1 R2 ImageChecker CT CAD PMA: Clinical Results Nicholas Petrick, Ph.D. Office of Science and Technology Center for Devices and Radiological Health U.S.

Slides:



Advertisements
Similar presentations
1 Propensity Scores Methodology for Receiver Operating Characteristic (ROC) Analysis. Marina Kondratovich, Ph.D. U.S. Food and Drug Administration, Center.
Advertisements

Describing Quantitative Variables
Hypothesis testing and confidence intervals by resampling by J. Kárász.
Vision and Image Analysis Group (VIA) Anthony P. Reeves School of Electrical and Computer Engineering Cornell University © A. P. Reeves 2007.
ADVANCED STATISTICS FOR MEDICAL STUDIES Mwarumba Mwavita, Ph.D. School of Educational Studies Research Evaluation Measurement and Statistics (REMS) Oklahoma.
Evaluation of segmentation. Example Reference standard & segmentation.
Population Population
CAD Performance Analysis for Pulmonary Nodule Detection: Comparison of Thick- and Thin-Slice Multi- detector CT Scans Randy D Ernst 1, Russell C Hardie.
ACR and SBI Statement Margarita Zuley, MD Associate Professor, Radiology Medical Director, Breast Imaging University of Pittsburgh.
MARE 250 Dr. Jason Turner Hypothesis Testing II. To ASSUME is to make an… Four assumptions for t-test hypothesis testing:
Introduction to Inference Estimating with Confidence Chapter 6.1.
9-1 Hypothesis Testing Statistical Hypotheses Statistical hypothesis testing and confidence interval estimation of parameters are the fundamental.
Bootstrapping LING 572 Fei Xia 1/31/06.
AP Biology Intro to Statistic
Thoughts on Biomarker Discovery and Validation Karla Ballman, Ph.D. Division of Biostatistics October 29, 2007.
Screening and Early Detection Epidemiological Basis for Disease Control – Fall 2001 Joel L. Weissfeld, M.D. M.P.H.
12/10/02Harry Bushar1 Computerized Thermal Imaging Breast Cancer System 2100 (CTI BCS2100) Radiological Devices Advisory Panel December 10, 2002 Statistical.
Chapter 9 Title and Outline 1 9 Tests of Hypotheses for a Single Sample 9-1 Hypothesis Testing Statistical Hypotheses Tests of Statistical.
AM Recitation 2/10/11.
Statistics in Screening/Diagnosis
Quantitative Skills: Data Analysis and Graphing.
Bootstrap and Cross-Validation Bootstrap and Cross-Validation.
Liver Imaging Reporting and Data System with MR Imaging: Evaluation in Nodules 20 mm or Smaller Detected in Cirrhosis at Screening US Radiology 2015; 275:
Random Sampling, Point Estimation and Maximum Likelihood.
Lecture 12 Statistical Inference (Estimation) Point and Interval estimation By Aziza Munir.
1 Institute of Engineering Mechanics Leopold-Franzens University Innsbruck, Austria, EU H.J. Pradlwarter and G.I. Schuëller Confidence.
Continuous Probability Distributions Continuous random variable –Values from interval of numbers –Absence of gaps Continuous probability distribution –Distribution.
EDRN Approaches to Biomarker Validation DMCC Statisticians Fred Hutchinson Cancer Research Center Margaret Pepe Ziding Feng, Mark Thornquist, Yingye Zheng,
9-1 Hypothesis Testing Statistical Hypotheses Definition Statistical hypothesis testing and confidence interval estimation of parameters are.
CSCE555 Bioinformatics Lecture 16 Identifying Differentially Expressed Genes from microarray data Meeting: MW 4:00PM-5:15PM SWGN2A21 Instructor: Dr. Jianjun.
MATH IN THE FORM OF STATISTICS IS VERY COMMON IN AP BIOLOGY YOU WILL NEED TO BE ABLE TO CALCULATE USING THE FORMULA OR INTERPRET THE MEANING OF THE RESULTS.
DATA IDENTIFICATION AND ANALYSIS. Introduction  During design phase of a study, the investigator must decide which type of data will be collected and.
Validation / citations. Validation u Expert review of model structure u Expert review of basic code implementation u Reproduce original inputs u Correctly.
Statistical Review of Intergel by Richard Kotz Statistician, CDRH/OSB.
1 OTC-TFM Monograph: Statistical Issues of Study Design and Analyses Thamban Valappil, Ph.D. Mathematical Statistician OPSS/OB/DBIII Nonprescription Drugs.
CAD Performance Analysis for Pulmonary Nodule Detection: Comparison of Thick- and Thin-Slice Helical CT Scans Randy D Ernst 1, Russell C Hardie 2, Metin.
2/3/04Sacks1 Clinical Description William Sacks, PhD, MD—ODE/CDRH Clinical Description William Sacks, PhD, MD—ODE/CDRH R2 Technology, Inc. ImageChecker.
L Berkley Davis Copyright 2009 MER301: Engineering Reliability Lecture 9 1 MER301:Engineering Reliability LECTURE 9: Chapter 4: Decision Making for a Single.
Statistical considerations Drs. Jan Welink Training workshop: Assessment of Interchangeable Multisource Medicines, Kenya, August 2009.
1 Risk Assessment Tests Marina Kondratovich, Ph.D. OIVD/CDRH/FDA March 9, 2011 Molecular and Clinical Genetics Panel for Direct-to-Consumer (DTC) Genetic.
1 9 Tests of Hypotheses for a Single Sample. © John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. 9-1.
Limits to Statistical Theory Bootstrap analysis ESM April 2006.
Medical Statistics as a science
August 20, 2003FDA Antiviral Drugs Advisory Committee Meeting 1 Statistical Considerations for Topical Microbicide Phase 2 and 3 Trial Designs: A Regulatory.
1 Lecture 6: Descriptive follow-up studies Natural history of disease and prognosis Survival analysis: Kaplan-Meier survival curves Cox proportional hazards.
Association between genotype and phenotype
Quality control & Statistics. Definition: it is the science of gathering, analyzing, interpreting and representing data. Example: introduction a new test.
Organization of statistical research. The role of Biostatisticians Biostatisticians play essential roles in designing studies, analyzing data and.
1 EFFICACY OF SHORT COURSE AMOXICILLIN FOR NON-SEVERE PNEUMONIA IN CHILDREN (Hazir T*, Latif E*, Qazi S** AND MASCOT Study Group) *Children’s Hospital,
Case Selection and Resampling Lucila Ohno-Machado HST951.
BIOSTATISTICS Lecture 2. The role of Biostatisticians Biostatisticians play essential roles in designing studies, analyzing data and creating methods.
Chapter 5: Credibility. Introduction Performance on the training set is not a good indicator of performance on an independent set. We need to predict.
Statistics Nik Bobrovitz BHSc, MSc PhD Student University of Oxford December 2015
Radiological Devices Advisory Panel Meeting Radiological Devices Advisory Panel Meeting Computer-Assisted Detection Devices Panel Questions Radiological.
12/10/02Sacks - Clinical Assessment1 Clinical Assessment – Part II William Sacks, PhD, MD Clinical Assessment – Part II William Sacks, PhD, MD COMPUTERIZED.
Confidence Intervals and Hypothesis Testing Mark Dancox Public Health Intelligence Course – Day 3.
The Natural History of Benign Thyroid Nodules JAMA. 2015;313(9): doi: /jama Modulator Prof. 전숙 / R1 윤수진.
Statistics and probability Dr. Khaled Ismael Almghari Phone No:
Populations.
AP Biology Intro to Statistics
9 Tests of Hypotheses for a Single Sample CHAPTER OUTLINE
AP Biology Intro to Statistic
QQ Plot Quantile to Quantile Plot Quantile: QQ Plot:
AP Biology Intro to Statistic
AP Biology Intro to Statistic
Bootstrapping Jackknifing
Anil Vachani, MD, Harvey I. Pass, MD, William N. Rom, MD, David E
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
Presentation transcript:

1 R2 ImageChecker CT CAD PMA: Clinical Results Nicholas Petrick, Ph.D. Office of Science and Technology Center for Devices and Radiological Health U.S. Food and Drug Administration

2 Outline Applicability of A z in analysis A z is same as area under the curve (AUC) Pool of CT cases for clinical study Defining actionable nodules by panel of experts Clinical studies Primary analysis: analysis using fixed expert panel Secondary analysis: analysis using random panels of experts Measurement of CAD standalone performance Algorithm’s performance with no reader involvement

3 Applicability of A z in analysis Average reader ROC Curves (pre/post CAD) Pre-CAD ROC Post-CAD ROC

4 Applicability of A z in analysis Pre and post-CAD curves do not cross No substantial pre/post-CAD crossing in either averaged or individual ROC curves A z is an appropriate performance measure A z used as figure of merit in all analysis

5 Pool of CT Cases Nodule cases Documented cancers Primary neoplasm or extrathoracic neoplasm with presumptive spread to lungs Cases were allowed to contain non-nodule, pathologic processes (e.g., pneumonia, emphysema, etc.) Non-nodule cases Normal cases No nodule deemed present by site P.I. Primarily relied upon original radiology report History of cancer, radiation therapy, or even previous thorocatomy allowed

6 Defining Actionable Nodules by Panel of Experts ‘Actionable’ nodules are objects of interest Panel of expert radiologists identify actionable nodules Nodules defined using a 2-pass process

7 Defining Actionable Nodules by Panel of Experts 1 st reading of CT cases Cases read independently & blinded by 3 expert radiologists Radiologist provided subject’s age, gender, and indication for exam Marked all findings deemed lung nodules Radiologist provided rating Intervention – Actionable, further workup advised Surveillance – Actionable, monitor with follow-up studies Probably Benign, calcified – no action required Probably Benign, non-calcified – no action required

8 Defining Actionable Nodules by Panel of Experts 2 nd pass Findings that lacked 100% consensus after 1 st pass were reviewed unblinded by all 3 radiologists 2/3 or 1/3 radiologists called the location a nodule are reevaluated Radiologists rated (or re-rated) the actionability of the nodule candidates Thresholds applied to all findings >4mm diameter > -100 HU maximum density Each lung quadrant categorized by the highest actionable finding within quadrant

9 Defining Actionable Nodules by Panel of Experts DispositionUnanimous Actionable 3/3 Majority Actionable 2/3 Minority Actionable 1/3 Sample Size experts per panel

10 Clinical Studies ROC Observer Study A z is test statistic Analysis of a 90 cases dataset (360 quadrants) Confidence intervals and significance testing ANOVA-after-jackknife Bootstrap analysis

11 Clinical Studies Analysis Flowchart Resampling Scheme Jackknife or Bootstrap Definition Of Nodules MRMC ROC Observer Study Pool of Cases Pool of Experts Pool of Readers Az Estimates

12 ANOVA-after-Jackknife Analysis Parametric analysis Leave-one case out (all 4 quadrants, quadrant-based analysis) Analysis assumes modality as a fixed effect and readers, cases and all interactions as random effects Example Set: [1 2 3], Partitions:[1 2], [1 3], [2 3]

13 Bootstrap Analysis Nonparametric analysis Randomly generated datasets, based on original data with replacement Example Set: [1 2 3], Partitions:[3 2 3], [3 1 2], [1 1 2], …

14 Clinical Studies Primary Analysis Resampling Scheme Jackknife or Bootstrap Definition Of Nodules MRMC ROC Observer Study Pool of Cases Pool of Experts Pool of Readers Az Estimates Fixed 3-member nodule definition panels (unanimous consensus) ANOVA-after-jackknife and Bootstrap analysis

15 Clinical Studies Primary Analysis Fixed 3-member nodule definition panels Variance Analysis Pre-CAD Az Post-CAD Az ΔAzp-value Lower C.L. Upper C.L. Jackknife Bootstrap <

16 Clinical Studies Primary Analysis Statistically significant improvement in A z pre- to post-CAD ΔA z ~0.025 ANOVA-after-jackknife and bootstrap analysis is consistent Analysis limited because it did not take into account any variation in the expert panel Variability of panel would add uncertainty to performance estimates How would performance change with a different panel makeup? Different number of panel members Different set of experts

17 Clinical Studies Secondary Analysis Resampling Scheme Bootstrap Definition Of Nodules MRMC ROC Observer Study Pool of Cases Pool of Experts Pool of Readers Az Estimates Random 3, 2, 1-member nodule definition panels (unanimous consensus) Only bootstrap analysis possible

18 Clinical Studies Secondary Analysis Bootstrap analysis Random 3-member nodule definition panels Random Panel Size Pre-CAD Az Post-CAD Az ΔAzp-value Lower C.L. Upper C.L. 3-members < members member <

19 Clinical Studies Secondary Analysis Sponsor's analysis takes into account random nature of expert panel for defining ‘actionable’ nodules Different number of panel members: 3, 2, 1-member panels Different panel makeup: bootstrap selection of panel All variations of panel makeup confirm a statistically significant improvement in A z from pre to post-CAD ΔA z ~0.02 Likely to be a more appropriate analysis for assessment of devices when only panel truth is available

20 CAD Standalone Performance Performance of the CAD algorithm alone Algorithm sensitivity and specificity (no reader involvement) Standalone CAD performance is important Radiologist needs this information to appropriately weight their confidence in the CAD markings Benchmark for future revisions to the algorithm What is an appropriate performance measure for this device?

21 CAD Standalone Performance Many of 142 findings (Fixed 3-member panel) did not meet criteria as a solid discrete, spherical density Second panel reevaluated nodules for appearance 5 independent radiologists 2 Categories Classic nodule: discrete solid, spherical or ovoid Non-classic: Not discrete Hyperdense Irregularly shaped Normal structure Not a nodule

22 CAD Standalone Performance No. Panelists defining as classic No. of Findings CAD TPF (%) CAD False Marker Rate TP Median Diameter (mm) <3/ ~3 per-case / / / All <3/ ~3 per-case ≥3/

23 CAD Standalone Performance Large variation in performance of the CAD based on physicians assessment of nodule appearance as “classic”

24 Summary A z appropriate test statistic for clinical analysis No substantial crossing of pre/post-CAD ROC curves Primary Analysis Nodule definition panel Fixed 3-member expert panel Shows statistically significant A z improvement in detection with CAD ANOVA-after-jackknife and bootstrap are comparable

25 Summary Secondary Analysis Nodule Definition panel Varied number of panel members Varied the panel makeup (bootstrap selection of panel members) Confirmed statistically significant A z improvement in detection with CAD Standalone performance Large variation in CAD performance based on reassessment of nodule appearance Necessary for appropriate utilization of the device by clinicians in the field and assessment of future algorithm revisions