.

artificial intelligence↗

PubMed2005

Unsupervised multistage image classification using hierarchical clustering with a Bayesian similarity measure.

A new multistage method using hierarchical clustering for unsupervised image classification is presented. In the first phase, the multistage method performs segmentation using a hierarchical clustering procedure which confines merging to spatially adjacent clusters and generates an image partition such that no union of any neighboring segments has homogeneous intensity values. In the second phase, the segments resulting from the first stage are classified into a small number of distinct states by a sequential merging operation. The region-merging procedure in the first phase makes use of spatial contextual information by characterizing the geophysical connectedness of a digital image structure with a Markov random field, while the second phase employs a context-free similarity measure in the clustering process. The segmentation procedure of region merging is implemented as a hierarchical clustering algorithm whereby a multiwindow approach using a pyramid-like structure is employed to increase computational efficiency while maintaining spatial connectivity in merging. From experiments with both simulated and remotely sensed data, the proposed method was determined to be quite effective for unsupervised analysis. In particular, the region-merging approach based on spatial contextual information was shown to provide more accurate classification of images with smooth spatial patterns.

Algorithms↗

PubMed2005

Quantitative characterization of airspace enlargement in emphysema.

The mean linear intercept (L(m)) can be used to estimate the surface area for gas exchange in the lung. However, in recent years, it is most commonly used as an index for characterizing the enlargement of airspaces in emphysema and the associated severity of structural destruction in the lung. Specifically, an increase in L(m) is thought to result from an increase in airspace sizes. In this paper, we examined how accurately L(m) measures the linear dimensions of airspaces from histological sections and a variety of computer-generated test images. To this end, we developed an automated method for measuring linear intercepts from digitized images of tissue sections and calculate L(m) as their mean. We examined how the shape of airspaces and the variability of their sizes influence L(m) as well as the distribution of linear intercepts. We found that, for a relatively homogeneous enlargement of airspaces, L(m) was a reliable index for detecting emphysema. However, in the presence of spatial heterogeneities with a large variability of airspace sizes, L(m) did not significantly increase and sometimes even decreased compared with its value in normal tissue. We also developed an automated method for measuring the area and computed an equivalent diameter of each individual airspace that is independent of shape. Finally, we introduced new indexes based on the moments of diameter that we found to be more reliable than L(m) to characterize airspace enlargement in the presence of heterogeneities.

Algorithms↗

PubMedDate not supplied

Ligand-based molecular modeling study on a chemically diverse series of cholecystokinin-B/gastrin receptor antagonists: generation of predictive model.

Pharmacophore hypotheses were developed for six structurally diverse series of cholecystokinin-B/gastrin receptor (CCK-BR) antagonists. A training set consisting of 33 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 2100 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond donors, one hydrophobic aliphatic, and one hydrophobic aromatic feature, had a correlation (r) of 0.884 and a root-mean-square deviation of 1.1526, and the cost difference between null cost and fixed cost was 81.5 bits. The model was validated on a test set consisting of six different series of 27 structurally diverse compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug-like molecules to retrieve new chemical entities as potent CCK-BR antagonists. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.

Algorithms↗

PubMed2005

LOCSVMPSI: a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST.

Subcellular location of a protein is one of the key functional characters as proteins must be localized correctly at the subcellular level to have normal biological function. In this paper, a novel method named LOCSVMPSI has been introduced, which is based on the support vector machine (SVM) and the position-specific scoring matrix generated from profiles of PSI-BLAST. With a jackknife test on the RH2427 data set, LOCSVMPSI achieved a high overall prediction accuracy of 90.2%, which is higher than the prediction results by SubLoc and ESLpred on this data set. In addition, prediction performance of LOCSVMPSI was evaluated with 5-fold cross validation test on the PK7579 data set and the prediction results were consistently better than the previous method based on several SVMs using composition of both amino acids and amino acid pairs. Further test on the SWISSPROT new-unique data set showed that LOCSVMPSI also performed better than some widely used prediction methods, such as PSORTII, TargetP and LOCnet. All these results indicate that LOCSVMPSI is a powerful tool for the prediction of eukaryotic protein subcellular localization. An online web server (current version is 1.3) based on this method has been developed and is freely available to both academic and commercial users, which can be accessed by at http://Bioinformatics.ustc.edu.cn/LOCSVMPSI/LOCSVMPSI.php.

Artificial Intelligence↗

PubMed1987

A decision-driven system to collect the patient history.

We have developed a computer-administered history designed to directly interview hospitalized patients with pulmonary disease. A frame-based decision system is used to direct the history and to generate a one- to five-member differential diagnostic list based on this history. This system incorporates a cognitive model of question selection and a Bayesian scoring algorithm. Structures to control the choice of questions are embedded in the diagnostic frames and in a QUERY program that makes the final choice of questions. We have compared the behavior of this decision-driven approach with a history taken using a paper questionnaire. The paper-based history presents 182 questions to every patient and captured 75% of 85 pulmonary diseases in its differential lists. The decision-driven system asks 50.7 +/- 31.0 (mean +/- standard deviation) and captured 74% of 61 pulmonary diseases. Our experience suggests that the use of a computerized diagnostic knowledge base to direct the selection of pertinent questions can substantially reduce the number of questions necessary to collect a diagnostically useful patient history.

Artificial Intelligence↗

PubMed1995

Ontology-based configuration of problem-solving methods and generation of knowledge-acquisition tools: application of PROTEGE-II to protocol-based decision support.

PROTEGE-II is a suite of tools and a methodology for building knowledge-based systems and domain-specific knowledge-acquisition tools. In this paper, we show how PROTEGE-II can be applied to the task of providing protocol-based decision support in the domain of treating HIV-infected patients. To apply PROTEGE-II, (1) we construct a decomposable problem-solving method called episodic skeletal-plan refinement, (2) we build an application ontology that consists of the terms and relations in the domain, and of method-specific distinctions not already captured in the domain terms, and (3) we specify mapping relations that link terms from the application ontology to the domain-independent terms used in the problem-solving method. From the application ontology, we automatically generate a domain-specific knowledge-acquisition tool that is custom-tailored for the application. The knowledge-acquisition tool is used for the creation and maintenance of domain knowledge used by the problem-solving method. The general goal of the PROTEGE-II approach is to produce systems and components that are reusable and easily maintained. This is the rationale for constructing ontologies and problem-solving methods that can be composed from a set of smaller-grained methods and mechanisms. This is also why we tightly couple the knowledge-acquisition tools to the application ontology that specifies the domain terms used in the problem-solving systems. Although our evaluation is still preliminary, for the application task of providing protocol-based decision support, we show that these goals of reusability and easy maintenance can be achieved. We discuss design decisions and the tradeoffs that have to be made in the development of the system.

Artificial Intelligence↗

PubMed2005

Data acceptance for automated leukocyte tracking through segmentation of spatiotemporal images.

A crucial task in inflammation research and inflammatory drug validation is leukocyte velocity data collection from microscopic video imagery. Since manual methods are bias-prone and extremely time consuming, automated tracking methods are required to compute cell velocities. However, an automated tracking method is of little practical use unless it is accompanied by a mechanism to validate the tracker output. In this paper, we propose a validation technique that accepts or rejects the output of automated tracking methods. The proposed method first generates a spatiotemporal image from the cell locations given by a tracking method; then, it segments the spatiotemporal image to detect the presence or absence of a leukocyte. For segmenting the spatiotemporal images, we employ an edge-direction sensitive nonlinear filter followed by an active contour based technique. The proposed nonlinear filter, the maximum absolute average directional derivative (MAADD), first computes the magnitude of the mean directional derivative over an oriented line segment and then chooses the maximum of all such values within a range of orientations of the line segment. The proposed active contour segmentation is obtained via growing contours controlled by a two-dimensional force field, which is constructed by imposing a Dirichlet boundary condition on the gradient vector flow (GVF) field equations. The performance of the proposed validation method is reported here for the outputs of three different tracking techniques: the method was successful in 97% of the trials using manual tracking, in 94% using correlation tracking and in 93% using active contour tracking.

Algorithms↗

PubMed2005

WaveRead: automatic measurement of relative gene expression levels from microarrays using wavelet analysis.

Gene expression microarrays monitor the expression levels of thousands of genes in an experiment simultaneously. To utilize the information generated, each of the thousands of spots on a microarray image must be properly quantified, including background correction. Most present methods require manual alignment of grids to the image data, and still often require additional minor adjustments on a spot by spot basis to correct for spotting irregularities. Such intervention is time consuming and also introduces inconsistency in the handling of data. A fully automatic, tested system would increase throughput and reliability in this field. In this paper, we describe WaveRead, a fully automated, standalone, open-source system for quantifying gene expression array images. Through the use of wavelet analysis to identify the spot locations and diameters, the system is able to automatically grid the image and quantify signal intensities and background corrections without any user intervention. The ability of WaveRead to perform proper quantification is demonstrated by analysis of both simulated images containing spots with donut shapes, elliptical shapes, and Gaussian intensity distributions, as well as of standard images from the National Cancer Institute.

Algorithms↗

PubMed2003

Statistical evaluation of the Predictive Toxicology Challenge 2000-2001.

MOTIVATION: The development of in silico models to predict chemical carcinogenesis from molecular structure would help greatly to prevent environmentally caused cancers. The Predictive Toxicology Challenge (PTC) competition was organized to test the state-of-the-art in applying machine learning to form such predictive models. RESULTS: Fourteen machine learning groups generated 111 models. The use of Receiver Operating Characteristic (ROC) space allowed the models to be uniformly compared regardless of the error cost function. We developed a statistical method to test if a model performs significantly better than random in ROC space. Using this test as criteria five models performed better than random guessing at a significance level p of 0.05 (not corrected for multiple testing). Statistically the best predictor was the Viniti model for female mice, with p value below 0.002. The toxicologically most interesting models were Leuven2 for male mice, and Kwansei for female rats. These models performed well in the statistical analysis and they are in the middle of ROC space, i.e. distant from extreme cost assumptions. These predictive models were also independently judged by domain experts to be among the three most interesting, and are believed to include a small but significant amount of empirically learned toxicological knowledge. AVAILABILITY: PTC details and data can be found at: http://www.predictive-toxicology.org/ptc/.

Algorithms↗

PubMed2005

Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery.

The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

Algorithms↗

PubMed2006

Self-organization of spiking neural network that generates autonomous behavior in a real mobile robot.

In this paper, we propose self-organization algorithm of spiking neural network (SNN) applicable to autonomous robot for generation of adoptive and goal-directed behavior. First, we formulated a SNN model whose inputs and outputs were analog and the hidden unites are interconnected each other. Next, we implemented it into a miniature mobile robot Khepera. In order to see whether or not a solution(s) for the given task(s) exists with the SNN, the robot was evolved with the genetic algorithm in the environment. The robot acquired the obstacle avoidance and navigation task successfully, exhibiting the presence of the solution. After that, a self-organization algorithm based on a use-dependent synaptic potentiation and depotentiation at synapses of input layer to hidden layer and of hidden layer to output layer was formulated and implemented into the robot. In the environment, the robot incrementally organized the network and the given tasks were successfully performed. The time needed to acquire the desired adoptive and goal-directed behavior using the proposed self-organization method was much less than that with the genetic evolution, approximately one fifth.

Action Potentials↗

PubMed2005

Case study: an evaluation of user-assisted hierarchical watershed segmentation.

This paper evaluates the effectiveness of an interactive, three-dimensional image segmentation technique that relies on watersheds. This paper presents two user-based case studies, which include two different groups of domain experts. Subjects manipulate a graphics-based front end to a hierarchy of segmented regions generated from a watershed segmentation algorithm, which is implemented in the Insight Toolkit. In the first study, medical students segment several different anatomical structures from the Visible Human Female head and neck color cryosection data. In the second study, radiologists use the interactive tool to produce models of brain tumors from MRI data. This paper presents a quantitative and qualitative comparison against hand contouring. To quantify accuracy, we estimate ground truth from the hand-contouring data using the Simultaneous Truth and Performance Estimation algorithm. We also apply metrics from the literature to estimate precision and efficiency. The watershed segmentation technique showed improved subject interaction times and increased inter-subject precision over hand contouring, with quality that is visually and statistically comparable. The analysis also identifies some failures in the watershed technique, where edges were poorly defined in the data, and note a trend in the hand-contouring results toward systematically larger segmentations, which raises questions about the wisdom of using expert segmentations to define ground truth.

Algorithms↗

PubMed2004

DBMap: a space-conscious data visualization and knowledge discovery framework for biomedical data warehouse.

Advances in digital imaging modalities as well as other diagnosis and therapeutic techniques have generated a massive amount of diverse data for clinical research. The purpose of this study is to investigate and implement a new intuitive and space-conscious visualization framework, called DBMap, to facilitate efficient multidimensional data visualization and knowledge discovery against the large-scale data warehouses of integrated image and nonimage data. The DBMap framework is built upon the TreeMap concept. TreeMap is a space constrained graphical representation of large hierarchical data sets, mapped to a matrix of rectangles, whose size and color represent interested database fields. It allows the display of a large amount of numerical and categorical information in limited real estate of the computer screen with an intuitive user interface. DBMap has been implemented and integrated into a large brain research data warehouse to support neurologic and neuroradiologic research at the University of California, San Francisco Medical Center. For imaging specialists and clinical researchers, this novel DBMap framework facilitates another way to better explore and classify the hidden knowledge embedded in medical image data warehouses.

Algorithms↗

PubMed2006

Metal artifact reduction in CT using tissue-class modeling and adaptive prefiltering.

High-density objects such as metal prostheses, surgical clips, or dental fillings generate streak-like artifacts in computed tomography images. We present a novel method for metal artifact reduction by in-painting missing information into the corrupted sinogram. The information is provided by a tissue-class model extracted from the distorted image. To this end the image is first adaptively filtered to reduce the noise content and to smooth out streak artifacts. Consecutively, the image is segmented into different material classes using a clustering algorithm. The corrupted and missing information in the original sinogram is completed using the forward projected information from the tissue-class model. The performance of the correction method is assessed on phantom images. Clinical images featuring a broad spectrum of metal artifacts are studied. Phantom and clinical studies show that metal artifacts, such as streaks, are significantly reduced and shadows in the image are eliminated. Furthermore, the novel approach improves detectability of organ contours. This can be of great relevance, for instance, in radiation therapy planning, where images affected by metal artifacts may lead to suboptimal treatment plans.

Algorithms↗

PubMed2005

Towards automated cellular image segmentation for RNAi genome-wide screening.

The Rho family of small GTPases is essential for morphological changes during normal cell development and migration, as well as during disease states such as cancer. Our goal is to identify novel effectors of Rho proteins using a cell-based assay for Rho activity to perform genome-wide functional screens using double stranded RNA (dsRNAs) interference. We aim to discover genes could cause the cell phenotype changed dramatically. Biologists currently attempt to perform the genome-wide RNAi screening to identify various image phenotypes. RNAi genome-wide screening, however, could easily generate more than a million of images per study, manual analysis is thus prohibitive. Image analysis becomes a bottleneck in realizing high content imaging screens. We propose a two-step segmentation approach to solve this problem. First, we determine the center of a cell using the information in the DNA-channel by segmenting the DNA nuclei and the dissimilarity function is employed to attenuate the over-segmentation problem, then we estimate a rough boundary for each cell using a polygon. Second, we apply fuzzy c-means based multi-threshold segmentation and sharpening technology; for isolation of touching spots, marker-controlled watershed is employed to remove touching cells. Furthermore, Voronoi diagrams are employed to correct the segmentation errors caused by overlapping cells. Image features are extracted for each cell. K-nearest neighbor classifier (KNN) is employed to perform cell phenotype classification. Experimental results indicate that the proposed approach can be used to identify cell phenotypes of RNAi genome-wide screens.

Algorithms↗