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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Messiou C, Lee R, Salto-Tellez M. Multimodal analysis and the oncology patient: Creating a hospital system for integrated diagnostics and discovery. Comput Struct Biotechnol J 2023; 21:4536-4539. [PMID: 37767106 PMCID: PMC10520501 DOI: 10.1016/j.csbj.2023.09.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
We propose that an information technology and computational framework that would unify access to hospital digital information silos, and enable integration of this information using machine learning methods, would bring a new paradigm to patient management and research. This is the core principle of Integrated Diagnostics (ID): the amalgamation of multiple analytical modalities, with evolved information technology, applied to a defined patient cohort, and resulting in a synergistic effect in the clinical value of the individual diagnostic tools. This has the potential to transform the practice of personalized oncology at a time at which it is very much needed. In this article we present different models from the literature that contribute to the vision of ID and we provide published exemplars of ID tools. We briefly describe ongoing efforts within a universal healthcare system to create national clinical datasets. Following this, we argue the case to create "hospital units" to leverage this multi-modal analysis, data integration and holistic clinical decision-making. Finally, we describe the joint model created in our institutions.
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Affiliation(s)
- Christina Messiou
- Imaging and Data Science Theme lead and Director of the Imaging AI hub at The Royal Marsden and Institute of Cancer Research, National Institute for Health Research Biomedical Research Centre, Sutton SM2 5PT, UK
| | - Richard Lee
- Consultant Respiratory Physician & Champion for Early Diagnosis Early Diagnosis and Detection Centre, NIHR Biomedical Research Centre at the Royal Marsden and ICR, National Heart and Lung Institute, Imperial College London, UK
| | - Manuel Salto-Tellez
- The Integrated Pathology Unit, the Institute of Cancer Research & The Royal Marsden Hospital, Sutton SM2 5PT, UK
- Precision Medicine Centre, Queen’s University Belfast, UK
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3
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Nordio G, Easmin R, Giacomel A, Dipasquale O, Martins D, Williams S, Turkheimer F, Howes O, Veronese M, Jauhar S, Rogdaki M, McCutcheon R, Kaar S, Vano L, Rutigliano G, Angelescu I, Borgan F, D’Ambrosio E, Dahoun T, Kim E, Kim S, Bloomfield M, Egerton A, Demjaha A, Bonoldi I, Nosarti C, Maccabe J, McGuire P, Matthews J, Talbot PS. An automatic analysis framework for FDOPA PET neuroimaging. J Cereb Blood Flow Metab 2023; 43:1285-1300. [PMID: 37026455 PMCID: PMC10369152 DOI: 10.1177/0271678x231168687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 01/23/2023] [Accepted: 02/05/2023] [Indexed: 04/08/2023]
Abstract
In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables and processing parameters. An instance of XNAT imaging platform was used to store the King's College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. The final data repository includes 892 FDOPA PET scans organized from 23 different studies. We found good reproducibility of the data analysis by the automated pipeline (in the striatum for the Kicer: for the controls ICC = 0.71, for the psychotic patients ICC = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p < 0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid resourse for standardised and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size.
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Affiliation(s)
- Giovanna Nordio
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Rubaida Easmin
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Alessio Giacomel
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Steven Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Oliver Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- MRC London Institute of Medical Sciences, Hammersmith Hospital, London, UK
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, UK
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Information Engineering (DEI), University of Padua, Padua, Italy
| | - and the FDOPA PET imaging working group:
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- MRC London Institute of Medical Sciences, Hammersmith Hospital, London, UK
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, UK
- Department of Information Engineering (DEI), University of Padua, Padua, Italy
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College, Imperial College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
- COMPASS Pathways plc, London, UK
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari "Aldo Moro", Bari, Italy
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
- Department of Psychiatry, Seoul National University Bundang Hospital, Gyeonggi-do, Republic of Korea
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Brain & Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
- Division of Psychiatry, Faculty of Brain Sciences, University College of London, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neurosicences, King’s College London, London, UK
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, UK
- Early Intervention Psychosis Clinical Academic Group, South London & Maudsley NHS Trust, London, UK
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Sameer Jauhar
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College, Imperial College London, London, UK
| | - Maria Rogdaki
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Robert McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College, Imperial College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Stephen Kaar
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, UK
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Luke Vano
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London, UK
| | - Grazia Rutigliano
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London, UK
| | - Ilinca Angelescu
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Faith Borgan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- COMPASS Pathways plc, London, UK
| | - Enrico D’Ambrosio
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari "Aldo Moro", Bari, Italy
| | - Tarik Dahoun
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College, Imperial College London, London, UK
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Euitae Kim
- Department of Psychiatry, Seoul National University Bundang Hospital, Gyeonggi-do, Republic of Korea
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Brain & Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
| | - Seoyoung Kim
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Micheal Bloomfield
- Division of Psychiatry, Faculty of Brain Sciences, University College of London, London, UK
| | - Alice Egerton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Arsime Demjaha
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Ilaria Bonoldi
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Chiara Nosarti
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neurosicences, King’s College London, London, UK
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, UK
| | - James Maccabe
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Early Intervention Psychosis Clinical Academic Group, South London & Maudsley NHS Trust, London, UK
| | - Julian Matthews
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Peter S Talbot
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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5
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Ramlee S, Hulse D, Bernatowicz K, Pérez-lópez R, Sala E, Aloj L. Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study. Cancers (Basel) 2022; 14:3656. [PMID: 35954318 PMCID: PMC9367613 DOI: 10.3390/cancers14153656] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/23/2022] [Accepted: 07/25/2022] [Indexed: 02/04/2023] Open
Abstract
The tumour immune microenvironment influences the efficacy of immune checkpoint inhibitors. Within this microenvironment are CD8-expressing tumour-infiltrating lymphocytes (CD8+ TILs), which are an important mediator and marker of anti-tumour response. In practice, the assessment of CD8+ TILs via tissue sampling involves logistical challenges. Radiomics, the high-throughput extraction of features from medical images, may offer a novel and non-invasive alternative. We performed a systematic review of the available literature reporting radiomic signatures associated with CD8+ TILs. We also aimed to evaluate the methodological quality of the identified studies using the Radiomics Quality Score (RQS) tool, and the risk of bias and applicability with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Articles were searched from inception until 31 December 2021, in three electronic databases, and screened against eligibility criteria. Twenty-seven articles were included. A wide variety of cancers have been studied. The reported radiomic signatures were heterogeneous, with very limited reproducibility between studies of the same cancer group. The overall quality of studies was found to be less than desirable (mean RQS = 33.3%), indicating a need for technical maturation. Some potential avenues for further investigation are also discussed.
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7
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Gallez B. The Role of Imaging Biomarkers to Guide Pharmacological Interventions Targeting Tumor Hypoxia. Front Pharmacol 2022; 13:853568. [PMID: 35910347 PMCID: PMC9335493 DOI: 10.3389/fphar.2022.853568] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 06/23/2022] [Indexed: 12/12/2022] Open
Abstract
Hypoxia is a common feature of solid tumors that contributes to angiogenesis, invasiveness, metastasis, altered metabolism and genomic instability. As hypoxia is a major actor in tumor progression and resistance to radiotherapy, chemotherapy and immunotherapy, multiple approaches have emerged to target tumor hypoxia. It includes among others pharmacological interventions designed to alleviate tumor hypoxia at the time of radiation therapy, prodrugs that are selectively activated in hypoxic cells or inhibitors of molecular targets involved in hypoxic cell survival (i.e., hypoxia inducible factors HIFs, PI3K/AKT/mTOR pathway, unfolded protein response). While numerous strategies were successful in pre-clinical models, their translation in the clinical practice has been disappointing so far. This therapeutic failure often results from the absence of appropriate stratification of patients that could benefit from targeted interventions. Companion diagnostics may help at different levels of the research and development, and in matching a patient to a specific intervention targeting hypoxia. In this review, we discuss the relative merits of the existing hypoxia biomarkers, their current status and the challenges for their future validation as companion diagnostics adapted to the nature of the intervention.
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8
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Arthur A, Johnston EW, Winfield JM, Blackledge MD, Jones RL, Huang PH, Messiou C. Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We? Front Oncol 2022; 12:892620. [PMID: 35847882 PMCID: PMC9286756 DOI: 10.3389/fonc.2022.892620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 05/31/2022] [Indexed: 12/13/2022] Open
Abstract
A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver “virtual biopsies” within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.
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Affiliation(s)
- Amani Arthur
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Edward W. Johnston
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Jessica M. Winfield
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Matthew D. Blackledge
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Robin L. Jones
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- Division of Clinical Studies, The Institute of Cancer Research, London, United Kingdom
| | - Paul H. Huang
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton, United Kingdom
- *Correspondence: Paul H. Huang, ; Christina Messiou,
| | - Christina Messiou
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- *Correspondence: Paul H. Huang, ; Christina Messiou,
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Doran SJ, Al Sa’d M, Petts JA, Darcy J, Alpert K, Cho W, Sanchez LE, Alle S, El Harouni A, Genereaux B, Ziegler E, Harris GJ, Aboagye EO, Sala E, Koh DM, Marcus D. Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies. Tomography 2022; 8:497-512. [PMID: 35202205 PMCID: PMC8875191 DOI: 10.3390/tomography8010040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/03/2022] [Accepted: 02/05/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a "smart CT" paintbrush tool; the integration of NVIDIA's Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions: The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future.
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Affiliation(s)
- Simon J. Doran
- Division of Radiotherapy and Imaging, Institute of Cancer Research, 15 Cotswold Rd, London SM2 5NG, UK;
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
| | - Mohammad Al Sa’d
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College, London SW7 2AZ, UK
| | - James A. Petts
- Ovela Solutions Ltd., 20-22 Wenlock Road, London N1 7GU, UK;
| | - James Darcy
- Division of Radiotherapy and Imaging, Institute of Cancer Research, 15 Cotswold Rd, London SM2 5NG, UK;
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
| | - Kate Alpert
- Flywheel LLC, 1015 Glenwood Ave, Suite 300, Minneapolis, MN 55405, USA; (K.A.); (D.M.)
| | - Woonchan Cho
- Neuroimaging Informatics Analysis Center, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA;
| | - Lorena Escudero Sanchez
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
- Department of Radiology, University of Cambridge, Hills Rd, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Sachidanand Alle
- NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051, USA; (S.A.); (A.E.H.); (B.G.)
| | - Ahmed El Harouni
- NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051, USA; (S.A.); (A.E.H.); (B.G.)
| | - Brad Genereaux
- NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051, USA; (S.A.); (A.E.H.); (B.G.)
| | - Erik Ziegler
- Open Health Imaging Foundation, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA; (E.Z.); (G.J.H.)
- Radical Imaging LLC, 188 Annie Moore Rd, Bolton, MA 01740-1140, USA
| | - Gordon J. Harris
- Open Health Imaging Foundation, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA; (E.Z.); (G.J.H.)
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
- Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
| | - Eric O. Aboagye
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College, London SW7 2AZ, UK
| | - Evis Sala
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
- Department of Radiology, University of Cambridge, Hills Rd, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Dow-Mu Koh
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
- Department of Radiology, Royal Marsden Hospital, Downs Rd, Sutton SM2 5PT, UK
| | - Dan Marcus
- Flywheel LLC, 1015 Glenwood Ave, Suite 300, Minneapolis, MN 55405, USA; (K.A.); (D.M.)
- Neuroimaging Informatics Analysis Center, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA;
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