Browsing by Author "Obuchowski, Nancy A"

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    Correction to: Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers.
    (European radiology, 2021-03-10) Fournier, Laure; Costaridou, Lena; Bidaut, Luc; Michoux, Nicolas; Lecouvet, Frederic E; de Geus-Oei, Lioe-Fee; Boellaard, Ronald; Oprea-Lager, Daniela E; Obuchowski, Nancy A; Caroli, Anna; Kunz, Wolfgang G; Oei, Edwin H; O'Connor, James PB; Mayerhoefer, Marius E; Franca, Manuela; Alberich-Bayarri, Angel; Deroose, Christophe M; Loewe, Christian; Manniesing, Rashindra; Caramella, Caroline; Lopci, Egesta; Lassau, Nathalie; Persson, Anders; Achten, Rik; Rosendahl, Karen; Clement, Olivier; Kotter, Elmar; Golay, Xavier; Smits, Marion; Dewey, Marc; Sullivan, Daniel C; van der Lugt, Aad; deSouza, Nandita M; European Society of Radiology
    © 2021, The Author(s). The original version of this article, published on 25 January 2021, unfortunately contained mistakes. The following corrections have therefore been made in the original: Firstly, “endorsed by the European Society of Radiology” was missing in the article title. Secondly, the institutional author “European Society of Radiology” was missing in the author line, including the related affiliation 34. Thirdly, the following sentence was missing in the Acknowledgements: This paper was endorsed by the ESR Executive Council in December 2020. The corrected title and author line are given above; the corrected affiliations are given below. The original article has been corrected.
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    Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers.
    (European radiology, 2021-01-25) Fournier, Laure; Costaridou, Lena; Bidaut, Luc; Michoux, Nicolas; Lecouvet, Frederic E; de Geus-Oei, Lioe-Fee; Boellaard, Ronald; Oprea-Lager, Daniela E; Obuchowski, Nancy A; Caroli, Anna; Kunz, Wolfgang G; Oei, Edwin H; O'Connor, James PB; Mayerhoefer, Marius E; Franca, Manuela; Alberich-Bayarri, Angel; Deroose, Christophe M; Loewe, Christian; Manniesing, Rashindra; Caramella, Caroline; Lopci, Egesta; Lassau, Nathalie; Persson, Anders; Achten, Rik; Rosendahl, Karen; Clement, Olivier; Kotter, Elmar; Golay, Xavier; Smits, Marion; Dewey, Marc; Sullivan, Daniel C; van der Lugt, Aad; deSouza, Nandita M; European Society Of Radiology
    Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.
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    Introduction to metrology series.
    (Statistical methods in medical research, 2015-02) Sullivan, Daniel C; Bresolin, Linda; Seto, Belinda; Obuchowski, Nancy A; Raunig, David L; Kessler, Larry G
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    Metrology Standards for Quantitative Imaging Biomarkers.
    (Radiology, 2015-12) Sullivan, Daniel C; Obuchowski, Nancy A; Kessler, Larry G; Raunig, David L; Gatsonis, Constantine; Huang, Erich P; Kondratovich, Marina; McShane, Lisa M; Reeves, Anthony P; Barboriak, Daniel P; Guimaraes, Alexander R; Wahl, Richard L; RSNA-QIBA Metrology Working Group
    Although investigators in the imaging community have been active in developing and evaluating quantitative imaging biomarkers (QIBs), the development and implementation of QIBs have been hampered by the inconsistent or incorrect use of terminology or methods for technical performance and statistical concepts. Technical performance is an assessment of how a test performs in reference objects or subjects under controlled conditions. In this article, some of the relevant statistical concepts are reviewed, methods that can be used for evaluating and comparing QIBs are described, and some of the technical performance issues related to imaging biomarkers are discussed. More consistent and correct use of terminology and study design principles will improve clinical research, advance regulatory science, and foster better care for patients who undergo imaging studies.
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    Statistical Issues in Testing Conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Profile Claims.
    (Academic radiology, 2016-04) Obuchowski, Nancy A; Buckler, Andrew; Kinahan, Paul; Chen-Mayer, Heather; Petrick, Nicholas; Barboriak, Daniel P; Bullen, Jennifer; Barnhart, Huiman; Sullivan, Daniel C
    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.
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    The QIBA Profile for FDG PET/CT as an Imaging Biomarker Measuring Response to Cancer Therapy.
    (Radiology, 2020-03) Kinahan, Paul E; Perlman, Eric S; Sunderland, John J; Subramaniam, Rathan; Subramaniam, Rathan; Wollenweber, Scott D; Turkington, Timothy G; Lodge, Martin A; Boellaard, Ronald; Obuchowski, Nancy A; Wahl, Richard L
    The Quantitative Imaging Biomarkers Alliance (QIBA) Profile for fluorodeoxyglucose (FDG) PET/CT imaging was created by QIBA to both characterize and reduce the variability of standardized uptake values (SUVs). The Profile provides two complementary claims on the precision of SUV measurements. First, tumor glycolytic activity as reflected by the maximum SUV (SUVmax) is measurable from FDG PET/CT with a within-subject coefficient of variation of 10%-12%. Second, a measured increase in SUVmax of 39% or more, or a decrease of 28% or more, indicates that a true change has occurred with 95% confidence. Two applicable use cases are clinical trials and following individual patients in clinical practice. Other components of the Profile address the protocols and conformance standards considered necessary to achieve the performance claim. The Profile is intended for use by a broad audience; applications can range from discovery science through clinical trials to clinical practice. The goal of this report is to provide a rationale and overview of the FDG PET/CT Profile claims as well as its context, and to outline future needs and potential developments.