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Aldawsari AM, Al-Qaisieh B, Broadbent DA, Bird D, Murray L, Speight R. The role and potential of using quantitative MRI biomarkers for imaging guidance in brain cancer radiotherapy treatment planning: A systematic review. Phys Imaging Radiat Oncol 2023; 27:100476. [PMID: 37565088 PMCID: PMC10410581 DOI: 10.1016/j.phro.2023.100476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 07/15/2023] [Accepted: 07/19/2023] [Indexed: 08/12/2023] Open
Abstract
Background and purpose Improving the accuracy of brain tumour radiotherapy (RT) treatment planning is important to optimise patient outcomes. This systematic review investigates primary studies providing clinical evidence for the integration of quantitative magnetic resonance imaging (qMRI) biomarkers and MRI radiomics to optimise brain tumour RT planning. Materials and methods PubMed, Scopus, Embase and Web of Science databases were searched for all years until June 21, 2022. The search identified original articles demonstrating clinical evidence for the use of qMRI biomarkers and MRI radiomics for the optimization of brain cancer RT planning. Relevant information was extracted and tabulated, including qMRI metrics and techniques, impact on RT plan optimization and changes in target and normal tissue contouring and dose distribution. Results Nineteen articles met the inclusion criteria. Studies were grouped according to the qMRI biomarkers into: 1) diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI; five studies); 2) diffusion tensor imaging (DTI; seven studies); and 3) MR spectroscopic imaging (MRSI; seven studies). No relevant MRI-based radiomics studies were identified. Integration of DTI maps offers the potential for improved organs at risk (OAR) sparing. MRSI metabolic maps are a promising technique for improving delineation accuracy in terms of heterogeneity and infiltration, with OAR sparing. No firm conclusions could be drawn regarding the integration of DWI metrics and PWI maps. Conclusions Integration of qMRI metrics into RT planning offers the potential to improve delineation and OAR sparing. Clinical trials and consensus guidelines are required to demonstrate the clinical benefits of such approaches.
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Affiliation(s)
- Abeer M. Aldawsari
- Leeds Institute of Cardiovascular & Metabolic Medicine (LICAMM), University of Leeds, Woodhouse, Leeds LS2 9JT, United Kingdom
- Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, Riyadh 12371, Saudi Arabia
| | - Bashar Al-Qaisieh
- Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, United Kingdom
| | - David A. Broadbent
- Leeds Institute of Cardiovascular & Metabolic Medicine (LICAMM), University of Leeds, Woodhouse, Leeds LS2 9JT, United Kingdom
- Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, United Kingdom
| | - David Bird
- Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, United Kingdom
| | - Louise Murray
- Department of Clinical Oncology, Leeds Teaching Hospitals NHS Trust, St James’s University Hospital, Leeds LS9 7LP, United Kingdom
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Richard Speight
- Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, United Kingdom
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Salvestrini V, Greco C, Guerini AE, Longo S, Nardone V, Boldrini L, Desideri I, De Felice F. The role of feature-based radiomics for predicting response and radiation injury after stereotactic radiation therapy for brain metastases: A critical review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). Transl Oncol 2021; 15:101275. [PMID: 34800918 PMCID: PMC8605350 DOI: 10.1016/j.tranon.2021.101275] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/04/2021] [Indexed: 12/15/2022] Open
Abstract
Introduction differential diagnosis of tumor recurrence and radiation injury after stereotactic radiotherapy (SRT) is challenging. The advances in imaging techniques and feature-based radiomics could aid to discriminate radionecrosis from progression. Methods we performed a systematic review of current literature, key references were obtained from a PubMed query. Data extraction was performed by 3 researchers and disagreements were resolved with a discussion among the authors. Results we identified 15 retrospective series, one prospective trial, one critical review and one editorial paper. Radiomics involves a wide range of imaging features referred to necrotic regions, rate of contrast-enhancing area or the measure of edema surrounding the metastases. Features were mainly defined through a multistep extraction/reduction/selection process and a final validation and comparison. Conclusions feature-based radiomics has an optimal potential to accurately predict response and radionecrosis after SRT of BM and facilitate differential diagnosis. Further validation studies are eagerly awaited to confirm radiomics reliability.
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Affiliation(s)
- Viola Salvestrini
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Carlo Greco
- Radiation Oncology, Campus Bio-Medico University of Rome, Rome, Italy.
| | - Andrea Emanuele Guerini
- Radiation Oncology Department, Università degli Studi di Brescia and ASST Spedali Civili, Piazzale Spedali Civili 1, Brescia 25123, Italy.
| | - Silvia Longo
- Radiation Oncology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Largo Agostino Gemelli 8, Rome 00168, Italy.
| | - Valerio Nardone
- Section of Radiology and Radiotherapy, Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples 80138, Italy.
| | - Luca Boldrini
- Radiation Oncology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Largo Agostino Gemelli 8, Rome 00168, Italy.
| | - Isacco Desideri
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy.
| | - Francesca De Felice
- Radiation Oncology, Policlinico Umberto I "Sapienza" University of Rome, Viale Regina Elena 326, Rome 00161, Italy.
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Li M, Zhang Q, Yang K. Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment. Front Oncol 2021; 11:645177. [PMID: 34513659 PMCID: PMC8429950 DOI: 10.3389/fonc.2021.645177] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 07/30/2021] [Indexed: 02/05/2023] Open
Abstract
Advances in radiation technology, such as intensity-modulated radiation therapy (IMRT), have largely enabled a biological dose escalation of the target volume (TV) and reduce the dose to adjacent tissues or organs at risk (OARs). However, the risk of radiation-induced injury increases as more radiation dose utilized during radiation therapy (RT), which predominantly limits further increases in TV dose distribution and reduces the local control rate. Thus, the accurate target delineation is crucial. Recently, technological improvements for precise target delineation have obtained more attention in the field of RT. The addition of functional imaging to RT can provide a more accurate anatomy of the tumor and normal tissues (such as location and size), along with biological information that aids to optimize the therapeutic index (TI) of RT. In this review, we discuss the application of some common MRI-based functional imaging techniques in clinical practice. In addition, we summarize the main challenges and prospects of these imaging technologies, expecting more inspiring developments and more productive research paths in the near future.
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Affiliation(s)
- Mei Li
- Department of Gynecology and Obstetrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Qin Zhang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Kaixuan Yang
- Department of Gynecology and Obstetrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
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de Causans A, Carré A, Roux A, Tauziède-Espariat A, Ammari S, Dezamis E, Dhermain F, Reuzé S, Deutsch E, Oppenheim C, Varlet P, Pallud J, Edjlali M, Robert C. Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis. Front Oncol 2021; 11:638262. [PMID: 34327133 PMCID: PMC8315001 DOI: 10.3389/fonc.2021.638262] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 06/17/2021] [Indexed: 01/06/2023] Open
Abstract
Objectives To differentiate Glioblastomas (GBM) and Brain Metastases (BM) using a radiomic features-based Machine Learning (ML) classifier trained from post-contrast three-dimensional T1-weighted (post-contrast 3DT1) MR imaging, and compare its performance in medical diagnosis versus human experts, on a testing cohort. Methods We enrolled 143 patients (71 GBM and 72 BM) in a retrospective bicentric study from January 2010 to May 2019 to train the classifier. Post-contrast 3DT1 MR images were performed on a 3-Tesla MR unit and 100 radiomic features were extracted. Selection and optimization of the Machine Learning (ML) classifier was performed using a nested cross-validation. Sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were calculated as performance metrics. The model final performance was cross-validated, then evaluated on a test set of 37 patients, and compared to human blind reading using a McNemar’s test. Results The ML classifier had a mean [95% confidence interval] sensitivity of 85% [77; 94], a specificity of 87% [78; 97], a balanced accuracy of 86% [80; 92], and an AUC of 92% [87; 97] with cross-validation. Sensitivity, specificity, balanced accuracy and AUC were equal to 75, 86, 80 and 85% on the test set. Sphericity 3D radiomic index highlighted the highest coefficient in the logistic regression model. There were no statistical significant differences observed between the performance of the classifier and the experts’ blinded examination. Conclusions The proposed diagnostic support system based on radiomic features extracted from post-contrast 3DT1 MR images helps in differentiating solitary BM from GBM with high diagnosis performance and generalizability.
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Affiliation(s)
- Alix de Causans
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | - Alexandre Carré
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Alexandre Roux
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Arnault Tauziède-Espariat
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neuropathologie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Samy Ammari
- Département de Radiologie, Gustave Roussy, Université Paris Saclay, Villejuif, France.,BioMaps UMR1281, Université Paris-Saclay, CNRS, INSERM, CEA, Orsay, France
| | - Edouard Dezamis
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Frederic Dhermain
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Sylvain Reuzé
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Eric Deutsch
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Catherine Oppenheim
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | | | - Johan Pallud
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Myriam Edjlali
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | - Charlotte Robert
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
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Civita P, Valerio O, Naccarato AG, Gumbleton M, Pilkington GJ. Satellitosis, a Crosstalk between Neurons, Vascular Structures and Neoplastic Cells in Brain Tumours; Early Manifestation of Invasive Behaviour. Cancers (Basel) 2020; 12:E3720. [PMID: 33322379 PMCID: PMC7763100 DOI: 10.3390/cancers12123720] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 11/28/2020] [Accepted: 12/04/2020] [Indexed: 01/06/2023] Open
Abstract
The secondary structures of Scherer commonly known as perineuronal and perivascular satellitosis have been identified as a histopathological hallmark of diffuse, invasive, high-grade gliomas. They are recognised as perineuronal satellitosis when clusters of neoplastic glial cells surround neurons cell bodies and perivascular satellitosis when such tumour cells surround blood vessels infiltrating Virchow-Robin spaces. In this review, we provide an overview of emerging knowledge regarding how interactions between neurons and glioma cells can modulate tumour evolution and how neurons play a key role in glioma growth and progression, as well as the role of perivascular satellitosis into mechanisms of glioma cells spread. At the same time, we review the current knowledge about the role of perineuronal satellitosis and perivascular satellitosis within the tumour microenvironment (TME), in order to highlight critical knowledge gaps in research space.
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Affiliation(s)
- Prospero Civita
- Brain Tumour Research Centre, Institute of Biological and Biomedical Sciences (IBBS), School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth PO1 2DT, UK
- School of Pharmacy and Pharmaceutical Sciences, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF10 3NB, UK;
| | - Ortenzi Valerio
- Department of Translational Research and New Technologies in Medicine and Surgery, Pisa University Hospital, 56100 Pisa, Italy; (O.V.); (A.G.N.)
| | - Antonio Giuseppe Naccarato
- Department of Translational Research and New Technologies in Medicine and Surgery, Pisa University Hospital, 56100 Pisa, Italy; (O.V.); (A.G.N.)
| | - Mark Gumbleton
- School of Pharmacy and Pharmaceutical Sciences, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF10 3NB, UK;
| | - Geoffrey J. Pilkington
- Brain Tumour Research Centre, Institute of Biological and Biomedical Sciences (IBBS), School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth PO1 2DT, UK
- School of Pharmacy and Pharmaceutical Sciences, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF10 3NB, UK;
- Division of Neuroscience, Department of Basic and Clinical Neuroscience, Institute of Psychiatry & Neurology, King’s College London, London SE5 9RX, UK
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