UB Paderborn / Katalog / Suche / in CDI

Sortiert nach

Einfacher Volltextzugriff von (fast) überall

Installieren Sie die LibKey Nomad-Browsererweiterung und erhalten Sie von vielen Webseiten einfachen Zugriff auf lizenzierte Volltexte oder Open Access Publikationen. Mehr erfahren...

Longitudinal analysis of the patient pathways to diagnosis of psoriatic arthritis

Arthritis research & therapy, 2021-10, Vol.23 (1), p.1-252, Article 252

2021

Volltextzugriff (PDF)

Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals

Schizophrenia bulletin, 2018-08, Vol.44 (5), p.1035-1044

2018

Volltextzugriff (PDF)

Does Gender Impact a Diagnosis of Ankylosing Spondylitis?

ACR open rheumatology, 2022-06, Vol.4 (6), p.540-546

2022

Volltextzugriff (PDF)

Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities

Journal of medical imaging (Bellingham, Wash.), 2020-05, Vol.7 (3), p.031505-031505

2020

Volltextzugriff (PDF)

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Scientific data, 2017-09, Vol.4 (1), p.170117-170117, Article 170117

2017

Volltextzugriff (PDF)

NIMG-20. IMAGING PATTERN ANALYSIS REVEALS THREE DISTINCT PHENOTYPIC SUBTYPES OF GBM WITH DIFFERENT SURVIVAL RATES

Neuro-oncology (Charlottesville, Va.), 2016-11, Vol.18 (suppl_6), p.vi128-vi128

2016

Volltextzugriff (PDF)

Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning

Journal of medical imaging (Bellingham, Wash.), 2018-04, Vol.5 (2), p.021219-021219

2018

Volltextzugriff (PDF)

In Vivo Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature Consistent with Deep Peritumoral Infiltration: The φ -Index

Clinical cancer research, 2017-08, Vol.23 (16), p.4724-4734

2017

Volltextzugriff (PDF)

Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1

Scientific reports, 2018-03, Vol.8 (1), p.5087-12, Article 5087

2018

Volltextzugriff (PDF)

Application of machine learning in the diagnosis of axial spondyloarthritis

Current opinion in rheumatology, 2019-07, Vol.31 (4), p.362-367

2019

Volltextzugriff (PDF)

In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature

Neuro-oncology (Charlottesville, Va.), 2018-07, Vol.20 (8), p.1068-1079

2018

Volltextzugriff (PDF)

Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis

Clinical rheumatology, 2020-04, Vol.39 (4), p.975-982

2020

Volltextzugriff (PDF)

NIMG-38. QUANTITATIVE IMAGING PREDICTORS OF OVERALL SURVIVAL IN GLIOBLASTOMA PATIENTS ROBUST IN THE PRESENCE OF INTER-SCANNER VARIATIONS

Neuro-oncology (Charlottesville, Va.), 2018-11, Vol.20 (suppl_6), p.vi184-vi184

2018

Volltextzugriff (PDF)

MPTH-02. EXTRACELLULAR EGFR289 ACTIVATING MUTATIONS CONFER POORER SURVIVAL AND SUGGEST ENHANCED MOTILITY IN PRIMARY GBMs

Neuro-oncology (Charlottesville, Va.), 2016-11, Vol.18 (suppl_6), p.vi105-vi106

2016

Volltextzugriff (PDF)

Use of Fetal Magnetic Resonance Image Analysis and Machine Learning to Predict the Need for Postnatal Cerebrospinal Fluid Diversion in Fetal Ventriculomegaly

JAMA pediatrics, 2018-02, Vol.172 (2), p.128

2018

Volltextzugriff (PDF)

Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, p.133-145

Volltextzugriff (PDF)

NIMG-70. QUANTITATIVE IMAGE ANALYSIS AND MACHINE LEARNING TECHNIQUES FOR DISTINGUISHING TRUE PROGRESSION FROM PSEUDOPROGRESSION IN PATIENTS WITH GLIOBLASTOMA

Neuro-oncology (Charlottesville, Va.), 2018-11, Vol.20 (suppl_6), p.vi191-vi192

2018

Volltextzugriff (PDF)

NIMG-70. QUANTITATIVE IMAGE ANALYSIS AND MACHINE LEARNING TECHNIQUES FOR DISTINGUISHING TRUE PROGRESSION FROM PSEUDOPROGRESSION IN PATIENTS WITH GLIOBLASTOMA

Neuro-oncology (Charlottesville, Va.), 2018-11, Vol.20 (suppl_6), p.vi191-vi192

2018

Volltextzugriff (PDF)

NIMG-44. QUANTITATIVE MULTI-PARAMETRIC IMAGE PROFILING REVEALS REMARKABLE HETEROGENEITY WITHIN IDH-WILDTYPE GLIOBLASTOMA, OFFERING PROGNOSTIC STRATIFICATION BEYOND CURRENT WHO CLASSIFICATIONS

Neuro-oncology (Charlottesville, Va.), 2018-11, Vol.20 (suppl_6), p.vi186-vi186

2018

Volltextzugriff (PDF)

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, p.144-155

Volltextzugriff (PDF)

Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2016-10, p.184-194

2016

Volltextzugriff (PDF)

Memory, executive, and multidomain subtle cognitive impairment: clinical and biomarker findings

Neurology, 2015-07, Vol.85 (2), p.144-153

2015

Volltextzugriff (PDF)

NIMG-05IDENTIFICATION OF IMAGING SIGNATURES OF THE EPIDERMAL GROWTH FACTOR RECEPTOR VARIANT III (EGFRvIII) IN GLIOBLASTOMA

Neuro-oncology (Charlottesville, Va.), 2015-11, Vol.17 (suppl 5), p.v154.1-v154

2015

Volltextzugriff (PDF)

1 – 25 von 49

Suche als RSS-Feed

Suchergebnisse filtern

Filter anzeigen

Nur zeigen

Geprüft (Peer-Review)
(42)
Mit Volltextzugriff
(44)

Publikationsform

Sprache

Englisch
(49)

Erscheinungsjahr

n.n

Thema

15 weitere anzeigen...

Weniger anzeigen...

Menü

Weitere Dienste

Einstellungen

Longitudinal analysis of the patient pathways to diagnosis of psoriatic arthritis

Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals

Does Gender Impact a Diagnosis of Ankylosing Spondylitis?

Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

NIMG-20. IMAGING PATTERN ANALYSIS REVEALS THREE DISTINCT PHENOTYPIC SUBTYPES OF GBM WITH DIFFERENT SURVIVAL RATES

Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning

In Vivo Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature Consistent with Deep Peritumoral Infiltration: The φ -Index

Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1

Application of machine learning in the diagnosis of axial spondyloarthritis

Histopathology‐validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo‐progression in glioblastoma

In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature

Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis

NIMG-38. QUANTITATIVE IMAGING PREDICTORS OF OVERALL SURVIVAL IN GLIOBLASTOMA PATIENTS ROBUST IN THE PRESENCE OF INTER-SCANNER VARIATIONS

MPTH-02. EXTRACELLULAR EGFR289 ACTIVATING MUTATIONS CONFER POORER SURVIVAL AND SUGGEST ENHANCED MOTILITY IN PRIMARY GBMs

Use of Fetal Magnetic Resonance Image Analysis and Machine Learning to Predict the Need for Postnatal Cerebrospinal Fluid Diversion in Fetal Ventriculomegaly

Histopathology-validated machine learning radiographic biomarker for non-invasive discrimination between true progression and pseudo-progression in glioblastoma

Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma

NIMG-70. QUANTITATIVE IMAGE ANALYSIS AND MACHINE LEARNING TECHNIQUES FOR DISTINGUISHING TRUE PROGRESSION FROM PSEUDOPROGRESSION IN PATIENTS WITH GLIOBLASTOMA

NIMG-70. QUANTITATIVE IMAGE ANALYSIS AND MACHINE LEARNING TECHNIQUES FOR DISTINGUISHING TRUE PROGRESSION FROM PSEUDOPROGRESSION IN PATIENTS WITH GLIOBLASTOMA

NIMG-44. QUANTITATIVE MULTI-PARAMETRIC IMAGE PROFILING REVEALS REMARKABLE HETEROGENEITY WITHIN IDH-WILDTYPE GLIOBLASTOMA, OFFERING PROGNOSTIC STRATIFICATION BEYOND CURRENT WHO CLASSIFICATIONS

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation

Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework

Memory, executive, and multidomain subtle cognitive impairment: clinical and biomarker findings

NIMG-05IDENTIFICATION OF IMAGING SIGNATURES OF THE EPIDERMAL GROWTH FACTOR RECEPTOR VARIANT III (EGFRvIII) IN GLIOBLASTOMA

Suchergebnisse filtern