Download Quantitative Imaging Biomarkers Alliance

Status and potential for further
collaboration with RSNA QIBA
QIN Meeting, March 28, 2014
D. Sullivan, MD
Duke University;
RSNA
Premise
• Variation in clinical practice results in
poorer outcomes and higher costs.
RSNA’s Perspective:
• Extracting objective, quantitative
results from imaging studies will
improve the value of imaging in
clinical practice.
Quantitative Imaging Biomarkers
Alliance (QIBA): Background
• Started in 2007
• Mission: Improve value and practicality of
quantitative imaging biomarkers by reducing
variability across devices, patients, and time.
– “Industrialize imaging biomarkers”
QIBA Criteria for Biomarker
Selection
• Transformational
– addresses a significant medical need
• Translational
– will likely result in significant improvement in the development,
approval, or delivery of care to patients.
• Feasible
– end goals can likely be achieved in a specific timeframe
• Practical
– leverages preexisting resources (e.g., intellectual capital,
personnel, facilities, specimens, reagents, data) wherever
possible; warrants access to RSNA resources and support.
• Collaborative
– the biomarker has the support of the stakeholder community and
the organizational impetus to sustain continued efforts.
QIBA Committees
Quantitative Magnetic Resonance Imaging [Q-MR]
 Perfusion, Diffusion, and Flow-MRI (PDF-MRI)
 Functional MRI (fMRI)
Quantitative Computed Tomography [Q-CT]
 CT Volumetry in Solid Tumors and Lung Nodules
 CT Densitometry in COPD
 Airway Morphology in Asthma
Quantitative Nuclear Medicine [Q-NM]
 FDG-PET SUV
 Amyloid-PET
Quantitative Ultrasound [Q-US]
 Shear Wave Speed for liver fibrosis
Imaging Assays
Assays are characterized by their:
•
Technical Performance
•
Clinical Performance
 Clinical validation
 Clinical utility
QIN
Variability in imaging measurements
is related to:
1. Image acquisition variability
2. Radiologist/Reader variability
3. Measurement method variability
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QIBA Profiles
A QIBA Profile describes a
specific performance Claim and
how it can be achieved.
QIBA Claim Template
• List Biomarkers/Measurand(s)
• Specify: Cross-sectional vs. Longitudinal
measurement
• List Indices:
– Bias Profile (Disaggregate indices)
– Precision Profile
• Test-retest Repeatability (Repeatability coefficient)
• Reproducibility (Reproducibility coefficient; Intra-class
Correlation Coefficient [ICC]; Concordant Correlation
Coefficient [CCC]).
– Specify conditions, e.g.,
» Measuring System variability (hardware &
software)
» Site variability
» Operator variability (Intra- or Inter-reader)
• Clinical Context
True Biologic Change …
• … is approximately twice the variability
• Clinical Significance of that change
needs to be determined by clinical
studies.
Topics for Collaboration
Discussion:
Reducing variability in imaging
measurements is important to both
QIN and QIBA:
1. Image acquisition variability
a) Test objects – physical and virtual
2. Radiologist/Reader variability
3. Measurement method variability
a) Algorithm comparisons
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Measurement method variability
How do we deal with the fact that different
algorithms that purport to measure the same
thing give different answers?
 Methodology for comparing algorithms
 Metrics of performance on same task
 Criteria for acceptability (compliance).
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Thank you.