Browsing by Author "Boellaard, Ronald"

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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.
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 C
Imaging 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.
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 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.
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, Brian
Quantitative 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.
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, Paul
The 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.
Open Access
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 (SUV_max) is measurable from FDG PET/CT with a within-subject coefficient of variation of 10%-12%. Second, a measured increase in SUV_max 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.