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Chauvie S, Mazzoni LN, O’Doherty J. A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation. Tomography 2023; 9:1876-1902. [PMID: 37888741 PMCID: PMC10610870 DOI: 10.3390/tomography9050149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023] Open
Abstract
Imaging biomarkers (IBs) have been proposed in medical literature that exploit images in a quantitative way, going beyond the visual assessment by an imaging physician. These IBs can be used in the diagnosis, prognosis, and response assessment of several pathologies and are very often used for patient management pathways. In this respect, IBs to be used in clinical practice and clinical trials have a requirement to be precise, accurate, and reproducible. Due to limitations in imaging technology, an error can be associated with their value when considering the entire imaging chain, from data acquisition to data reconstruction and subsequent analysis. From this point of view, the use of IBs in clinical trials requires a broadening of the concept of quality assurance and this can be a challenge for the responsible medical physics experts (MPEs). Within this manuscript, we describe the concept of an IB, examine some examples of IBs currently employed in clinical practice/clinical trials and analyze the procedure that should be carried out to achieve better accuracy and reproducibility in their use. We anticipate that this narrative review, written by the components of the EFOMP working group on "the role of the MPEs in clinical trials"-imaging sub-group, can represent a valid reference material for MPEs approaching the subject.
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Affiliation(s)
- Stephane Chauvie
- Medical Physics Division, Santa Croce e Carle Hospital, 12100 Cuneo, Italy;
| | | | - Jim O’Doherty
- Siemens Medical Solutions, Malvern, PA 19355, USA;
- Department of Radiology & Radiological Sciences, Medical University of South Carolina, Charleston, SC 20455, USA
- Radiography & Diagnostic Imaging, University College Dublin, D04 C7X2 Dublin, Ireland
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Hubbard Cristinacce PL, Keaveney S, Aboagye EO, Hall MG, Little RA, O'Connor JPB, Parker GJM, Waterton JC, Winfield JM, Jauregui-Osoro M. Clinical translation of quantitative magnetic resonance imaging biomarkers - An overview and gap analysis of current practice. Phys Med 2022; 101:165-182. [PMID: 36055125 DOI: 10.1016/j.ejmp.2022.08.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/05/2022] [Accepted: 08/17/2022] [Indexed: 10/14/2022] Open
Abstract
PURPOSE This overview of the current landscape of quantitative magnetic resonance imaging biomarkers (qMR IBs) aims to support the standardisation of academic IBs to assist their translation to clinical practice. METHODS We used three complementary approaches to investigate qMR IB use and quality management practices within the UK: 1) a literature search of qMR and quality management terms during 2011-2015 and 2016-2020; 2) a database search for clinical research studies using qMR IBs during 2016-2020; and 3) a survey to ascertain the current availability and quality management practices for clinical MRI scanners and associated equipment at research institutions across the UK. RESULTS The analysis showed increased use of all qMR methods between the periods 2011-2015 and 2016-2020 and diffusion-tensor MRI and volumetry to be popular methods. However, the "translation ratio" of journal articles to clinical research studies was higher for qMR methods that have evidence of clinical translation via a commercial route, such as fat fraction and T2 mapping. The number of journal articles citing quality management terms doubled between the periods 2011-2015 and 2016-2020; although, its proportion relative to all journal articles only increased by 3.0%. The survey suggested that quality assurance (QA) and quality control (QC) of data acquisition procedures are under-reported in the literature and that QA/QC of acquired data/data analysis are under-developed and lack consistency between institutions. CONCLUSIONS We summarise current attempts to standardise and translate qMR IBs, and conclude by outlining the ideal quality management practices and providing a gap analysis between current practice and a metrological standard.
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Affiliation(s)
| | - Sam Keaveney
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Eric O Aboagye
- Department of Surgery & Cancer, Division of Cancer, Imperial College London, W12 0NN London, UK
| | - Matt G Hall
- National Physical Laboratory, Hampton Road, Teddington TW11 0LW, UK
| | - Ross A Little
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - James P B O'Connor
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Geoff J M Parker
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, 90 High Holborn, London WC1V 6LJ, UK; Bioxydyn Ltd, Manchester M15 6SZ, UK
| | - John C Waterton
- Bioxydyn Ltd, Manchester M15 6SZ, UK; Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Jessica M Winfield
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Maite Jauregui-Osoro
- Department of Surgery & Cancer, Division of Cancer, Imperial College London, W12 0NN London, UK
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Pang Y, Malyarenko DI, Amouzandeh G, Barberi E, Cole M, Vom Endt A, Peeters J, Tan ET, Chenevert TL. Empirical validation of gradient field models for an accurate ADC measured on clinical 3T MR systems in body oncologic applications. Phys Med 2021; 86:113-120. [PMID: 34107440 PMCID: PMC8268998 DOI: 10.1016/j.ejmp.2021.05.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 04/28/2021] [Accepted: 05/21/2021] [Indexed: 12/20/2022] Open
Abstract
PURPOSE To empirically corroborate vendor-provided gradient nonlinearity (GNL) characteristics and demonstrate efficient GNL bias correction for human brain apparent diffusion coefficient (ADC) across 3T MR systems and spatial locations. METHODS Spatial distortion vector fields (DVF) were mapped in 3D using a surface fiducial array phantom for individual gradient channels on three 3T MR platforms from different vendors. Measured DVF were converted into empirical 3D GNL tensors and compared with their theoretical counterparts derived from vendor-provided spherical harmonic (SPH) coefficients. To illustrate spatial impact of GNL on ADC, diffusion weighted imaging using three orthogonal gradient directions was performed on a volunteer brain positioned at isocenter (as a reference) and offset superiorly by 10-17 cm (>10% predicted GNL bias). The SPH tensor-based GNL correction was applied to individual DWI gradient directions, and derived ADC was compared with low-bias reference for human brain white matter (WM) ROIs. RESULTS Empiric and predicted GNL errors were comparable for all three studied 3T MR systems, with <1.0% differences in the median and width of spatial histograms for individual GNL tensor elements. Median (±width) of ADC (10-3mm2/s) histograms measured at isocenter in WM reference ROIs from three MR systems were: 0.73 ± 0.11, 0.71 ± 0.14, 0.74 ± 0.17, and at off-isocenters (before versus after GNL correction) were respectively 0.63 ± 0.14 versus 0.72 ± 0.11, 0.53 ± 0.16 versus 0.74 ± 0.18, and 0.65 ± 0.16 versus 0.76 ± 0.18. CONCLUSION The phantom-based spatial distortion measurements validated vendor-provided gradient fields, and accurate WM ADC was recovered regardless of spatial locations and clinical MR platforms using system-specific tensor-based GNL correction for routine DWI.
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Affiliation(s)
- Yuxi Pang
- Radiology, University of Michigan, Ann Arbor, MI, United States.
| | | | | | - Enzo Barberi
- Modus Medical Devices Inc., London, ON, CA, Canada
| | - Michael Cole
- Modus Medical Devices Inc., London, ON, CA, Canada
| | | | | | - Ek T Tan
- Radiology and Imaging, Hospital for Special Surgery, New York, NY, United States
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Fedeli L, Benelli M, Busoni S, Belli G, Ciccarone A, Coniglio A, Esposito M, Nocetti L, Sghedoni R, Tarducci R, Altabella L, Belligotti E, Bettarini S, Betti M, Caivano R, Carnì M, Chiappiniello A, Cimolai S, Cretti F, Fulcheri C, Gasperi C, Giacometti M, Levrero F, Lizio D, Maieron M, Marzi S, Mascaro L, Mazzocchi S, Meliadò G, Morzenti S, Niespolo A, Noferini L, Oberhofer N, Orsingher L, Quattrocchi M, Ricci A, Savini A, Taddeucci A, Testa C, Tortoli P, Gobbi G, Gori C, Bernardi L, Giannelli M, Mazzoni LN. On the dependence of quantitative diffusion-weighted imaging on scanner system characteristics and acquisition parameters: A large multicenter and multiparametric phantom study with unsupervised clustering analysis. Phys Med 2021; 85:98-106. [PMID: 33991807 DOI: 10.1016/j.ejmp.2021.04.020] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/31/2021] [Accepted: 04/23/2021] [Indexed: 11/25/2022] Open
Abstract
PURPOSE The purpose of this multicenter phantom study was to exploit an innovative approach, based on an extensive acquisition protocol and unsupervised clustering analysis, in order to assess any potential bias in apparent diffusion coefficient (ADC) estimation due to different scanner characteristics. Moreover, we aimed at assessing, for the first time, any effect of acquisition plan/phase encoding direction on ADC estimation. METHODS Water phantom acquisitions were carried out on 39 scanners. DWI acquisitions (b-value = 0-200-400-600-800-1000 s/mm2) with different acquisition plans (axial, coronal, sagittal) and phase encoding directions (anterior/posterior and right/left, for the axial acquisition plan), for 3 orthogonal diffusion weighting gradient directions, were performed. For each acquisition setup, ADC values were measured in-center and off-center (6 different positions), resulting in an entire dataset of 84 × 39 = 3276 ADC values. Spatial uniformity of ADC maps was assessed by means of the percentage difference between off-center and in-center ADC values (Δ). RESULTS No significant dependence of in-center ADC values on acquisition plan/phase encoding direction was found. Ward unsupervised clustering analysis showed 3 distinct clusters of scanners and an association between Δ-values and manufacturer/model, whereas no association between Δ-values and maximum gradient strength, slew rate or static magnetic field strength was revealed. Several acquisition setups showed significant differences among groups, indicating the introduction of different biases in ADC estimation. CONCLUSIONS Unsupervised clustering analysis of DWI data, obtained from several scanners using an extensive acquisition protocol, allows to reveal an association between measured ADC values and manufacturer/model of scanner, as well as to identify suboptimal DWI acquisition setups for accurate ADC estimation.
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Affiliation(s)
- Luca Fedeli
- S.O.C. Fisica Sanitaria Pistoia-Prato, A.U.S.L. Toscana Centro, Italy
| | - Matteo Benelli
- Bioinformatics Unit, Hospital of Prato, A.U.S.L. Toscana Centro, Italy
| | - Simone Busoni
- U.O.C. Fisica Sanitaria, A.O.U. Careggi, Firenze, Italy
| | - Giacomo Belli
- U.O.C. Fisica Sanitaria, A.O.U. Careggi, Firenze, Italy
| | | | - Angela Coniglio
- Department of Medical Physics, P.O. S. Filippo Neri, Roma, Italy
| | - Marco Esposito
- S.C. Fisica Sanitaria Firenze-Empoli, A.U.S.L. Toscana Centro, Firenze, Italy
| | - Luca Nocetti
- Servizio di Fisica Medica, A.O.U. Policlinico di Modena, Modena, Italy
| | - Roberto Sghedoni
- Fisica Medica, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | | | - Luisa Altabella
- Medical Physics Department, Hospital of Trento, APSS, Trento, Italy
| | - Eleonora Belligotti
- Fisica Medica ed Alte Tecnologie, A.O. Ospedali Riuniti Marche Nord, Pesaro, Italy
| | - Silvia Bettarini
- U.O.C. Fisica Sanitaria, A.O.U. Careggi, Firenze, Italy; Università degli Studi di Firenze, Firenze, Italy
| | - Margherita Betti
- S.O.C. Fisica Sanitaria Pistoia-Prato, A.U.S.L. Toscana Centro, Italy
| | - Rocchina Caivano
- U.O. Radioterapia Oncologica e Fisica Sanitaria, I.R.C.C.S. CROB, Rionero in Vulture (PZ), Italy
| | - Marco Carnì
- U.O.D. Fisica Sanitaria, A.O.U. Policlinico Umberto I, Roma, Italy
| | | | - Sara Cimolai
- U.O. Fisica Sanitaria, U.L.S.S. 2 Marca Trevigiana, Treviso, Italy
| | - Fabiola Cretti
- U.S.C. Fisica Sanitaria, A.O. Papa Giovanni XXIII, Bergamo, Italy
| | | | - Chiara Gasperi
- U.O.S.D. Fisica Sanitaria Arezzo, A.U.S.L. Toscana Sud Est, Arezzo, Italy
| | - Mara Giacometti
- S.O.D. Fisica Sanitaria, A.O.U. Ospedali Riuniti di Ancona, Ancona, Italy
| | - Fabrizio Levrero
- U.O. Fisica Sanitaria, Ospedale Policlinico San Martino, Genova, Italy
| | - Domenico Lizio
- Fisica Sanitaria, A.S.S.T. Grande Ospedale Metropolitano Niguarda, Milano, Italy
| | - Marta Maieron
- S.O.C. Fisica Sanitaria, A.S.U.I. Udine S. Maria della Misericordia, Udine, Italy
| | - Simona Marzi
- S.C. Laboratorio di Fisica Medica e Sistemi Esperti, Istituto Nazionale Tumori Regina Elena, Roma, Italy
| | - Lorella Mascaro
- U.O.C. Fisica Sanitaria, A.S.S.T. Spedali Civili, Brescia, Italy
| | - Silvia Mazzocchi
- S.C. Fisica Sanitaria Firenze-Empoli, A.U.S.L. Toscana Centro, Firenze, Italy
| | - Gabriele Meliadò
- U.O.C. Fisica Sanitaria, A.O.U. Integrata di Verona, Verona, Italy
| | | | - Alessandra Niespolo
- U.O.C. Fisica Sanitaria Area Nord, A.U.S.L. Toscana Nord Ovest, Lucca, Italy
| | | | - Nadia Oberhofer
- Servizio Aziendale di Fisica Sanitaria, A.S. dell'Alto Adige, Bolzano, Italy
| | - Laura Orsingher
- U.O. Fisica Sanitaria, U.L.S.S. 2 Marca Trevigiana, Treviso, Italy
| | | | | | - Alessandro Savini
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | | | - Claudia Testa
- Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy
| | - Paolo Tortoli
- U.O.C. Fisica Sanitaria, A.O.U. Careggi, Firenze, Italy; Università degli Studi di Firenze, Firenze, Italy
| | - Gianni Gobbi
- Università degli Studi di Perugia, Perugia, Italy
| | - Cesare Gori
- Università degli Studi di Firenze, Firenze, Italy
| | - Luca Bernardi
- S.O.C. Fisica Sanitaria Pistoia-Prato, A.U.S.L. Toscana Centro, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy.
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