Hur utvärderar man klinisk bildkvalitet med statistiska metoder?

Size: px
Start display at page:

Download "Hur utvärderar man klinisk bildkvalitet med statistiska metoder?"

Transcription

1 Hur utvärderar man klinisk bildkvalitet med statistiska metoder? SK-kurs, Medicinsk strålningsfysik, 8 okt 2013 Sammanhang Val av behandling Hälsoeffekt Behandlingseffekt Undersökning Örjan Smedby Radiologi IMH/CMIV Linköpings universitet Efficacy of Diagnostic Methods Level 1: Technical efficacy Technical, resolution, noise... Level 2: Diagnostic accuracy efficacy Hur ofta blir diagnosen rätt? Level 3: Diagnostic thinking efficacy Hur påverkas remittentens diagnostiska tänkande? Level 4: Therapeutic efficacy Hur påverkas valet av behandling? Level 5: Patient outcome efficacy Hur påverkas patientens hälsa? Level 6: Societal efficacy ytta och kostnader för samhället (Fryback DG, Thornbury JR. Med Decis Making 1991) Image vs. diagnostic accuracy entire diagnostic process Reliable ground truth RC study physical parameters Physical measuring tools Classical statistical tools Receiver operating characteristics Generalization of sensitivity and specificity How is sens. and spec. affected as threshold is changed? Ett diagnostiskt test Pos test eg test Summa Sjuk 25! 5! 30 Frisk 15! 105! 120 Summa Hur stor är chansen att en sjuk klassificeras rätt? Sensitivitet 25/30 = 83%

2 Ett diagnostiskt test Pos test eg test Summa Sjuk 25! 5! 30 Frisk 15! 105! 120 Summa Hur stor är chansen att en frisk klassificeras rätt? Specificitet 105/120 = 88% Ett diagnostiskt test Pos test eg test Summa Sjuk 25! 5! 30 Frisk 15! 105! 120 Summa Hur stor är sannolikheten att en pat med pos test verkligen är sjuk? Positivt prediktionsvärde 25/40 = 63% Ett diagnostiskt test Pos test eg test Summa Sjuk 25! 5! 30 Frisk 15! 105! 120 Summa Hur stor är sannolikheten att en pat med neg test verkligen är frisk? egativt prediktionsvärde 105/110 = 95% Tröskelnivå Högre gräns för patologi: - sensitiviteten sjunker - specificiteten ökar Lägre gräns för patologi: - sensitiviteten ökar - specificiteten sjunker sensitivitet specificitet RC-curve Receiver operating characteristics sensitivity Area under RC curve (AURC): 1 perfect 0.5 worthless 1 specificity Generalization of sensitivity and specificity How is sens. and spec. affected as threshold is changed? Requires gold standard Requires large material Much work, large costs

3 Image vs. diagnostic accuracy Image vs. diagnostic accuracy entire diagnostic process physical parameters entire diagnostic process Visual image concept physical parameters Reliable ground truth RC study Physical measuring tools Classical statistical tools Reliable ground truth RC study Visual grading experiment? Physical measuring tools Classical statistical tools Single images Rate image A on a scale from 1 to 5 Study types Image pairs Rate the difference between image A and B on a scale from 2 to +2 Typical: visibility of an anatomical structure Visually sharp reproduction of the thoracic aorta 1. Criterion is fulfilled 2. Criterion is probably fulfilled 3. Indecisive Criteria & rating scale 4. Criterion is probably not fulfilled 5. Criterion is not fulfilled Situation Types of data Patient P1 P2 P3 P4... Im1 Im2 Postprocessing PP1 PP2 PP3 bserver score Interval: numerical, continuous rdinal: ordered categories ominal: individual categories, no order I Measurement Rating score Persons A B C D

4 Visual grading characteristics (VGC) (Båth & Månsson BJR 2007) För varje kvalitetsnivå: Hur stor andel uppfyller kravet med metod A resp. metod B? Metod A Metod B Figure 2. The visual grading characteristic (VGC) curve from the data presented in Tables 1 and 2. The boxes represent the operating points corresponding to the observer s interpretation of the scale steps of the rating scale. Patient 5 Discussion Statistical model system I Settings IC is a visual grading method for which valid statistical methods have been used most often previously. The dissatisfaction from the fact that the observer can only use processing a two-step rating scale in IC (criterion fulfilled/criterion not fulfilled) often leads to the use of VGA, enabling the use of multiple scale steps, although invalid statistical methods are often used. The use of VGC analysis can hopefully satisfy the needs for both a valid statistical bserver method and freedom for the observer. Furthermore, VGC analysis can be used directly on the image criteria defined by the European Commission giving statements of the needed levels of reproduction for certain anatomical landmarks without the need for extracting the relevant structures from the criteria and grading the visibility of these structures. This has the potential of leading to an increased validity in the use of the image Örjan criteria in Smedby, multiple-choice Linköping Univ. / Radiology (IMH) grading studies. However, VGC analysis is not limited to the use of European criteria. Modifications of the original criteria have been proposed for chest radiography [16], lumbar spine radiography [33] and mammography [23, 34] and these modified criteria as well as other relevant criteria may meritoriously be used. Furthermore, the grading task is not limited to normal anatomy. If applicable, grading of image criteria based on pathology may also be used. Postprocessing bserver VGC analysis consists of elements from both IC and relative and absolute VGA as well as from RC analysis. The concept of VGC analysis can be interpreted as IC meets RC with the VGC curve presenting the ICS B (the proportion of images rated as fulfilling a criterion for modality B) as a function of the ICS A (the proportion of images rated as fulfilling a criterion for modality A) for a grading task, just like the RC curve describes the TPF (the proportion of images rated as containing a signal for the positive images) as a function of the FPF (the proportion of images rated as containing a signal for the negative images) for a detection task. (ne important difference between the two curves being that the RC curve describes an observer s ability to separate the signal and noise distributions belonging to one modality from each other, whereas the VGC curve describes the observer s opinion about the separation of the image distributions from two modalities.) For the observer, the resulting study is similar to absolute VGA with the use of a multistep scale for grading the image. The resulting measure of image, AUC VGC, is finally, like in relative VGA, a relative measure of image, describing the image for modality B in comparison with modality A. Using the statistical methods of RC analysis, VGC analysis presents a solution to the need of nonparametric rank-invariant statistical methods for analysing the data from visual grading studies. The use of the RC technique for comparing data from studies other than detection tasks has been proposed previously. Sonn and Svensson [25] studied changes in activities of daily living (ADL) measured by a 10-level scale, the Staircase of ADL, in rehabilitation medicine and used the RC curve to analyse the M Båth and L G Månsson Im1 Im2 Im3 systematic change in ADL levels between two age groups. The use of the RC technique, enabling a statistically valid analysis of data, can probably be applied to many other rating10 tasks Strengths of VGC system I Settings Post-? Weaknesses of VGC The value of the AUC VGC can be criticized for the same reason as the Az can be questioned in RC analysis. The index A z is useful in most cases because it reflects accuracy in general through a range of possible operating points [35]. However, doubts have been expressed by some investigators concerning the fact that a large part of the area comes from the rightmost part of the curve and thereby include false positive fractions of limited or no clinical relevance. Also, crossing curves can cause confusion; one curve may have higher TPFs than another in the region of relevant FPFs, but if the curves cross for higher FPF values, the superiority for the first curve may be lost or even reversed if the area under each curve is used as an index of accuracy [27, 36]. In the same way a large part of the area of the VGC curve comes from a part of the curve which corresponds to a very low threshold of the observer for judging a criterion of being fulfilled possibly corresponding to an unacceptable image. The VGC curve It is important to realise that a VGC curve is completely determined by the two underlying distributions of the modalities being studied (in the same way as 174 The British Journal of Radiology, March 2007 regression score Statistical model Patient Logistic regression score Logit function logit (p) = log (p/(1 p)) Regression equation logit (p) = ax + b p = 1/(1 + exp(ax + b)) rdinal regression Statistical model Logit function logit (p) = log (p/(1 p)) Patient Regression equation logit (p) = ax + b p = 1/(1 + exp(ax + b)) VGR model logit (P(y n)) = a 1 Im1 +a 2 Im2 + b 1 PP1 +b 2 PP2 +b 3 PP3 + D P +E C n Im1 Im2 Im3 PP1 PP2 system I Settings Postprocessing bserver regression score (Smedby & Fredrikson, British Journal of Radiology 2010)!

5 random effect Im1 Im2 Im3 fixed effect PP1 PP2 fixed effect Statistical model Patient system I Settings Postprocessing regression score Empirical data (Jakob De Geer) Coronary CTA 24 patients (P1 P24) Standard (310 mas Ref) and reduced dose (62 mas Ref) Reduced-dose images post-processed with 2D adaptive filter (Sharpview) Filtered and unfiltered reduced-dose images viewed by 9 radiologists (R1 R9) bserver random effect Criteria Criterion 1: Visually sharp reproduction of the thoracic aorta. Criterion 2: Visually sharp reproduction of the wall of the thoracic aorta. Criterion 3: Visually sharp reproduction of the heart. Criterion 4: Visually sharp reproduction of the left main coronary artery (LMA). Criterion 5: The image noise in relevant regions is sufficiently low for diagnosis. Rating scale 1.Criterion is fulfilled 2.Criterion is probably fulfilled 3.Indecisive 4.Criterion is probably not fulfilled 5.Criterion is not fulfilled Statistical model Results: filter effect Patient Postprocessing bserver unfiltered filterered regression (GLLAMM) score Criterion 1: Visually sharp reproduction of the thoracic aorta 2: Visually sharp reproduction of the aortic wall rdinal regression regression coefficient p value < : Visually sharp reproduction of the heart : Visually sharp reproduction of the LMA : oise sufficiently low for diagnosis 0.96 <

6 Including mas effect Both standard-dose and reduced-dose images were viewed, reduced-dose images with and without filtering Postprocessing unfiltered filterered Statistical model with mas Patient I log mas setting bserver regression score Statistical model with mas etc. Dose reduction I Weight 1.0 Criterion Postprocessing unfiltered filterered Patient I log mas setting bserver Education regression (GLLAMM) score Probability of a score of 1 or mas Ref setting Unfiltered Filtered Results with mas Regression coefficients Criterion log (mas) adaptive filter 1: Visually sharp reproduction of the thoracic aorta : Visually sharp reproduction of the aortic wall : Visually sharp reproduction of the heart : Visually sharp reproduction of the LMA : oise sufficiently low for diagnosis Results with mas Regression coefficients Estimated Criterion log (mas) adaptive filter mas reduction 1: Visually sharp reproduction of the thoracic aorta % 2: Visually sharp reproduction of the aortic wall % 3: Visually sharp reproduction of the heart % 4: Visually sharp reproduction of the LMA % 5: oise sufficiently low for diagnosis %

7 Criterion 1: Visually sharp reproduction of the thoracic aorta 2: Visually sharp reproduction of the aortic wall 3: Visually sharp reproduction of the heart 4: Visually sharp reproduction of the LMA 5: oise sufficiently low for diagnosis Results with mas Regression coefficients (95% confidence limits) Estimated adaptive mas log (mas) filter reduction 2.52 ( 2.88; 2.16) 2.53 ( 2.82; 2.24) 2.54 ( 2.91; 2.18) 2.52 ( 2.81; 2.24) 2.74 ( 3.04; 2.44) 0.45 ( 0.78; 0.11) 0.75 ( 1.07; 0.44) 0.74 ( 1.12; 0.36) 0.61 ( 0.91; 0.30) 0.77 ( 1.07; 0.46) 16% (6%; 27%) 26% (17%; 34%) 25% (15%; 36%) 21% (13%; 30%) 24% (17%; 32%) For analyzing diagnostic accuracy, RC studies are superior, but costly and cumbersome. Visual grading experiments describe visual image. Simple comparisons can be made with VGC. rdinal regression (VGR) makes it possible to obtain direct numeric estimates of the potential for dose reduction. Particularly useful when testing and optimising acquisition/post-processing protocols. Conclusion

Methodologies for Evaluation of Standalone CAD System Performance

Methodologies for Evaluation of Standalone CAD System Performance Methodologies for Evaluation of Standalone CAD System Performance DB DSFM DCMS OSEL DESE DP DIAM Berkman Sahiner, PhD USFDA/CDRH/OSEL/DIAM AAPM CAD Subcommittee in Diagnostic Imaging CAD: CADe and CADx

More information

11. Analysis of Case-control Studies Logistic Regression

11. Analysis of Case-control Studies Logistic Regression Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:

More information

X X X a) perfect linear correlation b) no correlation c) positive correlation (r = 1) (r = 0) (0 < r < 1)

X X X a) perfect linear correlation b) no correlation c) positive correlation (r = 1) (r = 0) (0 < r < 1) CORRELATION AND REGRESSION / 47 CHAPTER EIGHT CORRELATION AND REGRESSION Correlation and regression are statistical methods that are commonly used in the medical literature to compare two or more variables.

More information

Health Care and Life Sciences

Health Care and Life Sciences Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS Implementations Wen Zhu 1, Nancy Zeng 2, Ning Wang 2 1 K&L consulting services, Inc, Fort Washington,

More information

FFR CT : Clinical studies

FFR CT : Clinical studies FFR CT : Clinical studies Bjarne Nørgaard Department Cardiology B Aarhus University Hospital Skejby, Denmark Disclosures: Research grants: Edwards and Siemens Coronary CTA: High diagnostic sensitivity

More information

Computed Tomography, Head Or Brain; Without Contrast Material, Followed By Contrast Material(S) And Further Sections

Computed Tomography, Head Or Brain; Without Contrast Material, Followed By Contrast Material(S) And Further Sections 1199SEIU BENEFIT AND PENSION FUNDS High Tech Diagnostic Radiology and s # 1 70336 Magnetic Resonance (Eg, Proton) Imaging, Temporomandibular Joint(S) 2 70450 Computed Tomography, Head Or Brain; Without

More information

University of Michigan Dearborn Graduate Psychology Assessment Program

University of Michigan Dearborn Graduate Psychology Assessment Program University of Michigan Dearborn Graduate Psychology Assessment Program Graduate Clinical Health Psychology Program Goals 1 Psychotherapy Skills Acquisition: To train students in the skills and knowledge

More information

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not. Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C

More information

P080003/S001 Hologic Selenia Dimensions C-View Software Module. Glossary of Terms

P080003/S001 Hologic Selenia Dimensions C-View Software Module. Glossary of Terms Glossary of Terms 2D plus 3D images a set of images that allow radiology is compare the results of a standard 2D mammogram image and the corresponding 3D tomosynthesis image, while viewing them independently

More information

CPT Radiology Codes Requiring Review by AIM Effective 01/01/2016

CPT Radiology Codes Requiring Review by AIM Effective 01/01/2016 CPT Radiology Codes Requiring Review by AIM Effective 01/01/2016 When a service is authorized only one test per group is payable. *Secondary codes or add-on codes do not require preauthorization or separate

More information

For the NXT Investigators

For the NXT Investigators Diagnostic performance of non-invasive fractional flow reserve derived from coronary CT angiography in suspected coronary artery disease: The NXT trial Bjarne L. Nørgaard, Jonathon Leipsic, Sara Gaur,

More information

Elisabeth Svensson Örebro University Sweden

Elisabeth Svensson Örebro University Sweden TEACHING STATISTICIANS AND APPLIED RESEARCHERS STATISTICAL METHODS FOR ANALYSIS OF DATA FROM RATING SCALES. EXPERIENCES FROM JOINT RESEARCH COURSES IN RATING SCALE DATA ANALYSIS Elisabeth Svensson Örebro

More information

THE RISK DISTRIBUTION CURVE AND ITS DERIVATIVES. Ralph Stern Cardiovascular Medicine University of Michigan Ann Arbor, Michigan. stern@umich.

THE RISK DISTRIBUTION CURVE AND ITS DERIVATIVES. Ralph Stern Cardiovascular Medicine University of Michigan Ann Arbor, Michigan. stern@umich. THE RISK DISTRIBUTION CURVE AND ITS DERIVATIVES Ralph Stern Cardiovascular Medicine University of Michigan Ann Arbor, Michigan stern@umich.edu ABSTRACT Risk stratification is most directly and informatively

More information

Validation of measurement procedures

Validation of measurement procedures Validation of measurement procedures R. Haeckel and I.Püntmann Zentralkrankenhaus Bremen The new ISO standard 15189 which has already been accepted by most nations will soon become the basis for accreditation

More information

8 General discussion. Chapter 8: general discussion 95

8 General discussion. Chapter 8: general discussion 95 8 General discussion The major pathologic events characterizing temporomandibular joint osteoarthritis include synovitis and internal derangements, giving rise to pain and restricted mobility of the temporomandibular

More information

Strategies for Identifying Students at Risk for USMLE Step 1 Failure

Strategies for Identifying Students at Risk for USMLE Step 1 Failure Vol. 42, No. 2 105 Medical Student Education Strategies for Identifying Students at Risk for USMLE Step 1 Failure Jira Coumarbatch, MD; Leah Robinson, EdS; Ronald Thomas, PhD; Patrick D. Bridge, PhD Background

More information

Evaluation of Diagnostic Tests

Evaluation of Diagnostic Tests Biostatistics for Health Care Researchers: A Short Course Evaluation of Diagnostic Tests Presented ed by: Siu L. Hui, Ph.D. Department of Medicine, Division of Biostatistics Indiana University School of

More information

Local classification and local likelihoods

Local classification and local likelihoods Local classification and local likelihoods November 18 k-nearest neighbors The idea of local regression can be extended to classification as well The simplest way of doing so is called nearest neighbor

More information

CT an Important Diagnostic Tool for ED Patient Management

CT an Important Diagnostic Tool for ED Patient Management CT an Important Diagnostic Tool for ED Patient Management Dr. Nicolas Grenier In recent years, the volume of CT examinations conducted in Emergency Departments (ED) has dramatically increased. CT is now

More information

Charles Secolsky County College of Morris. Sathasivam 'Kris' Krishnan The Richard Stockton College of New Jersey

Charles Secolsky County College of Morris. Sathasivam 'Kris' Krishnan The Richard Stockton College of New Jersey Using logistic regression for validating or invalidating initial statewide cut-off scores on basic skills placement tests at the community college level Abstract Charles Secolsky County College of Morris

More information

Radiologic Science Degree Completion Program. 2010-2011 Assessment Report

Radiologic Science Degree Completion Program. 2010-2011 Assessment Report Radiologic Science Degree Completion Program 2010-2011 Assessment Report 1 I. Introduction II. III. IV. Mission, Objectives, and Student Learning Outcomes a. Radiologic Science Degree Completion Program

More information

Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7)

Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) Overview Classes 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) 2-4 Loglinear models (8) 5-4 15-17 hrs; 5B02 Building and

More information

College Readiness LINKING STUDY

College Readiness LINKING STUDY College Readiness LINKING STUDY A Study of the Alignment of the RIT Scales of NWEA s MAP Assessments with the College Readiness Benchmarks of EXPLORE, PLAN, and ACT December 2011 (updated January 17, 2012)

More information

Physics testing of image detectors

Physics testing of image detectors Physics testing of image detectors Parameters to test Spatial resolution Contrast resolution Uniformity/geometric distortion Features and Weaknesses of Phantoms for CR/DR System Testing Dose response/signal

More information

CHAPTER 4 QUALITY ASSURANCE AND TEST VALIDATION

CHAPTER 4 QUALITY ASSURANCE AND TEST VALIDATION CHAPTER 4 QUALITY ASSURANCE AND TEST VALIDATION CINDY WEILAND AND SANDRA L. KATANICK Continued innovations in noninvasive testing equipment provide skilled sonographers and physicians with the technology

More information

Method Validation Procedure

Method Validation Procedure Method Validation Procedure Risk Assessment [2 x 2 = 4] This procedure has been examined under COSHH Guidelines, Manual Handling and VDU Regulations and has been assessed as LOW RISK if carried out as

More information

Scoring (manual, automated, automated with manual review)

Scoring (manual, automated, automated with manual review) A. Source and Extractor Author, Year Reference test PMID RefID Index test 1 Key Question(s) Index test 2 Extractor B. Study description Sampling population A Recruitment Multicenter? Enrollment method

More information

The aspect of the data that we want to describe/measure is the degree of linear relationship between and The statistic r describes/measures the degree

The aspect of the data that we want to describe/measure is the degree of linear relationship between and The statistic r describes/measures the degree PS 511: Advanced Statistics for Psychological and Behavioral Research 1 Both examine linear (straight line) relationships Correlation works with a pair of scores One score on each of two variables ( and

More information

Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.

Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics. Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing

More information

The Proportional Odds Model for Assessing Rater Agreement with Multiple Modalities

The Proportional Odds Model for Assessing Rater Agreement with Multiple Modalities The Proportional Odds Model for Assessing Rater Agreement with Multiple Modalities Elizabeth Garrett-Mayer, PhD Assistant Professor Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University 1

More information

STUDY PLAN FOR THE CERTIFICATE OF THE HIGHER SPECIALIZATION IN ( Diagnostic Radiology)

STUDY PLAN FOR THE CERTIFICATE OF THE HIGHER SPECIALIZATION IN ( Diagnostic Radiology) STUDY PLAN FOR THE CERTIFICATE OF THE HIGHER SPECIALIZATION IN ( Diagnostic Radiology) plan number :15/11/97/NT I-GENERAL RULES AND CONDITIONS: 1- This plan conforms to the regulations of granting the

More information

Biomarker Discovery and Data Visualization Tool for Ovarian Cancer Screening

Biomarker Discovery and Data Visualization Tool for Ovarian Cancer Screening , pp.169-178 http://dx.doi.org/10.14257/ijbsbt.2014.6.2.17 Biomarker Discovery and Data Visualization Tool for Ovarian Cancer Screening Ki-Seok Cheong 2,3, Hye-Jeong Song 1,3, Chan-Young Park 1,3, Jong-Dae

More information

Evaluation & Validation: Credibility: Evaluating what has been learned

Evaluation & Validation: Credibility: Evaluating what has been learned Evaluation & Validation: Credibility: Evaluating what has been learned How predictive is a learned model? How can we evaluate a model Test the model Statistical tests Considerations in evaluating a Model

More information

Despite its emphasis on credit-scoring/rating model validation,

Despite its emphasis on credit-scoring/rating model validation, RETAIL RISK MANAGEMENT Empirical Validation of Retail Always a good idea, development of a systematic, enterprise-wide method to continuously validate credit-scoring/rating models nonetheless received

More information

Unit 1 Practice Problems: Real Estate

Unit 1 Practice Problems: Real Estate Unit 1 Practice Problems: Real Estate PRACTICE PROBLEM 1: Perform a categorical analysis on the construction of the homes. Describe your findings. PRACTICE PROBLEM 2: Create a frequency distribution &

More information

Low-dose CT for Pulmonary Embolism

Low-dose CT for Pulmonary Embolism Low-dose CT for Pulmonary Embolism Gautham Gautham P. P. Reddy, Reddy, MD, MD, MPH MPH University University of of Washington Washington Introduction Introduction CT CT accounts accounts for for > 50%

More information

STATISTICA Formula Guide: Logistic Regression. Table of Contents

STATISTICA Formula Guide: Logistic Regression. Table of Contents : Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary

More information

Radiologic Technology (RAD, CLE) courses are open only to Radiologic Technology majors.

Radiologic Technology (RAD, CLE) courses are open only to Radiologic Technology majors. RADIOLOGIC TECHNOLOGY (A.A.S Degree) Director: Prof. Virginia Mishkin, M.S., R.T. (R) (M) (QM) A radiologic technologist is a skilled professional who provides a specialized health care service. This rewarding

More information

ESTIMATING POPULATION DOSES FROM MEDICAL RADIOLOGY

ESTIMATING POPULATION DOSES FROM MEDICAL RADIOLOGY ESTIMATING POPULATION DOSES FROM MEDICAL RADIOLOGY B. Wall, (Co-ordinator) 1, D. Hart 1, H. Mol 2, A. Lecluyse 2, A. Aroua 3, P. Trueb 3, J. Griebel 4, E. Nekolla 4, P. Gron 5, H. Waltenburg 5, H. Beauvais-March

More information

Choices, choices, choices... Which sequence database? Which modifications? What mass tolerance?

Choices, choices, choices... Which sequence database? Which modifications? What mass tolerance? Optimization 1 Choices, choices, choices... Which sequence database? Which modifications? What mass tolerance? Where to begin? 2 Sequence Databases Swiss-prot MSDB, NCBI nr dbest Species specific ORFS

More information

Course Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics

Course Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGraw-Hill/Irwin, 2010, ISBN: 9780077384470 [This

More information

Analytical Test Method Validation Report Template

Analytical Test Method Validation Report Template Analytical Test Method Validation Report Template 1. Purpose The purpose of this Validation Summary Report is to summarize the finding of the validation of test method Determination of, following Validation

More information

X-Mind. Instinct for perfection

X-Mind. Instinct for perfection X-Mind Instinct for perfection X-Mind tubes are located at the back of the head which gives the patient better protection because the distance between the focal spot and the skin is 50% greater than in

More information

Medical imaging monitors specification guidelines

Medical imaging monitors specification guidelines Medical imaging monitors specification guidelines Document details Contact for enquiries and proposed changes If you have any questions regarding this document or if you have a suggestion for improvements,

More information

WHERE IN THE WORLD JILL LIPOTI?

WHERE IN THE WORLD JILL LIPOTI? WHERE IN THE WORLD IS JILL LIPOTI? HELLO FROM NEW JERSEY CRCPD - National Symposium on Fusion Imaging and Multimodalities February 18-20, 2004 Kansas City, Missouri New Jersey s Requirements As They Pertain

More information

R/F. Efforts to Reduce Exposure Dose in Chest Tomosynthesis Targeting Lung Cancer Screening. 3. Utility of Chest Tomosynthesis. 1.

R/F. Efforts to Reduce Exposure Dose in Chest Tomosynthesis Targeting Lung Cancer Screening. 3. Utility of Chest Tomosynthesis. 1. R/F Efforts to Reduce Exposure Dose in Chest Tomosynthesis Targeting Lung Cancer Screening Department of Radiology, National Cancer Center Hospital East Kaoru Shimizu Ms. Kaoru Shimizu 1. Introduction

More information

Diagnostic Imaging Prior Review Code List 3 rd Quarter 2016

Diagnostic Imaging Prior Review Code List 3 rd Quarter 2016 Computerized Tomography (CT) Abdomen 6 Abdomen/Pelvis Combination 101 Service 74150 CT abdomen; w/o 74160 CT abdomen; with 74170 CT abdomen; w/o followed by 74176 Computed tomography, abdomen and pelvis;

More information

PROCEDURE DESCRIPTION RADIOLOGY STUDIES

PROCEDURE DESCRIPTION RADIOLOGY STUDIES CPT CODE PROCEDURE DESCRIPTION RADIOLOGY STUDIES CT SCANS: 70450 CT HEAD/BRAIN W/O CONTRAST 70460 CT HEAD/BRAIN W/ CONTRAST 70470 CT HEAD/BRAIN W/O & W/ CONTRAST 70480 CT ORBIT W/O CONTRAST 70481 CT ORBIT

More information

This clinical study synopsis is provided in line with Boehringer Ingelheim s Policy on Transparency and Publication of Clinical Study Data.

This clinical study synopsis is provided in line with Boehringer Ingelheim s Policy on Transparency and Publication of Clinical Study Data. abcd Clinical Study for Public Disclosure This clinical study synopsis is provided in line with s Policy on Transparency and Publication of Clinical Study Data. The synopsis which is part of the clinical

More information

The Wondrous World of fmri statistics

The Wondrous World of fmri statistics Outline The Wondrous World of fmri statistics FMRI data and Statistics course, Leiden, 11-3-2008 The General Linear Model Overview of fmri data analysis steps fmri timeseries Modeling effects of interest

More information

Radiologic Science Degree Completion Program. 2011-2012 Assessment Report

Radiologic Science Degree Completion Program. 2011-2012 Assessment Report Radiologic Science Degree Completion Program 2011-2012 Assessment Report I. Introduction II. III. Mission, Objectives, and Student Learning Outcomes a. Radiologic Science Degree Completion Program Mission

More information

Critical Appraisal of Article on Therapy

Critical Appraisal of Article on Therapy Critical Appraisal of Article on Therapy What question did the study ask? Guide Are the results Valid 1. Was the assignment of patients to treatments randomized? And was the randomization list concealed?

More information

Weight of Evidence Module

Weight of Evidence Module Formula Guide The purpose of the Weight of Evidence (WoE) module is to provide flexible tools to recode the values in continuous and categorical predictor variables into discrete categories automatically,

More information

Measures of diagnostic accuracy: basic definitions

Measures of diagnostic accuracy: basic definitions Measures of diagnostic accuracy: basic definitions Ana-Maria Šimundić Department of Molecular Diagnostics University Department of Chemistry, Sestre milosrdnice University Hospital, Zagreb, Croatia E-mail

More information

Data Mining - Evaluation of Classifiers

Data Mining - Evaluation of Classifiers Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010

More information

Basic research methods. Basic research methods. Question: BRM.2. Question: BRM.1

Basic research methods. Basic research methods. Question: BRM.2. Question: BRM.1 BRM.1 The proportion of individuals with a particular disease who die from that condition is called... BRM.2 This study design examines factors that may contribute to a condition by comparing subjects

More information

ABSTRACT INTRODUCTION

ABSTRACT INTRODUCTION Paper SP03-2009 Illustrative Logistic Regression Examples using PROC LOGISTIC: New Features in SAS/STAT 9.2 Robert G. Downer, Grand Valley State University, Allendale, MI Patrick J. Richardson, Van Andel

More information

Validity, Fairness, and Testing

Validity, Fairness, and Testing Validity, Fairness, and Testing Michael Kane Educational Testing Service Conference on Conversations on Validity Around the World Teachers College, New York March 2012 Unpublished Work Copyright 2010 by

More information

Logistic Regression for Spam Filtering

Logistic Regression for Spam Filtering Logistic Regression for Spam Filtering Nikhila Arkalgud February 14, 28 Abstract The goal of the spam filtering problem is to identify an email as a spam or not spam. One of the classic techniques used

More information

Studying Achievement

Studying Achievement Journal of Business and Economics, ISSN 2155-7950, USA November 2014, Volume 5, No. 11, pp. 2052-2056 DOI: 10.15341/jbe(2155-7950)/11.05.2014/009 Academic Star Publishing Company, 2014 http://www.academicstar.us

More information

Learning Objectives. Breast Density, Risk Assessment and Supplemental Screening Options. Breast Density. What is Breast Density??

Learning Objectives. Breast Density, Risk Assessment and Supplemental Screening Options. Breast Density. What is Breast Density?? , Risk Assessment and Supplemental Screening Options ASCLS ND Convention May 2016 Christina Tello Skjerseth, MD Sanford Health Bismarck Radiology Learning Objectives Understand what breast density is and

More information

Appropriate Use: Big Brother is Watching. Jason H. Rogers, MD Director, Interventional Cardiology UC Davis Medical Center

Appropriate Use: Big Brother is Watching. Jason H. Rogers, MD Director, Interventional Cardiology UC Davis Medical Center Appropriate Use: Big Brother is Watching Jason H. Rogers, MD Director, Interventional Cardiology UC Davis Medical Center Disclosures Consultant Boston Scientific, Medtronic, Middle Peak Medical, Millipede,

More information

Descriptive Statistics

Descriptive Statistics Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize

More information

AI CPT Codes. x x. 70336 MRI Magnetic resonance (eg, proton) imaging, temporomandibular joint(s)

AI CPT Codes. x x. 70336 MRI Magnetic resonance (eg, proton) imaging, temporomandibular joint(s) Code Category Description Auth Required Medicaid Medicare 0126T IMT Testing Common carotid intima-media thickness (IMT) study for evaluation of atherosclerotic burden or coronary heart disease risk factor

More information

Staff Doses & Practical Radiation Protection in DEXA

Staff Doses & Practical Radiation Protection in DEXA Patient Xray X Doses Staff Doses & Practical Radiation Protection in DEXA Una O ConnorO Dept. of Medical Physics & Bioengineering, St. James s s Hospital. Examination Types General XrayX Fluoroscopy /

More information

Simple Regression Theory II 2010 Samuel L. Baker

Simple Regression Theory II 2010 Samuel L. Baker SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the

More information

Picture archiving and communication systems (PACS) and guidelines on diagnostic display devices

Picture archiving and communication systems (PACS) and guidelines on diagnostic display devices IT Guidance Documents Picture archiving and communication systems (PACS) and guidelines on diagnostic display devices Second edition Board of the Faculty of Clinical Radiology The Royal College of Radiologists

More information

HEART CENTER. Touching Lives

HEART CENTER. Touching Lives HEART CENTER Touching Lives with INNOVATIVE TOOLS and an EXPERIENCED TEAM THE HEART MATTERS If you or someone you love is faced with a heart problem, you want to put your trust in experienced professionals

More information

Screening and diagnostic tests. Outline. Screening. Mikael Hartman, MD PhD Gustaf Edgren, PhD. Introduction to screening

Screening and diagnostic tests. Outline. Screening. Mikael Hartman, MD PhD Gustaf Edgren, PhD. Introduction to screening Screening and diagnostic tests Mikael Hartman, MD PhD Gustaf Edgren, PhD Outline Introduction to screening Assessing the performance of screening tests Screening programs and their evaluation Summary Screening

More information

PEER REVIEW HISTORY ARTICLE DETAILS TITLE (PROVISIONAL)

PEER REVIEW HISTORY ARTICLE DETAILS TITLE (PROVISIONAL) PEER REVIEW HISTORY BMJ Open publishes all reviews undertaken for accepted manuscripts. Reviewers are asked to complete a checklist review form (http://bmjopen.bmj.com/site/about/resources/checklist.pdf)

More information

DEVELOPMENT OF AN IMAGING SYSTEM FOR THE CHARACTERIZATION OF THE THORACIC AORTA.

DEVELOPMENT OF AN IMAGING SYSTEM FOR THE CHARACTERIZATION OF THE THORACIC AORTA. DEVELOPMENT OF AN IMAGING SYSTEM FOR THE CHARACTERIZATION OF THE THORACIC AORTA. Juan Antonio Martínez Mera Centro Singular de Investigación en Tecnoloxías da Información Universidade de Santiago de Compostela

More information

4D Magnetic Resonance Analysis. MR 4D Flow. Visualization and Quantification of Aortic Blood Flow

4D Magnetic Resonance Analysis. MR 4D Flow. Visualization and Quantification of Aortic Blood Flow 4D Magnetic Resonance Analysis MR 4D Flow Visualization and Quantification of Aortic Blood Flow 4D Magnetic Resonance Analysis Pie Medical Imaging, manufacturer of Quantitative Analysis software for cardiology

More information

Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios

Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios By: Michael Banasiak & By: Daniel Tantum, Ph.D. What Are Statistical Based Behavior Scoring Models And How Are

More information

In this chapter, you will learn improvement curve concepts and their application to cost and price analysis.

In this chapter, you will learn improvement curve concepts and their application to cost and price analysis. 7.0 - Chapter Introduction In this chapter, you will learn improvement curve concepts and their application to cost and price analysis. Basic Improvement Curve Concept. You may have learned about improvement

More information

Performance Measures in Data Mining

Performance Measures in Data Mining Performance Measures in Data Mining Common Performance Measures used in Data Mining and Machine Learning Approaches L. Richter J.M. Cejuela Department of Computer Science Technische Universität München

More information

Standard Comparison Protocols at NICU, Regional and National levels.

Standard Comparison Protocols at NICU, Regional and National levels. ANNEX 5.- Deliverable 5: Standard Comparison Protocols 2006 Standard Comparison Protocols at NICU, Regional and National levels. 1 ) Data Entry: Before data is analysed, a quality control protocol is performed.

More information

The Practice Standards for Medical Imaging and Radiation Therapy. Radiography Practice Standards

The Practice Standards for Medical Imaging and Radiation Therapy. Radiography Practice Standards The Practice Standards for Medical Imaging and Radiation Therapy Radiography Practice Standards 2015 American Society of Radiologic Technologists. All rights reserved. Reprinting all or part of this document

More information

2/28/2011. MIPPA overview and CMS requirements. CT accreditation. Today s agenda. About MIPPA. Computed Tomography

2/28/2011. MIPPA overview and CMS requirements. CT accreditation. Today s agenda. About MIPPA. Computed Tomography Today s agenda Computed Tomography Presented by: Dina Hernandez, BSRT, RT (R), CT, QM Krista Bush, RT, MBA Leonard Lucey, JD ACR Quality & Safety MIPPA overview and CMS requirements CT accreditation How

More information

Ordinal Regression. Chapter

Ordinal Regression. Chapter Ordinal Regression Chapter 4 Many variables of interest are ordinal. That is, you can rank the values, but the real distance between categories is unknown. Diseases are graded on scales from least severe

More information

Big Data, Socio- Psychological Theory, Algorithmic Text Analysis, and Predicting the Michigan Consumer Sentiment Index

Big Data, Socio- Psychological Theory, Algorithmic Text Analysis, and Predicting the Michigan Consumer Sentiment Index Big Data, Socio- Psychological Theory, Algorithmic Text Analysis, and Predicting the Michigan Consumer Sentiment Index Rickard Nyman *, Paul Ormerod Centre for the Study of Decision Making Under Uncertainty,

More information

CPT CODE PROCEDURE DESCRIPTION. CT Scans 70450 CT HEAD/BRAIN W/O CONTRAST 70460 CT HEAD/BRAIN W/ CONTRAST 70470 CT HEAD/BRAIN W/O & W/ CONTRAST

CPT CODE PROCEDURE DESCRIPTION. CT Scans 70450 CT HEAD/BRAIN W/O CONTRAST 70460 CT HEAD/BRAIN W/ CONTRAST 70470 CT HEAD/BRAIN W/O & W/ CONTRAST CPT CODE PROCEDURE DESCRIPTION CT Scans 70450 CT HEAD/BRAIN W/O CONTRAST 70460 CT HEAD/BRAIN W/ CONTRAST 70470 CT HEAD/BRAIN W/O & W/ CONTRAST 70480 CT ORBIT W/O CONTRAST 70481 CT ORBIT W/ CONTRAST 70482

More information

Application of Design of Experiments to an Automated Trading System

Application of Design of Experiments to an Automated Trading System Application of Design of Experiments to an Automated Trading System Ronald Schoenberg, Ph.D. Trading Desk Strategies, LLC ronschoenberg@optionbots.com www.optionbots.com A Design of Experiments (DOE) method

More information

Yiming Peng, Department of Statistics. February 12, 2013

Yiming Peng, Department of Statistics. February 12, 2013 Regression Analysis Using JMP Yiming Peng, Department of Statistics February 12, 2013 2 Presentation and Data http://www.lisa.stat.vt.edu Short Courses Regression Analysis Using JMP Download Data to Desktop

More information

ERCIYES UNIVERSITY MEDICAL FACULTY CARDIOVASCULAR SURGERY

ERCIYES UNIVERSITY MEDICAL FACULTY CARDIOVASCULAR SURGERY ERCIYES UNIVERSITY MEDICAL FACULTY Code - Title Stage of study MED511 THORACIC AND CARDIOVASCULAR SURGERY 5 th semester LOCAL CREDIT: 2 ECTS CREDITS: 3 Coordinating Lecturer ALL CONTACTS CONCERNİNG SOCRATE

More information

Two-sample hypothesis testing, I 9.07 3/09/2004

Two-sample hypothesis testing, I 9.07 3/09/2004 Two-sample hypothesis testing, I 9.07 3/09/2004 But first, from last time More on the tradeoff between Type I and Type II errors The null and the alternative: Sampling distribution of the mean, m, given

More information

CPT * Codes Included in AIM Preauthorization Program for 2013 With Grouper Numbers

CPT * Codes Included in AIM Preauthorization Program for 2013 With Grouper Numbers CPT * Codes Included in AIM Preauthorization Program for 2013 With Grouper Numbers Computerized Tomography (CT) CPT Description Abdomen 74150 CT abdomen; w/o contrast 6 74160 CT abdomen; with contrast

More information

Executive Summary. Summary - 1

Executive Summary. Summary - 1 Executive Summary For as long as human beings have deceived one another, people have tried to develop techniques for detecting deception and finding truth. Lie detection took on aspects of modern science

More information

Likelihood Approaches for Trial Designs in Early Phase Oncology

Likelihood Approaches for Trial Designs in Early Phase Oncology Likelihood Approaches for Trial Designs in Early Phase Oncology Clinical Trials Elizabeth Garrett-Mayer, PhD Cody Chiuzan, PhD Hollings Cancer Center Department of Public Health Sciences Medical University

More information

Dr. H. Declercq Az St Blasius Dendermonde

Dr. H. Declercq Az St Blasius Dendermonde Dr. H. Declercq Az St Blasius Dendermonde Before 16 slice scanner Introduction ict augustus 2010 Cardiac CT course (level 2 cardiac ct) Start cardiac CT in november 2010 More than 600 cardiac CT patients

More information

Twenty-first century radiology requires rapid and. Radiology by Nonradiologists: Is Report Documentation Adequate? CLINICAL. Shelley Nan Weiner, MD

Twenty-first century radiology requires rapid and. Radiology by Nonradiologists: Is Report Documentation Adequate? CLINICAL. Shelley Nan Weiner, MD CLINICAL Radiology by Nonradiologists: Is Report Documentation Adequate? Shelley Nan Weiner, MD Objective: To determine if the quality of medical imaging reports differs significantly between radiologists

More information

Advanced Quantitative Methods for Health Care Professionals PUBH 742 Spring 2015

Advanced Quantitative Methods for Health Care Professionals PUBH 742 Spring 2015 1 Advanced Quantitative Methods for Health Care Professionals PUBH 742 Spring 2015 Instructor: Joanne M. Garrett, PhD e-mail: joanne_garrett@med.unc.edu Class Notes: Copies of the class lecture slides

More information

6/5/2013. Predicting Student Loan Delinquency and Default. Outline. Introduction - Motivation (1) Reuben Ford CASFAA Conference, Ottawa June 10, 2013

6/5/2013. Predicting Student Loan Delinquency and Default. Outline. Introduction - Motivation (1) Reuben Ford CASFAA Conference, Ottawa June 10, 2013 Predicting Student Loan Delinquency and Default Reuben Ford CASFAA Conference, Ottawa June 10, 2013 Outline Introduction: Motivation and Research Questions Literature Review Methodology Selected Descriptive

More information

Radiologist Assistant Role Delineation

Radiologist Assistant Role Delineation Radiologist Assistant Role Delineation January 2005 Background The American Registry of Radiologic Technologists (ARRT) is developing a certification program for a new level of imaging technologist called

More information

On-line Spam Filter Fusion

On-line Spam Filter Fusion On-line Spam Filter Fusion Thomas Lynam & Gordon Cormack originally presented at SIGIR 2006 On-line vs Batch Classification Batch Hard Classifier separate training and test data sets Given ham/spam classification

More information

Correlational Research. Correlational Research. Stephen E. Brock, Ph.D., NCSP EDS 250. Descriptive Research 1. Correlational Research: Scatter Plots

Correlational Research. Correlational Research. Stephen E. Brock, Ph.D., NCSP EDS 250. Descriptive Research 1. Correlational Research: Scatter Plots Correlational Research Stephen E. Brock, Ph.D., NCSP California State University, Sacramento 1 Correlational Research A quantitative methodology used to determine whether, and to what degree, a relationship

More information

Chapter Five: Paired Samples Methods 1/38

Chapter Five: Paired Samples Methods 1/38 Chapter Five: Paired Samples Methods 1/38 5.1 Introduction 2/38 Introduction Paired data arise with some frequency in a variety of research contexts. Patients might have a particular type of laser surgery

More information

MedicalBiostatistics.com

MedicalBiostatistics.com 1 MedicalBiostatistics.com HOME ROC Curve Many full term births in a hospital require induction of labor. Induction succeeds in most cases but fails in a few. In case the induction fails, a Cesarean is

More information

Determining Minimum Sample Sizes for Estimating Prediction Equations for College Freshman Grade Average

Determining Minimum Sample Sizes for Estimating Prediction Equations for College Freshman Grade Average A C T Research Report Series 87-4 Determining Minimum Sample Sizes for Estimating Prediction Equations for College Freshman Grade Average Richard Sawyer March 1987 For additional copies write: ACT Research

More information

Testing Market Efficiency in a Fixed Odds Betting Market

Testing Market Efficiency in a Fixed Odds Betting Market WORKING PAPER SERIES WORKING PAPER NO 2, 2007 ESI Testing Market Efficiency in a Fixed Odds Betting Market Robin Jakobsson Department of Statistics Örebro University robin.akobsson@esi.oru.se By Niklas

More information

Generalized Linear Models

Generalized Linear Models Generalized Linear Models We have previously worked with regression models where the response variable is quantitative and normally distributed. Now we turn our attention to two types of models where the

More information