E-Book, Englisch, 590 Seiten
Foundations, Modeling, and Applications with R-Based Examples
E-Book, Englisch, 590 Seiten
Reihe: Imaging in Medical Diagnosis and Therapy
ISBN: 978-1-351-23071-1
Verlag: Taylor & Francis
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
– from the Foreword by Prof. Harold L. Kundel, Department of Radiology, Perelman School of Medicine, University of Pennsylvania
"This book will benefit individuals interested in observer performance evaluations in diagnostic medical imaging and provide additional insights to those that have worked in the field for many years."
– Prof. Gary T. Barnes, Department of Radiology, University of Alabama at Birmingham
This book provides a complete introductory overview of this growing field and its applications in medical imaging, utilizing worked examples and exercises to demystify statistics for readers of any background. It includes a tutorial on the use of the open source, widely used R software, as well as basic statistical background, before addressing localization tasks common in medical imaging. The coverage includes a discussion of study design basics and the use of the techniques in imaging system optimization, memory effects in clinical interpretations, predictions of clinical task performance, alternatives to ROC analysis, and non-medical applications.
Dev P. Chakraborty, PhD, is a clinical diagnostic imaging physicist, certified by the American Board of Radiology in Diagnostic Radiological Physics and Medical Nuclear Physics. He has held faculty positions at the University of Alabama at Birmingham, University of Pennsylvania, and most recently at the University of Pittsburgh.
Autoren/Hrsg.
Fachgebiete
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Bildgebende Verfahren, Nuklearmedizin, Strahlentherapie Nuklearmedizin, PET, Radiotherapie
- Naturwissenschaften Physik Physik Allgemein
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Bildgebende Verfahren, Nuklearmedizin, Strahlentherapie Radiologie, Bildgebende Verfahren
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
Weitere Infos & Material
Preliminaries
Introduction
Clinical tasks
Imaging device development and its clinical deployment
Image quality vs. task performance
Why physical measures of image quality are not enough
Model observers
Measuring observer performance: four paradigms
Hierarchy of assessment methods
Overview of the book and how to use it
PART I
The binary paradigm
Introduction
Decision vs. truth: the fundamental 2x2 table of ROC analysis
Sensitivity and specificity
Reasons for the names sensitivity and specificity
Estimating sensitivity and specificity
Disease prevalence
Accuracy
Positive and negative predictive values
Example: calculation of PPV and NPV
PPV and NPV are irrelevant to laboratory tasks
Modeling the binary task
Introduction
Decision variable and decision threshold
Changing the decision threshold: Example I
Changing the decision threshold: Example II
The equal-variance binormal model
The normal distribution
Demonstration of the concepts of sensitivity and specificity
Inverse variation of sensitivity and specificity
The ROC curve
Assigning confidence intervals to an operating point
Variability in sensitivity and specificity: the Beam et al study
The ratings paradigm
Introduction
The ROC counts table
Operating points from counts table
Relation between ratings paradigm and the binary task
Ratings are not numerical values
A single "clinical" operating point from ratings data
The forced choice paradigm
Observer performance studies as laboratory simulations of clinical tasks
Discrete vs. continuous ratings: the Miller study
The BIRADS ratings scale and ROC studies
The controversy
Empirical AUC
Introduction
The empirical ROC plot
Empirical operating points from ratings data
AUC under the empirical ROC plot
The Wilcoxon statistic
Bamber’s theorem
The importance of Bamber’s theorem
Appendix 5.A: Details of Wilcoxon theorem
Binormal model
Introduction
The binormal model
Least-squares estimation
Maximum likelihood estimation (MLE)
Expression for area under ROC curve
Appendix 6.A: Expressions for partial and full area under the binormal ROC
Sources of variability affecting AUC
Introduction
Three sources of variability
Dependence of AUC on the case sample
Estimating case-sampling variability using the DeLong method
Estimating case-sampling variability of AUC using the bootstrap method
Estimating case-sampling variability of AUC using the jackknife method
Estimating case-sampling variability of AUC using a calibrated simulator
Dependence of AUC on the reader’s expertise
Dependence of AUC on the modality
Effect on empirical AUC of variations in thresholds and numbers of bins
Empirical vs. fitted AUCs
PART II
Hypothesis Testing
Introduction
Hypothesis testing for a single-modality single-reader ROC study
Type-I errors
One-sided vs. two sided tests
Statistical power
Some comments on the code
Why is alpha chosen to be 5%
Dorfman-Berbaum-Metz-Hillis (DBMH) analysis
Co-author: Xuetong Zhai, MS
Introduction
Random and fixed factors
Reader and case populations and data correlations
Three types of analyses
General approach
The Dorfman-Berbaum-Metz (DBM) method
Random-reader random-case (RRRC) analysis
Fixed-reader random-case (FRRC) analysis
Random-reader fixed-case (RRFC) analysis
DBMH Analysis: Example 1
DBMH Analysis: Example 2
Validation of DBMH analysis
Meaning of pseudovalues
Obuchowski-Rockette-Hillis (ORH) analysis
Co-author: Xuetong Zhai, MS
Introduction
The single reader multiple treatment model
The multiple reader multiple treatment model ORH model
Special cases: fixed-reader and fixed-case analyses
Example of ORH analysis
Comparison of ORH and DBMH methods
Sample size estimation for ROC studies
Introduction
Statistical power
Sample size estimation
Dependence of statistical power on estimates of model parameters
Formulae for random-reader random-case (RRRC) sample size estimation
Formulae for fixed-reader random-case (FRRC) sample size estimation
Formulae for random-reader fixed-case (RRFC) sample size estimation
Example 1
Example 2
Details of the sample size estimation process
Cautionary notes: the Kraemer et al paper
Prediction accuracy of sample size estimation method
On the unit of effect-size: a proposal
PART III
The FROC paradigm
Introduction
Location specific paradigms
The FROC paradigm as a search task
A pioneering FROC study in medical imaging
Population and binned FROC plots
The "solar" analogy: search vs. classification performance
Empirical operating characteristics derivable from FROC data
Introduction
Latent vs. actual marks
Formalism: the FROC plot
Formalism: the alternative FROC (AFROC) plot
The EFROC plot
Formalism: the inferred ROC plot
Formalism: the weighted-AFROC (wAFROC) plot
Formalism: the AFROC1 plot
Formalism: the weighted-AFROC1 (wAFROC1) plot
Example: "raw" FROC plots
Confusion about location-level "true-negatives"
Example: binned FROC plots
Example: "raw" FROC / AFROC plots
Example: binned AFROC plots
Example: binned FROC/AFROC/ROC plots
Recommendations
Computation and meanings of empirical FROC FOM-statistics and AUC measures
Introduction
Empirical AFROC FOM-statistic
Empirical weighted-AFROC FOM-statistic
Two Theorems
Understanding the AFROC and wAFROC empirical plots
Physical interpretation of AFROC-based FOMs
Visual Search Paradigms
Introduction
Grouping and labeling ROIs in an image
Recognition/Finding vs. detection
Two visual search paradigms
Determining where the radiologist is looking
The Nodine - Kundel search model
Analyzing simultaneously acquired eye-tracking & FROC data
The radiological search model (RSM)
Introduction
The radiological search model (RSM)
Physical interpretation of RSM parameters
Model re-parameterization
Equation Chapter 16 Section 1
Predictions of the RSM
Introduction
Inferred integer ROC ratings
Constrained end-point property of the RSM-predicted ROC curve
The RSM-predicted ROC curve
Example: RSM-predicted ROC/pdf curves
The RSM-predicted FROC curve
The RSM-predicted AFROC curve
Quantifying search performance
Quantifying lesion-classification performance
The FROC curve is a poor descriptor of search performance
Evidence for the RSM
Fitting RSM to FROC/ROC data and key findings
Introduction
FROC Likelihood function
IDCA Likelihood function
ROC Likelihood function
RSM vs. PROPROC and CBM, and a serendipitous finding
Reason for serendipitous finding
Analyzing FROC data and sample size estimation
Introduction
Example analysis of a FROC dataset
Plotting wAFROC curves
Single fixed-factor Analysis
Crossed treatment analysis
Sample size estimation: wAFROC FOM
PART IV
Proper ROC models
Introduction
Theorem: slope of ROC equals likelihood ratio
Theorem: likelihood ratio observer maximizes AUC
Proper vs. improper ROC curves
Degenerate datasets
The likelihood ratio observer
PROPROC formalism
The contaminated binormal model
The bigamma model
The bivariate binormal model and its software implementation (CORROC2)
Introduction
The bivariate binormal model
The multivariate probability density function
Visualizing the bivariate probability density function
Estimating bivariate binormal model parameters
CORROC2 software
Application to a real dataset
Comparing performance of standalone CAD to a group of radiologists interpreting the same cases
Introduction
The Hupse-Karssemeijer et al. study
Extending the analysis to random cases
Ambiguity in interpreting a point-based FOM
Results using full-area measures
Design and calibration of a single-modality multiple-reader decision variable simulator and using it to validate the proposed CAD analysis method
Co-author: Xuetong Zhai, MS
Introduction
Bivariate contaminated binormal model (BCBM)
Single-modality multiple-reader decision variable simulator
Calibration, validation of simulator and testing its NH behavior
