A Collaborative Enterprise for Multi-Stakeholder Participation in the Advancement of Quantitative Imaging

Buckler, Andrew J.; Bresolin, Linda; Dunnick, N. Reed; Sullivan, Daniel C.

doi:10.1148/radiol.10100799

Cited by 157 publications

(119 citation statements)

References 23 publications

(9 reference statements)

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“…The Radiological Society of North America has formed the Quantitative Imaging Biomarker Alliance (QIBA) to advance quantitative imaging in clinical care and facilitating imaging as a biomarker in clinical trials [24,25]. While engaging many stakeholders across academia, government, and industry, the effort is limited to the most mature biomarkers.…”

Section: Imaging Biomarker Researchmentioning

confidence: 99%

Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers

et al. 2013

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A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have demonstrated effectiveness in quantifying biological processes as they occur in vivo and in the early prediction of therapeutic outcomes. However, quantitative imaging biomarker data and knowledge are not standardized, representing a critical barrier to accumulating medical knowledge based on quantitative imaging data. We use an ontology to represent, integrate, and harmonize heterogeneous knowledge across the domain of imaging biomarkers. This advances the goal of developing applications to (1) improve precision and recall of storage and retrieval of quantitative imaging-related data using standardized terminology; (2) streamline the discovery and development of novel imaging biomarkers by normalizing knowledge across heterogeneous resources; (3) effectively annotate imaging experiments thus aiding comprehension, re-use, and reproducibility; and (4) provide validation frameworks through rigorous specification as a basis for testable hypotheses and compliance tests. We have developed the Quantitative Imaging Biomarker Ontology (QIBO), which currently consists of 488 terms spanning the following upper classes: experimental subject, biological intervention, imaging agent, imaging instrument, image postprocessing algorithm, biological target, indicated biology, and biomarker application. We have demonstrated that QIBO can be used to annotate imaging experiments with standardized terms in the ontology and to generate hypotheses for novel imaging biomarker-disease associations. Our results established the utility of QIBO in enabling integrated analysis of quantitative imaging data.

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Section: Imaging Biomarker Researchmentioning

confidence: 99%

Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers

et al. 2013

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“…Other QIBs include coronary artery calcium scores from CT for atherosclerosis screening, Standardized Uptake Values (SUV) in PET imaging to measure cancer cell activity, and Dynamic Contrast-Enhanced MRI for measuring microvascularity of tumors. The Radiological Society of North America (RSNA) created the Quantitative Imaging Biomarker Alliance (QIBA) to investigate the role of quantitative imaging methods, including CT volumetry, in evaluating disease and responses to treatment [1-3]. …”

Section: Introductionmentioning

confidence: 99%

Statistical issues in the comparison of quantitative imaging biomarker algorithms using pulmonary nodule volume as an example

Obuchowski

Barnhart

Buckler

et al. 2014

Stat Methods Med Res

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Quantitative imaging biomarkers (QIBs) are being used increasingly in medicine to diagnose and monitor patients' disease. The computer algorithms that measure QIBs have different technical performance characteristics. In this paper we illustrate the appropriate statistical methods for assessing and comparing the bias, precision, and agreement of computer algorithms. We use data from three studies of pulmonary nodules. The first study is a small phantom study used to illustrate metrics for assessing repeatability. The second study is a large phantom study allowing assessment of four algorithms' bias and reproducibility for measuring tumor volume and the change in tumor volume. The third study is a small clinical study of patients whose tumors were measured on two occasions. This study allows a direct assessment of six algorithms' performance for measuring tumor change. With these three examples we compare and contrast study designs and performance metrics, and we illustrate the advantages and limitations of various common statistical methods for QIB studies.

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“…This paper is motivated by the activities of the Radiological Society of North America (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) [9]. The mission of QIBA is to improve the value and practicality of quantitative imaging biomarkers by reducing variability across devices, patients, and time.…”

Section: Background and Problem Statementmentioning

confidence: 99%

Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons

Obuchowski

Reeves

Huang

et al. 2014

Stat Methods Med Res

145

157

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Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, digital reference images, and zero-change studies), designs with a reference standard (e.g. studies testing equivalence with a reference standard), and designs without a reference standard (e.g. agreement studies and studies of algorithm precision). The statistical methods for comparing QIB algorithms are then presented for various study types using both aggregate and disaggregate approaches. We propose a series of steps for establishing the performance of a QIB algorithm, identify limitations in the current statistical literature, and suggest future directions for research.

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A Collaborative Enterprise for Multi-Stakeholder Participation in the Advancement of Quantitative Imaging

Cited by 157 publications

References 23 publications

Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers

Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers

Statistical issues in the comparison of quantitative imaging biomarker algorithms using pulmonary nodule volume as an example

Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons

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