Statistical Issues in Testing Conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Profile Claims.

Abstract

A major initiative of the Quantitative Imaging Biomarker Alliance is to develop standards-based documents called "Profiles," which describe one or more technical performance claims for a given imaging modality. The term "actor" denotes any entity (device, software, or person) whose performance must meet certain specifications for the claim to be met. The objective of this paper is to present the statistical issues in testing actors' conformance with the specifications. In particular, we present the general rationale and interpretation of the claims, the minimum requirements for testing whether an actor achieves the performance requirements, the study designs used for testing conformity, and the statistical analysis plan. We use three examples to illustrate the process: apparent diffusion coefficient in solid tumors measured by MRI, change in Perc 15 as a biomarker for the progression of emphysema, and percent change in solid tumor volume by computed tomography as a biomarker for lung cancer progression.

Department

Description

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Citation

Published Version (Please cite this version)

10.1016/j.acra.2015.12.020

Publication Info

Obuchowski, Nancy A, Andrew Buckler, Paul Kinahan, Heather Chen-Mayer, Nicholas Petrick, Daniel P Barboriak, Jennifer Bullen, Huiman Barnhart, et al. (2016). Statistical Issues in Testing Conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Profile Claims. Academic radiology, 23(4). pp. 496–506. 10.1016/j.acra.2015.12.020 Retrieved from https://hdl.handle.net/10161/27330.

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Scholars@Duke

Barboriak

Daniel P. Barboriak

Professor of Radiology

(1) MR Imaging in Neuro-oncology. (2) MR Perfusion Imaging for quantitation of blood volume and permeability. (3) MR Diffusion Imaging. (4) Image Processing for Registration and Segmentation. (5) Pediatric Neuroradiology.

Barnhart

Huiman Xie Barnhart

James B. Duke Distinguished Professor

My research interests include both statistical methodology and disease-specific clinical research biostatistics. My statistical research areas include methods for outcomes, endpoints, estimands, assessing reliability/agreement between methods or raters, evaluating performance of new medical diagnostic tests, and methods for design of clinical trials. My collaborative research include the following clinical areas: liver injury, cardiovascular imaging, radiology imaging, cardiovascular disease, renal disease, reproductive medicine, Parkinson disease, and aging.

Sullivan

Daniel Carl Sullivan

Professor Emeritus of Radiology

Research interests are in oncologic imaging, especially the clinical evaluation and validation of imaging biomarkers for therapeutic response assessment.


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