QIBA CT Volumetrics Group 1B: (Patient Image Datasets) Update April 19, 2011.

Slides:



Advertisements
Similar presentations
3D Imaging Research Massachusetts General Hospital Harvard Medical School 3D Computer-Aided Diagnosis (CAD) in Radiology Hiro Yoshida, PhD.
Advertisements

Statistical vs. Practical Significance
Study Size Planning for Observational Comparative Effectiveness Research Prepared for: Agency for Healthcare Research and Quality (AHRQ)
Chapter 9 Choosing the Right Research Design Chapter 9.
Sample Size And Power I Jean B. Nachega, MD, PhD Department of Medicine & Centre for Infectious Diseases Stellenbosch University
SPH 247 Statistical Analysis of Laboratory Data 1April 2, 2013SPH 247 Statistical Analysis of Laboratory Data.
3-Dimensional Gait Measurement Really expensive and fancy measurement system with lots of cameras and computers Produces graphs of kinematics (joint.
PET/CT Working Group Update Jayashree Kalpathy-Cramer Sandy Napel.
Volumetric Measurement of Tumors David F. Yankelevitz, MD.
McDaniels – Sept 12, Outline Uncertainty in ADC value Correction Manuscript.
Estimation and Reporting of Heterogeneity of Treatment Effects in Observational Comparative Effectiveness Research Prepared for: Agency for Healthcare.
15 de Abril de A Meta-Analysis is a review in which bias has been reduced by the systematic identification, appraisal, synthesis and statistical.
Sampling & Estimation. Normal Distribution Normal Sample.
SAMPLE SIZE ESTIMATION
8-1 Is Process Capable ? The Quality Improvement Model Use SPC to Maintain Current Process Collect & Interpret Data Select Measures Define Process Is Process.
Intro to Statistics for the Behavioral Sciences PSYC 1900 Lecture 10: Hypothesis Tests for Two Means: Related & Independent Samples.
McDaniels – Oct 10, Outline Geometric Uncertainty Uncertainty in average intensity due to lesion placement ADC uncertainty.
Automated Image Quality Assessment for Diffusion Tensor Data 2 J.Meakin, 1 S. Counsell, 1 E. Hughes, 1 J.V.Hajnal and 1 D.J.Larkman 1 Imaging Sciences.
© ABSL Power Solutions 2007 © STM Quality Limited STM Quality Limited Measurement Systems Analysis TOTAL QUALITY MANAGEMENT M.S.A.
Standard error of estimate & Confidence interval.
Basic principles Geometry and historical development
Factor Analysis Psy 524 Ainsworth.
QIBA CT Volumetrics - Cross-Platform Study (Group 1C) May 6, 2009 Interclinic Comparison of CT Volumetry Quantitative Imaging Biomarker Alliance.
Update on Lung Cancer Image Processing Rick Avila Karthik Krishnan Luis Ibanez Kitware, Inc. April 19, 2006.
Attenuation of Treatment Effect Due to Measurement Variability in Assessment of Progression-Free Survival Pharmaceut. Statist. 2012, –402 Nicola.
QIBA CT Volumetrics - Cross-Platform Study (Group 1C) December 23, 2009 CT Cross-platform Sizing of Phantom Lesions Quantitative Imaging Biomarker Alliance.
Slide - 1 Confidential. Slide - 2 Confidential Slide - 3 Confidential Interalgorithm Study using CT Images of synthetic nodules……….
Guide to Handling Missing Information Contacting researchers Algebraic recalculations, conversions and approximations Imputation method (substituting missing.
The next step in performance monitoring – Stochastic monitoring (and reserving!) NZ Actuarial Conference November 2010.
Precision, Error and Accuracy Physics 12 Adv. Measurement  When taking measurements, it is important to note that no measurement can be taken exactly.
How to Teach Statistics in EBM Rafael Perera. Basic teaching advice Know your audience Know your audience! Create a knowledge gap Give a map of the main.
Success depends upon the ability to measure performance. Rule #1:A process is only as good as the ability to reliably measure.
COMBI RECIST 1.1 February Target Lesions: Selection at Baseline Perform baseline evaluations as close to treatment start as possible (no more than.
1 Inter-observer Agreement of The Response To Therapy AssessmentInter-observer Agreement of The Response To Therapy Assessment in Advanced Lung Cancer.
RECIST Overview.
2005 Updated 10/19/09 Sampling Distribution Tripthi M. Mathew, MD, MPH, MBA.
Topic (ii): New and Emerging Methods Maria Garcia (USA) Jeroen Pannekoek (Netherlands) UNECE Work Session on Statistical Data Editing Paris, France,
QIBA CT Volumetrics - Cross-Platform Study (Group 1C) September 2, 2009 Interclinical Comparison of CT Volumetry Quantitative Imaging Biomarker Alliance.
The Campbell Collaborationwww.campbellcollaboration.org C2 Training: May 9 – 10, 2011 Introduction to meta-analysis.
QIBA CT Volumetrics Group 1B: (Patient Image Datasets)
Slide - 1 Confidential 3A Group Dr Maria Athelogou Grace Kim: Clarification in the study design: Since we have contours of lesions from many readers (for.
QIBA CT Volumetrics - Cross-Platform Study (Group 1C) March 18, 2009 Interclinic Comparison of CT Volumetry Quantitative Imaging Biomarker Alliance.
QIBA CT Volumetrics Group 1B: (Patient Image Datasets) Teleconference Nov 5, 2008.
N Petrick RSNA QIBA Phantom Group November 13, QIBA Proposed Phase 1A Project V5.0 QIBA Phantom Subgroup.
Worked Example Using R. > plot(y~x) >plot(epsilon1~x) This is a plot of residuals against the exploratory variable, x.
JAM-boree: A Meta-Analysis of Judgments of Associative Memory Kathrene D. Valentine, Erin M. Buchanan, Missouri State University Abstract Judgments of.
Sample Size Determination
The PET/CT Working Group: CT Segmentation Challenge Informatics Issues Multi-site algorithm comparison Task: CT-based lung nodule segmentation.
October 16, 2002Experimental Design1 Principles of Experimental Design October 16, 2002 Mark Conaway.
Methods for Dummies Second level Analysis (for fMRI) Chris Hardy, Alex Fellows Expert: Guillaume Flandin.
Precision, Error and Accuracy Physics 12. Measurement  When taking measurements, it is important to note that no measurement can be taken exactly  Therefore,
Standard Response Evaluation Criteria in Solid Tumors (RECIST) using 3D Slicer Slicer3 Training Compendium Image here. Jeffrey Yap, PhD Wendy Plesniak,
CLEAR-III CT Radiology Course
Automatic Lung Nodule Detection Using Deep Learning
Scottish National Burden of Disease, Injuries and Risk Factors study:
Physica Medica 32 (2016) 1570–1574 報告人:王俊淵
H676 Week 3 – Effect Sizes Additional week for coding?
The Importance of Adequately Powered Studies
Waterfall plots Antonio Nieto / Javier Gómez
Gerald - P&R Chapter 7 (to 217) and TEXT Chapters 15 & 16
Johnny Suh M.D., Dr. Jacobson M.D., Dr. Pond M.D.
Estimates and 95% CIs of between- and within-pair variations for SS and OS twin pairs and achievement test scores in mathematics and reading assessed in.
Basic principles Geometry and historical development
Quantitative Discrimination of Pigmented Lesions Using Three-Dimensional High- Resolution Ultrasound Reflex Transmission Imaging  Deepak Rallan, Nigel.
SAMPLING AND STATISTICAL POWER
Rapid onset of action in patients with moderate-to-severe psoriasis treated with brodalumab: A pooled analysis of data from two phase 3 randomized clinical.
Differences in spinal T1 imaging with epidural fat region of interest selection and validation. Differences in spinal T1 imaging with epidural fat region.
Lab Improvements/Changes
Impact of approaches for clinical and radiological monitoring on predicting of short-term and long-term disability outcomes in multiple sclerosis Brian.
Presentation transcript:

QIBA CT Volumetrics Group 1B: (Patient Image Datasets) Update April 19, 2011

Experiments to Explore First: 1. What level of bias and variance can be achieved in measuring tumor volumes in patient datasets? 2. What is the minimum detectable level of change that can be achieved when measuring tumors in patient datasets under a “No Change” condition?

Agenda  Review Study Aims, Designs, Methods Study Aims, Designs, Methods  Analyses Pooled Pooled Subjective 1 (Mike O’Neal) Subjective 1 (Mike O’Neal) Data Driven (over Grace’s objections) Data Driven (over Grace’s objections) Subjective 2 (Mike McNitt-Gray) Subjective 2 (Mike McNitt-Gray) Min. Detectable Change Min. Detectable Change  Next Steps

Experiment 2 – What is the min detectable level of change in patient datasets under a “No Change” condition? 1. Specific Aims (a) For patient datasets acquired over a very short time interval (presumably the “no change” condition”) investigate variance of both readers and algorithm- assisted readers in measuring change in volume, diameter and bi-directional diameters of lesions (here, the expected value of the change should be zero) (b) Investigate several change metrics such as: (a)Absolute value of change (b)fractional change in volume/diameter (c) Investigate inter-observer variability in each task (d) Investigate Intra-observer variability in each task (NOTE: again, observer should be interpreted broadly – as reader measuring manually for diameters as well as algorithm-assisted reader measuring contours).

Methods  RIDER – MSK Coffee Break Experiment (No Change Condition) 32 NSCLC patients 32 NSCLC patients Imaged twice on the same scanner w/in 15 minutes Imaged twice on the same scanner w/in 15 minutes Thin section (1.25 mm) images Thin section (1.25 mm) images Selected only one lesion per patient ->32 lesions. Selected only one lesion per patient ->32 lesions.

Methods Multiple Markings Multiple Markings Manual linear measurements (Single Longest Diameter on one image) Manual linear measurements (Single Longest Diameter on one image) Separate Manual 2 Bi-direectional Diameters (Longest Diameter and Diameter perp.) Separate Manual 2 Bi-direectional Diameters (Longest Diameter and Diameter perp.) Single Longest Diameter is also retained for ComparisonSingle Longest Diameter is also retained for Comparison Separate Algorithm Assisted volume (Reader contours entire boundary of lesion). Separate Algorithm Assisted volume (Reader contours entire boundary of lesion). Also calculate Single Longest diameter in a given image as well as perp diameterAlso calculate Single Longest diameter in a given image as well as perp diameter

Methods 5 readers 5 readers Read each case: Read each case: Scan 1, Scan 2, repeat read of Scan 1 (to assess intra reader variation)Scan 1, Scan 2, repeat read of Scan 1 (to assess intra reader variation) Order is randomized by patient, scan, measurement type Order is randomized by patient, scan, measurement type

Methods 1. METHODS and MATERIALS  To expedite lesion identification, Lesions are pre- identified and approximate locations are provided to readers. This will be done using proprietary software at RadPharm.This will be done using proprietary software at RadPharm. Lesions were pre-identified by placing an ellipse on the 2nd or 3rd slice through the lesion (eliminating slice selection bias by the reader).Lesions were pre-identified by placing an ellipse on the 2nd or 3rd slice through the lesion (eliminating slice selection bias by the reader). Reading permissions for the linear measurement application were set so that each reader can see these annotations, but no one else’s.Reading permissions for the linear measurement application were set so that each reader can see these annotations, but no one else’s. Readers can move quickly to identify the lesion to performing marking taskReaders can move quickly to identify the lesion to performing marking task

Methods  Analyses Estimate variance measured values for Estimate variance measured values for Diameter (from Manual, Bidimensional and Vol)Diameter (from Manual, Bidimensional and Vol) Product of Diameters (Manual and Vol)Product of Diameters (Manual and Vol) VolumeVolume Estimate inter-reader variability Estimate inter-reader variability Intra-reader variability from those cases repeated by readers Intra-reader variability from those cases repeated by readers

Results  Pooled Analysis across all readers and all lesions across all readers and all lesions  Percent Difference between scans 1 and 2 Mean (SD) Mean (SD) 1D: 5.84 (23.83) 1D: 5.84 (23.83) 2D: (68.45) 2D: (68.45) 3D: (117.88) 3D: (117.88)

Results Heterogeneity across Readers and Lesions (especially small ones)

Results

Results

Subgroup Analyses – 1  Mike O’Neal rated RECIST measurable or NO  28 cases were measurable, 4 NO Method1D2D3D RECIST measurable (21.77) (68.92)32.40 (101.22) NOT (11.79) (21.93)77.42 (180.43)

Subgroup Analyses – 2  MMG rated Difficult/Moderate/Easy  Percent Difference between scans Method1D2D3D Easy0.99 (.06)2.58 (15.61)2.85 (13.53) Moderate9.42 (28.57)17.99 (61.79)42.82 (107.49) Difficult8.52 (29.54)24.40 (91.75)36.76 (159.12)

Minimum Detectable Change 1D [95% CI]* 2D [95% CI]* 3D [95% CI]* All (N=32)[-0.3%, 12%][1%, 29%][-4%, 55%] Measurable (N=28)[-1%, 14%][-1%, 32%][-1%, 41%] Un-measurable (N=4)[-8%, 13%][-4%, 30%][-31, 158%] *: mixed effect model were used, where readers were random effects.

Minimum Detectable Change 1D [95% CI]* 2D [95% CI]* 3D [95% CI]* All (N=32)[-0.3%, 12%][1%, 29%][-4%, 55%] 2 nd subjective assessment – EASY (N=12)[-1%, 3%][-2%, 7%][-1, 7%] Moderate (N=5)[-4, 23%][-17, 53%][-4, 89%] Difficult (N=15)[-1%, 18%][2, 47%][-10, 83%] *: mixed effect model were used, where readers were random effects.

% Change in Volume by Type

Only cases < 400%

Next Steps  Further Analyses?  Present to QIBA volCT group  Manuscript One manuscript on these results One manuscript on these results One editorial on implications for QIBA/Clinical Trials, etc.? One editorial on implications for QIBA/Clinical Trials, etc.?