RSNA 2014 

Abstract Archives of the RSNA, 2014


SPSI22

Special Interest Session: Optimizing Quantitative Imaging Biomarkers for Practice: QIBA Examples from CT, MR, PET and US

Special Courses — Biomarkers/Quantitative Imaging, Computed Tomography, Magnetic Resonance Imaging, Nuclear Medicine, Oncologic Imaging, Ultrasound,

Presented on December 1, 2014

Participants

Daniel C. Sullivan MD, Moderator: Nothing to Disclose

LEARNING OBJECTIVES

1) Understand the activities that RSNA supports to help move the profession of radiology from a primarily qualitative interpretation paradigm to a more quantitative-based interpretation model. 2) Describe the challenges of extracting uniform, standardized quantitative measures from clinical imaging scans. 3) Describe the benefits of implementing more quantitative image interpretation in clinical radiology practice, including quality assurance activities and for the development of decision-support tools. 4) List an example of an imaging biomarker from CT, MR, PET and ultrasound scans that are needed in clinical practice.

ABSTRACT

In response to the need for reliable and reproducible quantification of biomedical imaging data, the RSNA in 2007 organized the Quantitative Imaging Biomarkers Alliance (QIBA, http://rsna.org/QIBA_.aspx) whose mission is to improve the value and practicality of quantitative imaging biomarkers by reducing variability across devices, patients and time. QIBA participants span a wide range of expertise including clinical practice, clinical research, physics, statistics, engineering, marketing, regulatory, pharmaceutical, and computer science. QIBA employs a systematic, consensus-driven approach to produce a QIBA Profile that includes one or more Claims and specifications for the image acquisition and processing necessary to achieve that Claim. QIBA Profiles are based on published data whenever such data are available and on expert consensus opinion for specifications where no data exist. Thus there are several sources of variability in the quantitative results obtained from clinical images, which can be grouped into three categories: (1) the image acquisition hardware, software and procedures; (2) the measurement methods used; and (3) the reader variability.  Examples of QIBA Profiles for CT volumetry, DW-MR, FDG-PET and ultrasound for liver elastography will be discussed.

Sub-Events

SPSI22A     Introduction
Daniel Sullivan MD

SPSI22B     CT for Lung Cancer Screening
James Mulshine MD

SPSI22C     DW-MR for Cancer Staging and Monitoring
Mark Rosen MD, PhD

SPSI22D     FDG-PET for Cancer Staging and Monitoring
Richard Wahl MD

SPSI22E     US Elastography for Liver Fibrosis Diagnosis and Monitoring
Anthony Samir MD

Cite This Abstract

Sullivan, D, Special Interest Session: Optimizing Quantitative Imaging Biomarkers for Practice: QIBA Examples from CT, MR, PET and US.  Radiological Society of North America 2014 Scientific Assembly and Annual Meeting, - ,Chicago IL. http://archive.rsna.org/2014/14002935.html