Search results for "ROC CURVE"

showing 10 items of 291 documents

Impact of Fasting Glycemia on Short-Term Prognosis after Acute Myocardial Infarction

2007

The prognosis of patients with acute myocardial infarction (MI), according to the new criteria for impaired fasting glucose (IFG) (FG 100-126 mg/dl), has not been evaluated.A total of 2353 patients with acute MI and surviving at d 5 after admission were analyzed for short-term morbidity and mortality. FG was obtained at d 4 and 5. Patients were classified as diabetes mellitus (known diabetes or FGor = 126 mg/dl), high IFG (110or = FG126 mg/dl), low IFG (100or = FG110 mg/dl), and normal fasting glucose (NFG) (FG100 mg/dl).Among the 2353 patients, 968 (41%) had diabetes mellitus, 262 (11%) had high IFG, 332 (14%) had low IFG, and 791 (34%) had NFG. Compared with NFG patients, 30-d cardiovascu…

Blood GlucoseMalemedicine.medical_specialtyendocrine system diseasesHeart diseaseEndocrinology Diabetes and MetabolismClinical BiochemistryMyocardial InfarctionSensitivity and Specificitybehavioral disciplines and activitiesBiochemistryCohort StudiesFasting glucoseEndocrinologyRisk FactorsInternal medicineDiabetes mellitusDiabetes MellitusPrevalencemedicineHumansMyocardial infarctionAcute miAgedCardiovascular mortalitybusiness.industryBiochemistry (medical)nutritional and metabolic diseasesFastingMiddle AgedPrognosismedicine.diseaseImpaired fasting glucoseEndocrinologyROC CurveHyperglycemiaHeart failureFemaleMorbiditybusinesshormones hormone substitutes and hormone antagonistspsychological phenomena and processesThe Journal of Clinical Endocrinology & Metabolism
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3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

2022

Rationale and Objectives: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radio-mics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation).Materials and Methods: 107 radiomic features were extracted from a …

Breast cancer Dynamic contrast-enhanced magnetic resonance imagingSupport Vector MachineComputer scienceNormalization (image processing)Breast NeoplasmsFeature selectionBreast cancerBreast cancerDiscriminative modelmedicineHumansRadiology Nuclear Medicine and imagingBreastRetrospective StudiesDynamic contrast-enhanced magnetic resonance imagingRadiomicsSupport vector machinesReceiver operating characteristicbusiness.industryPattern recognitionmedicine.diseaseMagnetic Resonance Imagingmachine learning Radiomics unsupervised feature selection Support vector machinesSupport vector machinemachine learningROC CurveFeature (computer vision)Test setFemaleArtificial intelligenceSettore MED/36 - Diagnostica Per Immagini E Radioterapiabusinessunsupervised feature selectionBreast cancer Dynamic contrast-enhanced magnetic resonance imaging; machine learning Radiomics unsupervised feature selection Support vector machinesAcademic Radiology
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Circulating cathepsin K and cystatin C in patients with cancer related bone disease: clinical and therapeutic implications.

2007

Abstract The clinical significance of serum cathepsin K and cystatin C was assessed in patients with breast cancer (BCa) or prostate cancer (PCa) with confined disease (M0) or bone metastasis (BM). Cathepsin K and cystatin C circulating levels were determined by ELISAs in 63 cancer patients, in 35 patients with nonmalignant diseases and in 42 healthy blood donors (control group). In BCa patients, cathepsin K serum levels were significantly lower than in sex matched control group (HS; p  = 0.0008) or in patients with primary osteoporosis (OP; p  = 0.0009). On the contrary, cystatin C levels were significantly higher in BCa patients than in HS ( p  = 0.0001) or OP ( p  = 0.017). In PCa patien…

CA15-3Malemedicine.medical_specialtyCathepsin KProstatic HyperplasiaBone NeoplasmsBreast NeoplasmsEnzyme-Linked Immunosorbent Assayurologic and male genital diseasesZoledronic AcidProstate cancerInternal medicinemedicineCathepsin KBiomarkers TumorHumansCystatin CAgedPharmacologyAged 80 and overbiologyBone Density Conservation AgentsDiphosphonatesbusiness.industryBone cancerImidazolesCancerBone metastasisProstatic NeoplasmsGeneral MedicineMiddle Agedmedicine.diseaseCathepsinsCystatinsBone metastasis; cathepsin K; Cystatin CEndocrinologyZoledronic acidCystatin CROC CurveBone metastasiCase-Control Studiesbiology.proteinDisease ProgressionOsteoporosisFemaleDrug Monitoringbusinessmedicine.drugBiomedicinepharmacotherapy = Biomedecinepharmacotherapie
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Procalcitonin and C-reactive protein as early predictors of anastomotic leak in colorectal surgery: a prospective observational study.

2013

Although the early diagnosis of anastomotic leak is a key point in reducing its clinical consequences, in daily practice, anastomotic leak diagnosis is often late.The aim of this study was to determine whether procalcitonin and C-reactive protein are good predictors of anastomotic leak in colorectal surgery.This is a prospective observational study.This study was conducted by a specialized colorectal multidisciplinary team of a tertiary teaching hospital.A series of 205 consecutive patients who underwent elective colorectal surgery in a specialized unit was prospectively analyzed. The following data were collected: demographic, surgical, ASA class, POSSUM, and morbidity. During the first 5 …

CalcitoninMalemedicine.medical_specialtyLeakColonCalcitonin Gene-Related PeptideAnastomotic LeakAnastomosisSensitivity and SpecificityProcalcitoninColon surgeryPredictive Value of TestsMedicineHumansPostoperative PeriodProspective StudiesProtein PrecursorsProspective cohort studybiologybusiness.industryC-reactive proteinGastroenterologyRectumGeneral MedicineMiddle AgedColorectal surgerySurgeryC-Reactive ProteinEarly DiagnosisROC CurvePredictive value of testsbiology.proteinFemalebusinessBiomarkersDiseases of the colon and rectum
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Compression-based classification of biological sequences and structures via the Universal Similarity Metric: experimental assessment.

2007

Abstract Background Similarity of sequences is a key mathematical notion for Classification and Phylogenetic studies in Biology. It is currently primarily handled using alignments. However, the alignment methods seem inadequate for post-genomic studies since they do not scale well with data set size and they seem to be confined only to genomic and proteomic sequences. Therefore, alignment-free similarity measures are actively pursued. Among those, USM (Universal Similarity Metric) has gained prominence. It is based on the deep theory of Kolmogorov Complexity and universality is its most novel striking feature. Since it can only be approximated via data compression, USM is a methodology rath…

Computer scienceAlgorismesPrediction by partial matchingCompression dissimilaritycomputer.software_genreBiochemistryProtein Structure SecondaryPhylogenetic studiesStructural BiologySequence Analysis ProteinDatabases Proteinlcsh:QH301-705.5Biological dataNCDApplied MathematicsGenomicsClassificationCDComputer Science ApplicationsBenchmarking:Informàtica::Informàtica teòrica [Àrees temàtiques de la UPC]Universal compression dissimilarityArea Under CurveMetric (mathematics)lcsh:R858-859.7Data miningAlgorithmsData compressionResearch Article:Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC]Normalization (statistics)lcsh:Computer applications to medicine. Medical informaticsBioinformatics Sequence Alignment AlgorithmsSet (abstract data type)Similarity (network science)Normalized compression sissimilarityData compression (Computer science)AnimalsHumansAmino Acid SequenceMolecular BiologyBiologyDades -- Compressió (Informàtica)USMUniversal similarity metricProteinsUCDProtein Structure TertiaryData setGenòmicaStatistical classificationlcsh:Biology (General)ROC CurvecomputerSequence AlignmentSoftwareBMC bioinformatics
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Dynamic-shared Pharmacophore Approach as Tool to Design New Allosteric PRC2 Inhibitors, Targeting EED Binding Pocket.

2020

Abstract: The Polycomb Repressive complex 2 (PRC2) maintains a repressive chromatin state and silences many genes, acting as methylase on histone tails. This enzyme was found overexpressed in many types of cancer. In this work, we have set up a Computer-Aided Drug Design approach based on the allosteric modulation of PRC2. In order to minimize the possible bias derived from using a single set of coordinates within the protein-ligand complex, a dynamic workflow was developed. In details, molecular dynamic was used as tool to identify the most significant ligand-protein interactions from several crystallized protein structures. The identified features were used for the creation of dynamic pha…

Computer scienceAllosteric regulationBinding pocketmacromolecular substancesComputational biologyMolecular Dynamics SimulationLigands01 natural sciences03 medical and health sciencesProtein structureStructural BiologyDrug DiscoveryHumans030304 developmental biologyEED0303 health sciencesVirtual screeningBinding SitesbiologyOrganic ChemistryMolecular DynamicPolycomb Repressive Complex 2Dynamic pharmacophorePRC20104 chemical sciencesComputer Science ApplicationsChromatinMolecular Docking Simulation010404 medicinal & biomolecular chemistryROC CurveDocking (molecular)Drug Designbiology.proteinMolecular MedicinePharmacophorePRC2Allosteric SiteProtein BindingMolecular informaticsReferences
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An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

2019

The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentati…

Computer scienceSVMKNN02 engineering and technologylcsh:TechnologyIIF imageHough transformlaw.inventionlcsh:Chemistry03 medical and health scienceslawClassifier (linguistics)0202 electrical engineering electronic engineering information engineeringPreprocessorGeneral Materials ScienceSegmentationcell segmentationlcsh:QH301-705.5InstrumentationIIF images030304 developmental biologyFluid Flow and Transfer Processes0303 health sciencesIndirect immunofluorescencelcsh:Tbusiness.industryProcess Chemistry and TechnologyGeneral EngineeringPattern recognitionSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)ROC curvelcsh:QC1-999Computer Science ApplicationsSupport vector machineParameter identification problemFluorescence intensityHough transformlcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040020201 artificial intelligence & image processingArtificial intelligencelcsh:Engineering (General). Civil engineering (General)businesslcsh:Physicsactive contours modelApplied Sciences
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A Microcalcification Detection System in Mammograms based on ANN Clustering

2018

Breast cancer is one of the leading causes to women mortality in the world. Clustered microcalcifications (MCs) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and treat the disease. In this work, we present a novel method for the detection of MCs in mammograms which consists of regions of Interest (ROIs) segmentation, based on a spatial filter that allows the detection of small and large microcalcifications, clustering and classification of MCs by Artificial Neural Network. The system has been tested on a public dataset of digital images and compared with previous approaches. The results demonstrate that the proposed approach could achie…

Computer sciencemammography02 engineering and technology030218 nuclear medicine & medical imaging03 medical and health sciencesDigital image0302 clinical medicineBreast cancer0202 electrical engineering electronic engineering information engineeringmedicineSegmentationSensitivity (control systems)Cluster analysisBreast canceimage segmentationArtificial neural networkbusiness.industryPattern recognitionmedicine.diseaseCad systemROC curveSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)020201 artificial intelligence & image processingArtificial intelligenceMicrocalcificationmedicine.symptombusinessANNclustering
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Automatic Extraction of Blood Vessels, Bifurcations and End Points in the Retinal Vascular Tree

2008

In this paper we present an effective algorithm for automated extraction of the vascular tree in retinal images, including bifurcations, crossovers and end-points detection. Correct identification of these features in the ocular fundus helps the diagnosis of important systematic diseases, such as diabetes and hypertension. The pre-processing consists in artefacts removal based on anisotropic diffusion filter. Then a matched filter is applied to enhance blood vessels. The filter uses a full adaptive kernel because each vessel has a proper orientation and thickness. The kernel of the filter needs to be rotated for all possible directions. As a consequence, a suitable kernel has been designed …

Cross-correlationPixelAnisotropic Diffusion Matched Filter Retinal Vessels ROC curve.Computer scienceAnisotropic diffusionbusiness.industryQuantitative Biology::Tissues and OrgansMatched filterBinary imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONRetinalPattern recognitionchemistry.chemical_compoundTree structurechemistryKernel (image processing)Artificial intelligencebusiness
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FABC: Retinal Vessel Segmentation Using AdaBoost

2010

This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as we…

Databases FactualComputer scienceFeature vectorFeature extractionNormal DistributionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingModels BiologicalEdge detectionArtificial IntelligenceImage Processing Computer-AssistedHumansSegmentationComputer visionAdaBoostFluorescein AngiographyElectrical and Electronic EngineeringTraining setPixelContextual image classificationSettore INF/01 - Informaticabusiness.industryReproducibility of ResultsRetinal VesselsWavelet transformBayes TheoremPattern recognitionGeneral MedicineImage segmentationComputer Science ApplicationsComputingMethodologies_PATTERNRECOGNITIONROC CurveTest setAdaBoost classifier retinal images vessel segmentationArtificial intelligencebusinessAlgorithmsBiotechnology
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