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The Resource Imaging Biomarkers : Development and Clinical Integration

Imaging Biomarkers : Development and Clinical Integration

Label
Imaging Biomarkers : Development and Clinical Integration
Title
Imaging Biomarkers
Title remainder
Development and Clinical Integration
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Contributor
Subject
Language
eng
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Imaging Biomarkers : Development and Clinical Integration
Label
Imaging Biomarkers : Development and Clinical Integration
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4732609
Publication
Copyright
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface -- Contents -- 1: The Shift in Paradigm to Precision Medicine in Imaging: International Initiatives for the Promotion of Imaging Biomarkers -- 1.1 Background -- 1.2 Quantitative Imaging Biomarkers Alliance (QIBA) -- 1.3 European Imaging Biomarkers Alliance (EIBALL) -- 1.4 Organizational Structure and Aspects of EIBALL -- 1.5 Collaboration of EIBALL with the European Organisation for Research and Treatment of Cancer (EORTC) -- 1.6 The aims of EIBALL in collaboration with EORTC are the following: -- 1.6.1 EIBIR should provide organizational support for this concept. -- 1.7 Collaboration of EIBALL with QIBATM -- References -- 2: Introduction to the Stepwise Development of Imaging Biomarkers -- 2.1 Introduction to Imaging Biomarkers and Precision Medicine -- 2.2 Pipeline Development of Imaging Biomarkers: The Hypothesis -- 2.3 Image Acquisition and Preparation for Analysis -- 2.4 Image Analysis and Feature Extraction -- 2.4.1 Imaging Biomarker's Metrics -- 2.5 Biomarker Validations -- 2.6 Innovating with Biomarkers -- 2.7 Biobanks and Biomarkers: Big Data -- References -- 3: Defining the Biological Basis and Clinical Question (Proof of Concept) -- Looking for the Interrelationship (Proof of Mechanism) -- 3.1 Introduction -- 3.2 Imaging Biomarker of Atherosclerosis: Coronary Artery Calcification -- 3.2.1 Pathophysiological Correlate and Clinical Problem -- 3.2.2 Methodological Approaches and Distribution in Normal Cohorts -- 3.2.3 Scientific Evidence of Coronary Calcium Score as a Diagnostic and Prognostic Biomarker -- 3.2.4 Future Outlook and Developments -- 3.3 Imaging Biomarker of Myocardial Fibrosis: Late Gadolinium Enhancement MRI -- 3.3.1 Clinical Problem -- 3.3.2 Pathophysiological Correlate -- 3.3.3 Scientific Evidence and Prognostic Impact
  • 3.4 Imaging Biomarker of Tumor Angiogenesis: Assessment of Perfusion -- 3.4.1 The Clinical Problem and Pathophysiological Correlate -- 3.4.2 Perfusion Imaging as a Biomarker for Prognosis and Response -- 3.5 Imaging Biomarker of Osteoarthritis: T2 Relaxation Time of the Articular Cartilage -- 3.5.1 Clinical Background -- 3.5.2 Underlying Pathophysiology -- 3.5.3 Establishing Imaging Biomarkers of Cartilage Degeneration and Trauma -- References -- 4: Image Acquisition: Modality and Protocol Definition -- 4.1 Medical Image Modality Selection -- 4.2 Imaging Protocol Definition -- References -- 5: MRI Preprocessing -- 5.1 Introduction -- 5.2 Denoising -- 5.3 Inhomogeneity Correction -- 5.4 Superresolution -- 5.5 Registration -- 5.6 Intensity Standardization -- 5.7 Preprocessing Pipeline -- References -- 6: Imaging Biomarker Structural Analysis -- 6.1 Introduction -- 6.2 Morphology and Volumetry Biomarkers -- 6.3 Irregularity Biomarkers -- 6.4 Texture Biomarkers -- References -- 7: Imaging Biomarker Model-Based Analysis -- 7.1 Introduction -- 7.2 Diffusion-Weighted Imaging (DWI) MRI -- 7.2.1 DWI Modeling -- 7.2.1.1 Gaussian Mono-exponential -- 7.2.1.2 Gaussian Bi-exponential -- 7.2.1.3 Non-Gaussian Mono-exponential -- 7.2.1.4 Non-Gaussian Bi-exponential (IVIM-Kurtosis) -- 7.2.1.5 Stretched Exponential Model -- 7.2.2 DWI Analysis -- 7.2.2.1 Data Fitting -- Complete Fitting -- Partial Fitting -- 7.2.2.2 Evaluation of the DWI Models and Comparative Studies -- 7.2.2.3 Qualitative and Quantitative Data Presentation -- 7.3 Dynamic Contrast-Enhanced (DCE)-MRI -- 7.3.1 Reliability of the Pharmacokinetic (PK) Biomarkers -- 7.3.2 Estimation of Contrast Agent Concentration -- 7.3.3 Arterial Input Function (AIF) -- 7.3.4 Quantitative Models -- 7.3.4.1 Tofts Model (TM) and Extended Tofts Model (ETM)
  • 7.3.4.2 Adiabatic Tissue Homogeneity (ATH) Model -- 7.3.4.3 Two-Compartment Exchange Model (2CXM) -- 7.3.5 Model-Free Analysis -- References -- 8: Imaging Biomarker Measurements -- 8.1 Introduction -- 8.2 Size Measurements -- 8.3 Lesion Segmentation -- 8.4 Shape-Based Measurements -- 8.5 Intensity and Texture Analyses -- 8.5.1 Analysis Based on the Distribution of Signal Intensity -- 8.5.2 Analysis Based on the Organization of Signal Intensity in the Image Domain -- 8.5.3 Analysis Based on the Organization of Geometric Pattern in the Image Domain -- 8.5.4 Texture Analysis in the Frequency Domain -- 8.6 Data Reduction -- 8.7 Data Classification -- 8.8 Radiomics -- 8.9 Limitations of Radiomics -- References -- 9: Detecting Measurement Biases: Sources of Uncertainty, Accuracy, and Precision of the Measurements -- 9.1 Introduction -- 9.2 What Makes a Biomarker Accurate and Precise? -- 9.3 How Reliable Are the Measures Provided by Biomarkers? -- 9.3.1 Internal Consistency -- 9.3.2 Observer Variation -- 9.3.3 Test-Retest Reliability -- 9.3.4 Method Comparison -- 9.4 How Well Do Biomarkers Represent the Constructs Being Measured? -- 9.4.1 Content Validity -- 9.4.2 Construct Validity -- 9.4.3 Criterion Validity -- 9.5 How Accurately Can Biomarkers Detect Small Clinical Effects? -- 9.5.1 Internal Responsiveness -- 9.5.2 External Responsiveness -- References -- 10: Validating the Imaging Biomarker: The Proof of Efficacy and Effectiveness -- 10.1 Introduction -- 10.2 Road for Biomarker Development -- 10.3 Proof of Efficacy and Effectiveness Through Analytical Validation -- 10.3.1 Standardization: A Prerequisite Before Validation -- 10.3.2 Bias and Linearity -- 10.3.3 Reproducibility -- 10.3.4 Repeatability -- References -- 11: The Final Step: Imaging Biomarkers in Structured Reports -- 11.1 Introduction
  • 11.2 Data Mining on an Unstructured Radiology Report -- 11.3 Structured Report -- 11.4 Imaging Biomarkers in Reports -- References -- 12: Pearls and Pitfalls in Gold Standards and Biological Correlation -- 12.1 Introduction -- 12.2 Surrogacy and Biological Plausibility -- 12.2.1 MR Elastography -- 12.2.2 CT Colonography -- 12.2.3 CT Perfusion Metrics -- 12.3 Selecting a Suitable Reference Standard for Validation -- 12.3.1 Liver MRE -- 12.3.2 CT Colonography -- 12.3.3 CT Perfusion Metrics -- 12.4 Imperfect Standards: Effects -- 12.4.1 Liver MRE and Histopathology -- 12.4.2 CT Colonography and Optical Colonoscopy -- 12.4.3 CT Perfusion Metrics -- 12.5 Managing Imperfect Standards -- 12.5.1 Liver MRE and Histopathology -- 12.5.2 CT Colonography and Optical Colonoscopy -- 12.5.3 CT Perfusion Metrics -- 12.6 Biomarker Qualification -- 12.6.1 Liver MRE -- 12.6.2 CT Colonography -- 12.6.3 CT Perfusion Metrics -- 12.7 Pearls to Help Avoid Pitfalls -- References -- 13: Imaging Biobanks, Big Data, and Population-Based Imaging Biomarkers -- 13.1 Introduction -- 13.2 Radiomics and Personalized Care -- 13.3 Imaging Biobanks: Current Status -- 13.4 Imaging Data Standardization -- 13.5 Ethical Issues -- References -- 14: A Proposed Imaging Biomarkers Analysis Platform Architecture for Integration in Clinics -- 14.1 Introduction -- 14.2 Current Solutions -- 14.2.1 Image Processing Workstations -- 14.2.2 Server-Client Platforms -- 14.2.3 Service-Oriented Solutions -- 14.2.4 Modality-Embedded Solutions -- 14.2.5 Cloud-Based Solutions -- 14.3 The Requirements -- 14.3.1 Modular -- 14.3.2 Integrated -- 14.3.3 Scalable -- 14.3.4 Pipelines -- 14.3.5 Data Mining -- 14.3.6 Web Based -- 14.3.7 Vendor Agnostic -- 14.3.8 Marketplace Strategy -- 14.3.9 Structured Reporting Generation -- 14.4 A Proposed Architecture -- References
  • 15: Use Case I: Imaging Biomarkers in Neurological Disease. Focus on Multiple Sclerosis -- 15.1 Introduction -- 15.2 Imaging Biomarkers Relevant to MS -- 15.2.1 Background -- 15.2.2 Natural Course of the Disease -- 15.2.3 Treatment -- 15.3 Acquisition Requirements -- 15.4 Analysis Methods -- 15.4.1 Cross-Sectional Biomarkers -- 15.4.1.1 Brain Volume Computations -- 15.4.1.2 Lesion Detection and Volume Estimation -- 15.4.2 Longitudinal Biomarkers -- 15.5 How to Transmit the Information to the Clinician -- References -- 16: Use Case II: Imaging Biomarkers and New Trends for Integrated Glioblastoma Management -- 16.1 Introduction -- 16.2 The Standard Clinical Workflow -- 16.3 Main Questions in Glioblastoma Management -- 16.3.1 Presurgery Decision -- 16.3.2 Post-surgery Decision -- 16.3.3 Pre-radiotherapy Decision -- 16.3.4 Follow-Up Decisions -- 16.4 Imaging Biomarkers in Glioblastoma Management -- 16.5 New Trends for Integrated GB Management -- 16.5.1 GB Molecular Subtypes -- 16.5.2 Key Enabling Molecular Biomarkers in the Clinical Practice -- 16.5.3 Advanced Multiscale Data Modelling in GB: In Silico Oncology Models -- 16.6 Including Key Enabling Technologies in Clinical Practice -- 16.6.1 Integrating the DSS into Clinical Workflows and Computer Systems -- 16.6.2 The Acceptability by the End Users -- References -- 17: Use Case III: Imaging Biomarkers in Breast Tumours. Development and Clinical Integration -- 17.1 Introduction -- 17.2 Molecular Biomarkers of Breast Cancer -- 17.3 Quantitative Breast Imaging Biomarkers -- 17.3.1 Risk Prediction for the Development of Breast Cancer -- 17.3.1.1 Breast Density on Mammography -- 17.3.1.2 Amount of Fibroglandular Tissue and Background Parenchymal Enhancement on Magnetic Resonance Imaging
  • 17.3.2 Tumor Characterization and Prognosis for the Hallmarks of Breast Cancer with Magnetic Resonance Imaging (MRI)
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1 online resource (313 pages)
Form of item
online
Isbn
9783319435046
Media category
computer
Media MARC source
rdamedia
Media type code
c
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remote

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