Browsing by Author "Sullivan, Daniel C"
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Item Open Access A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging.(Radiology, 2011-03) Buckler, Andrew J; Bresolin, Linda; Dunnick, N Reed; Sullivan, Daniel C; GroupMedical imaging has seen substantial and rapid technical advances during the past decade, including advances in image acquisition devices, processing and analysis software, and agents to enhance specificity. Traditionally, medical imaging has defined anatomy, but increasingly newer, more advanced, imaging technologies provide biochemical and physiologic information based on both static and dynamic modalities. These advanced technologies are important not only for detecting disease but for characterizing and assessing change of disease with time or therapy. Because of the rapidity of these advances, research to determine the utility of quantitative imaging in either clinical research or clinical practice has not had time to mature. Methods to appropriately develop, assess, regulate, and reimburse must be established for these advanced technologies. Efficient and methodical processes that meet the needs of stakeholders in the biomedical research community, therapeutics developers, and health care delivery enterprises will ultimately benefit individual patients. To help address this, the authors formed a collaborative program-the Quantitative Imaging Biomarker Alliance. This program draws from the very successful precedent set by the Integrating the Healthcare Enterprise effort but is adapted to the needs of imaging science. Strategic guidance supporting the development, qualification, and deployment of quantitative imaging biomarkers will lead to improved standardization of imaging tests, proof of imaging test performance, and greater use of imaging to predict the biologic behavior of tissue and monitor therapy response. These, in turn, confer value to corporate stakeholders, providing incentives to bring new and innovative products to market.Item Open Access An Online Repository for Pre-Clinical Imaging Protocols (PIPs).(Tomography (Ann Arbor, Mich.), 2023-03) Gammon, Seth T; Cohen, Allison S; Lehnert, Adrienne L; Sullivan, Daniel C; Malyarenko, Dariya; Manning, Henry Charles; Hormuth, David A; Daldrup-Link, Heike E; An, Hongyu; Quirk, James D; Shoghi, Kooresh; Pagel, Mark David; Kinahan, Paul E; Miyaoka, Robert S; Houghton, A McGarry; Lewis, Michael T; Larson, Peder; Sriram, Renuka; Blocker, Stephanie J; Pickup, Stephen; Badea, Alexandra; Badea, Cristian T; Yankeelov, Thomas E; Chenevert, Thomas LProviding method descriptions that are more detailed than currently available in typical peer reviewed journals has been identified as an actionable area for improvement. In the biochemical and cell biology space, this need has been met through the creation of new journals focused on detailed protocols and materials sourcing. However, this format is not well suited for capturing instrument validation, detailed imaging protocols, and extensive statistical analysis. Furthermore, the need for additional information must be counterbalanced by the additional time burden placed upon researchers who may be already overtasked. To address these competing issues, this white paper describes protocol templates for positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance imaging (MRI) that can be leveraged by the broad community of quantitative imaging experts to write and self-publish protocols in protocols.io. Similar to the Structured Transparent Accessible Reproducible (STAR) or Journal of Visualized Experiments (JoVE) articles, authors are encouraged to publish peer reviewed papers and then to submit more detailed experimental protocols using this template to the online resource. Such protocols should be easy to use, readily accessible, readily searchable, considered open access, enable community feedback, editable, and citable by the author.Item Open Access 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.Item Open Access Critical Review of Current Approaches for Echocardiographic Reproducibility and Reliability Assessment in Clinical Research.(Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography, 2016-12) Crowley, Anna Lisa; Yow, Eric; Barnhart, Huiman X; Daubert, Melissa A; Bigelow, Robert; Sullivan, Daniel C; Pencina, Michael; Douglas, Pamela SBackground
There is no broadly accepted standard method for assessing the quality of echocardiographic measurements in clinical research reports, despite the recognized importance of this information in assessing the quality of study results.Methods
Twenty unique clinical studies were identified reporting echocardiographic data quality for determinations of left ventricular (LV) volumes (n = 13), ejection fraction (n = 12), mass (n = 9), outflow tract diameter (n = 3), and mitral Doppler peak early velocity (n = 4). To better understand the range of possible estimates of data quality and to compare their utility, reported reproducibility measures were tabulated, and de novo estimates were then calculated for missing measures, including intraclass correlation coefficient (ICC), 95% limits of agreement, coefficient of variation (CV), coverage probability, and total deviation index, for each variable for each study.Results
The studies varied in approaches to reproducibility testing, sample size, and metrics assessed and values reported. Reported metrics included mean difference and its SD (n = 7 studies), ICC (n = 5), CV (n = 4), and Bland-Altman limits of agreement (n = 4). Once de novo estimates of all missing indices were determined, reasonable reproducibility targets for each were identified as those achieved by the majority of studies. These included, for LV end-diastolic volume, ICC > 0.95, CV < 7%, and coverage probability > 0.93 within 30 mL; for LV ejection fraction, ICC > 0.85, CV < 8%, and coverage probability > 0.85 within 10%; and for LV mass, ICC > 0.85, CV < 10%, and coverage probability > 0.60 within 20 g.Conclusions
Assessment of data quality in echocardiographic clinical research is infrequent, and methods vary substantially. A first step to standardizing echocardiographic quality reporting is to standardize assessments and reporting metrics. Potential benefits include clearer communication of data quality and the identification of achievable targets to benchmark quality improvement initiatives.Item Open Access Current and future cancer staging after neoadjuvant treatment for solid tumors.(CA: a cancer journal for clinicians, 2021-03) Byrd, David R; Brierley, James D; Baker, Thomas P; Sullivan, Daniel C; Gress, Donna MUntil recently, cancer registries have only collected cancer clinical stage at diagnosis, before any therapy, and pathological stage after surgical resection, provided no treatment has been given before the surgery, but they have not collected stage data after neoadjuvant therapy (NAT). Because NAT is increasingly being used to treat a variety of tumors, it has become important to make the distinction between both the clinical and the pathological assessment without NAT and the assessment after NAT to avoid any misunderstanding of the significance of the clinical and pathological findings. It also is important that cancer registries collect data after NAT to assess response and effectiveness of this treatment approach on a population basis. The prefix y is used to denote stage after NAT. Currently, cancer registries of the American College of Surgeons' Commission on Cancer only partially collect y stage data, and data on the clinical response to NAT (yc or posttherapy clinical information) are not collected or recorded in a standardized fashion. In addition to NAT, nonoperative management after radiation and chemotherapy is being used with increasing frequency in rectal cancer and may be expanded to other treatment sites. Using examples from breast, rectal, and esophageal cancers, the pathological and imaging changes seen after NAT are reviewed to demonstrate appropriate staging.Item Open Access Do Radiologists Have Stage Fright? Tumor Staging and How We Can Add Value to the Care of Patients with Cancer.(Radiology, 2016-01) Glastonbury, Christine M; Bhosale, Priya R; Choyke, Peter L; D'Orsi, Carl J; Erasmus, Jeremy J; Gill, Ritu R; Mukherji, Suresh K; Panicek, David M; Schwartz, Lawrence H; Subramaniam, Rathan M; Sullivan, Daniel CItem Open Access Imaging biomarker roadmap for cancer studies.(Nature reviews. Clinical oncology, 2017-03) O'Connor, James PB; Aboagye, Eric O; Adams, Judith E; Aerts, Hugo JWL; Barrington, Sally F; Beer, Ambros J; Boellaard, Ronald; Bohndiek, Sarah E; Brady, Michael; Brown, Gina; Buckley, David L; Chenevert, Thomas L; Clarke, Laurence P; Collette, Sandra; Cook, Gary J; deSouza, Nandita M; Dickson, John C; Dive, Caroline; Evelhoch, Jeffrey L; Faivre-Finn, Corinne; Gallagher, Ferdia A; Gilbert, Fiona J; Gillies, Robert J; Goh, Vicky; Griffiths, John R; Groves, Ashley M; Halligan, Steve; Harris, Adrian L; Hawkes, David J; Hoekstra, Otto S; Huang, Erich P; Hutton, Brian F; Jackson, Edward F; Jayson, Gordon C; Jones, Andrew; Koh, Dow-Mu; Lacombe, Denis; Lambin, Philippe; Lassau, Nathalie; Leach, Martin O; Lee, Ting-Yim; Leen, Edward L; Lewis, Jason S; Liu, Yan; Lythgoe, Mark F; Manoharan, Prakash; Maxwell, Ross J; Miles, Kenneth A; Morgan, Bruno; Morris, Steve; Ng, Tony; Padhani, Anwar R; Parker, Geoff JM; Partridge, Mike; Pathak, Arvind P; Peet, Andrew C; Punwani, Shonit; Reynolds, Andrew R; Robinson, Simon P; Shankar, Lalitha K; Sharma, Ricky A; Soloviev, Dmitry; Stroobants, Sigrid; Sullivan, Daniel C; Taylor, Stuart A; Tofts, Paul S; Tozer, Gillian M; van Herk, Marcel; Walker-Samuel, Simon; Wason, James; Williams, Kaye J; Workman, Paul; Yankeelov, Thomas E; Brindle, Kevin M; McShane, Lisa M; Jackson, Alan; Waterton, John CImaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.Item Open Access 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 RadiologyExisting 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.Item Open Access 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 GItem Open Access Magnetic Resonance Biomarkers in Radiation Oncology.(Medical physics, 2021-04-17) McGee, Kiaran P; Hwang, Ken-Pin; Sullivan, Daniel C; Kurhanewicz, John; Hu, Yanle; Wang, Jihong; Li, Wen; Debbins, Josef; Paulson, Eric; Olsen, Jeffrey R; Hua, Chia-Ho; Warner, Lizette; Ma, Daniel; Moros, Eduardo; Tyagi, Neelam; Chung, CarolineA magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.Item Open Access 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 GroupAlthough 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.Item Open Access QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications.(Clinical imaging, 2021-02-25) Avila, Ricardo S; Fain, Sean B; Hatt, Chuck; Armato, Samuel G; Mulshine, James L; Gierada, David; Silva, Mario; Lynch, David A; Hoffman, Eric A; Ranallo, Frank N; Mayo, John R; Yankelevitz, David; Estepar, Raul San Jose; Subramaniam, Raja; Henschke, Claudia I; Guimaraes, Alex; Sullivan, Daniel CAs the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.Item Open Access Quantitative imaging in oncology patients: Part 1, radiology practice patterns at major U.S. cancer centers.(AJR. American journal of roentgenology, 2010-07) Jaffe, Tracy A; Wickersham, Nicholas W; Sullivan, Daniel COBJECTIVE: The objective of our study was to examine radiologists' opinions and practice patterns concerning tumor measurements in cancer patients. MATERIALS AND METHODS: An electronic mail survey was sent to 565 abdominal imaging radiologists at 55 U.S. National Cancer Institute (NCI)-funded cancer centers. The survey contained questions about departmental demographics, procedures for interpretation of imaging in oncologic patients, and opinions concerning the role of radiologists in using the Response Evaluation Criteria in Solid Tumors (RECIST) system for tumor measurements. RESULTS: Two hundred ninety-six responses (52%) were received. The distribution of the size of the respondents' abdominal imaging groups was as follows: 1-5 (16/295, 5%), 6-10 (112/295, 38%), 11-15 (77/295, 26%), and > 20 (73/295, 25%). Most respondents dictate some but not all tumor measurements in the first clinical scan (236/270, 87%). For follow-up imaging, 95% (255/268) of respondents dictate tumor measurements for selected index lesions. Most respondents believe inclusion of tumor measurements in the first scan is the responsibility of the radiologist (248/262, 95%). Ninety percent of respondents (235/261) believe inclusion of several index lesion measurements is satisfactory to document disease activity. Eighty-two percent (214/260) of respondents were familiar with RECIST. Forty-two percent (110/262) of respondents' departments have a centralized process for approval of industry-sponsored oncologic trials in which imaging is an important component of the protocol end point. CONCLUSION: Most oncologic imaging at NCI-sponsored cancer centers includes tumor measurements on initial and follow-up imaging. Very few radiology departments have a centralized process for approval of clinical trial protocols that require imaging.Item Open Access Quantitative imaging test approval and biomarker qualification: interrelated but distinct activities.(Radiology, 2011-06) Buckler, Andrew J; Bresolin, Linda; Dunnick, N Reed; Sullivan, Daniel C; Aerts, Hugo JWL; Bendriem, Bernard; Bendtsen, Claus; Boellaard, Ronald; Boone, John M; Cole, Patricia E; Conklin, James J; Dorfman, Gary S; Douglas, Pamela S; Eidsaunet, Willy; Elsinger, Cathy; Frank, Richard A; Gatsonis, Constantine; Giger, Maryellen L; Gupta, Sandeep N; Gustafson, David; Hoekstra, Otto S; Jackson, Edward F; Karam, Lisa; Kelloff, Gary J; Kinahan, Paul E; McLennan, Geoffrey; Miller, Colin G; Mozley, P David; Muller, Keith E; Patt, Rick; Raunig, David; Rosen, Mark; Rupani, Haren; Schwartz, Lawrence H; Siegel, Barry A; Sorensen, A Gregory; Wahl, Richard L; Waterton, John C; Wolf, Walter; Zahlmann, Gudrun; Zimmerman, BrianQuantitative imaging biomarkers could speed the development of new treatments for unmet medical needs and improve routine clinical care. However, it is not clear how the various regulatory and nonregulatory (eg, reimbursement) processes (often referred to as pathways) relate, nor is it clear which data need to be collected to support these different pathways most efficiently, given the time- and cost-intensive nature of doing so. The purpose of this article is to describe current thinking regarding these pathways emerging from diverse stakeholders interested and active in the definition, validation, and qualification of quantitative imaging biomarkers and to propose processes to facilitate the development and use of quantitative imaging biomarkers. A flexible framework is described that may be adapted for each imaging application, providing mechanisms that can be used to develop, assess, and evaluate relevant biomarkers. From this framework, processes can be mapped that would be applicable to both imaging product development and to quantitative imaging biomarker development aimed at increasing the effectiveness and availability of quantitative imaging.http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100800/-/DC1.Item Open Access Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs.(Journal of magnetic resonance imaging : JMRI, 2019-01-24) Keenan, Kathryn E; Biller, Joshua R; Delfino, Jana G; Boss, Michael A; Does, Mark D; Evelhoch, Jeffrey L; Griswold, Mark A; Gunter, Jeffrey L; Hinks, R Scott; Hoffman, Stuart W; Kim, Geena; Lattanzi, Riccardo; Li, Xiaojuan; Marinelli, Luca; Metzger, Gregory J; Mukherjee, Pratik; Nordstrom, Robert J; Peskin, Adele P; Perez, Elena; Russek, Stephen E; Sahiner, Berkman; Serkova, Natalie; Shukla-Dave, Amita; Steckner, Michael; Stupic, Karl F; Wilmes, Lisa J; Wu, Holden H; Zhang, Huiming; Jackson, Edward F; Sullivan, Daniel CLEVEL OF EVIDENCE:5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.Item Open Access Response to treatment series: part 1 and introduction, measuring tumor response--challenges in the era of molecular medicine.(AJR Am J Roentgenol, 2011-07) Sullivan, Daniel C; Gatsonis, ConstantineItem Open Access 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 CA 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.Item Open Access Task Group 174 Report: Utilization of [18 F]Fluorodeoxyglucose Positron Emission Tomography ([18 F]FDG-PET) in Radiation Therapy.(Medical physics, 2019-10) Das, Shiva K; McGurk, Ross; Miften, Moyed; Mutic, Sasa; Bowsher, James; Bayouth, John; Erdi, Yusuf; Mawlawi, Osama; Boellaard, Ronald; Bowen, Stephen R; Xing, Lei; Bradley, Jeffrey; Schoder, Heiko; Yin, Fang-Fang; Sullivan, Daniel C; Kinahan, PaulThe use of positron emission tomography (PET) in radiation therapy (RT) is rapidly increasing in the areas of staging, segmentation, treatment planning, and response assessment. The most common radiotracer is 18 F-fluorodeoxyglucose ([18 F]FDG), a glucose analog with demonstrated efficacy in cancer diagnosis and staging. However, diagnosis and RT planning are different endeavors with unique requirements, and very little literature is available for guiding physicists and clinicians in the utilization of [18 F]FDG-PET in RT. The two goals of this report are to educate and provide recommendations. The report provides background and education on current PET imaging systems, PET tracers, intensity quantification, and current utilization in RT (staging, segmentation, image registration, treatment planning, and therapy response assessment). Recommendations are provided on acceptance testing, annual and monthly quality assurance, scanning protocols to ensure consistency between interpatient scans and intrapatient longitudinal scans, reporting of patient and scan parameters in literature, requirements for incorporation of [18 F]FDG-PET in treatment planning systems, and image registration. The recommendations provided here are minimum requirements and are not meant to cover all aspects of the use of [18 F]FDG-PET for RT.Item Open Access The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions.(Statistical methods in medical research, 2015-02) Kessler, Larry G; Barnhart, Huiman X; Buckler, Andrew J; Choudhury, Kingshuk Roy; Kondratovich, Marina V; Toledano, Alicia; Guimaraes, Alexander R; Filice, Ross; Zhang, Zheng; Sullivan, Daniel C; QIBA Terminology Working GroupThe development and implementation of quantitative imaging biomarkers has been hampered by the inconsistent and often incorrect use of terminology related to these markers. Sponsored by the Radiological Society of North America, an interdisciplinary group of radiologists, statisticians, physicists, and other researchers worked to develop a comprehensive terminology to serve as a foundation for quantitative imaging biomarker claims. Where possible, this working group adapted existing definitions derived from national or international standards bodies rather than invent new definitions for these terms. This terminology also serves as a foundation for the design of studies that evaluate the technical performance of quantitative imaging biomarkers and for studies of algorithms that generate the quantitative imaging biomarkers from clinical scans. This paper provides examples of research studies and quantitative imaging biomarker claims that use terminology consistent with these definitions as well as examples of the rampant confusion in this emerging field. We provide recommendations for appropriate use of quantitative imaging biomarker terminological concepts. It is hoped that this document will assist researchers and regulatory reviewers who examine quantitative imaging biomarkers and will also inform regulatory guidance. More consistent and correct use of terminology could advance regulatory science, improve clinical research, and provide better care for patients who undergo imaging studies.Item Open Access The International Association for the Study of Lung Cancer Early Lung Imaging Confederation.(JCO clinical cancer informatics, 2020-02) Mulshine, James L; Avila, Ricardo S; Conley, Ed; Devaraj, Anand; Ambrose, Laurie Fenton; Flanagan, Tanya; Henschke, Claudia I; Hirsch, Fred R; Janz, Robert; Kakinuma, Ryutaro; Lam, Stephen; McWilliams, Annette; Van Ooijen, Peter MA; Oudkerk, Matthijs; Pastorino, Ugo; Reeves, Anthony; Rogalla, Patrick; Schmidt, Heidi; Sullivan, Daniel C; Wind, Haije HJ; Wu, Ning; Wynes, Murry; Xueqian, Xie; Yankelevitz, David F; Field, John KPurpose
To improve outcomes for lung cancer through low-dose computed tomography (LDCT) early lung cancer detection. The International Association for the Study of Lung Cancer is developing the Early Lung Imaging Confederation (ELIC) to serve as an open-source, international, universally accessible environment to analyze large collections of quality-controlled LDCT images and associated biomedical data for research and routine screening care.Methods
ELIC is an international confederation that allows access to efficiently analyze large numbers of high-quality computed tomography (CT) images with associated de-identified clinical information without moving primary imaging/clinical or imaging data from its local or regional site of origin. Rather, ELIC uses a cloud-based infrastructure to distribute analysis tools to the local site of the stored imaging and clinical data, thereby allowing for research and quality studies to proceed in a vendor-neutral, collaborative environment. ELIC's hub-and-spoke architecture will be deployed to permit analysis of CT images and associated data in a secure environment, without any requirement to reveal the data itself (ie, privacy protecting). Identifiable data remain under local control, so the resulting environment complies with national regulations and mitigates against privacy or data disclosure risk.Results
The goal of pilot experiments is to connect image collections of LDCT scans that can be accurately analyzed in a fashion to support a global network using methodologies that can be readily scaled to accrued databases of sufficient size to develop and validate robust quantitative imaging tools.Conclusion
This initiative can rapidly accelerate improvements to the multidisciplinary management of early, curable lung cancer and other major thoracic diseases (eg, coronary artery disease and chronic obstructive pulmonary disease) visualized on a screening LDCT scan. The addition of a facile, quantitative CT scanner image quality conformance process is a unique step toward improving the reliability of clinical decision support with CT screening worldwide.