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Pang Y, Wang H, Li H. Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy. Front Oncol 2022; 11:764665. [PMID: 35111666 PMCID: PMC8801459 DOI: 10.3389/fonc.2021.764665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 11/29/2021] [Indexed: 12/22/2022] Open
Abstract
Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.
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Affiliation(s)
- Yaru Pang
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Hui Wang
- Department of Chemical Engineering, University College London, London, United Kingdom
| | - He Li
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
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2
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Tang PLY, Méndez Romero A, Jaspers JPM, Warnert EAH. The potential of advanced MR techniques for precision radiotherapy of glioblastoma. MAGMA (NEW YORK, N.Y.) 2022; 35:127-143. [PMID: 35129718 PMCID: PMC8901515 DOI: 10.1007/s10334-021-00997-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 06/14/2023]
Abstract
As microscopic tumour infiltration of glioblastomas is not visible on conventional magnetic resonance (MR) imaging, an isotropic expansion of 1-2 cm around the visible tumour is applied to define the clinical target volume for radiotherapy. An opportunity to visualize microscopic infiltration arises with advanced MR imaging. In this review, various advanced MR biomarkers are explored that could improve target volume delineation for radiotherapy of glioblastomas. Various physiological processes in glioblastomas can be visualized with different advanced MR techniques. Combining maps of oxygen metabolism (CMRO2), relative cerebral blood volume (rCBV), vessel size imaging (VSI), and apparent diffusion coefficient (ADC) or amide proton transfer (APT) can provide early information on tumour infiltration and high-risk regions of future recurrence. Oxygen consumption is increased 6 months prior to tumour progression being visible on conventional MR imaging. However, presence of the Warburg effect, marking a switch from an infiltrative to a proliferative phenotype, could result in CMRO2 to appear unaltered in high-risk regions. Including information on biomarkers representing angiogenesis (rCBV and VSI) and hypercellularity (ADC) or protein concentration (APT) can omit misinterpretation due to the Warburg effect. Future research should evaluate these biomarkers in radiotherapy planning to explore the potential of advanced MR techniques to personalize target volume delineation with the aim to improve local tumour control and/or reduce radiation-induced toxicity.
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Affiliation(s)
- Patrick L Y Tang
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands.
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
| | - Alejandra Méndez Romero
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Jaap P M Jaspers
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Esther A H Warnert
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
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Is Diffusion Tensor Imaging-Guided Radiotherapy the New State-of-the-Art? A Review of the Current Literature and Technical Insights. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12020816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Despite the increasing precision of radiotherapy delivery, it is still frequently associated with neurological complications. This is in part due to damage to eloquent white matter (WM) tracts, which is made more likely by the fact they cannot be visualised on standard structural imaging. WM is additionally more vulnerable than grey matter to radiation damage. Primary brain malignancies also are known to spread along the WM. Diffusion tensor imaging (DTI) is the only in vivo method of delineating WM tracts. DTI is an imaging technique that models the direction of diffusion and therefore can infer the orientation of WM fibres. This review article evaluates the current evidence for using DTI to guide intracranial radiotherapy and whether it constitutes a new state-of-the-art technique. We provide a basic overview of DTI and its known applications in radiotherapy, which include using tractography to reduce the radiation dose to eloquent WM tracts and using DTI to detect or predict tumoural spread. We evaluate the evidence for DTI-guided radiotherapy in gliomas, metastatic disease, and benign conditions, finding that the strongest evidence is for its use in arteriovenous malformations. However, the evidence is weak in other conditions due to a lack of case-controlled trials.
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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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Knobe S, Dzierma Y, Wenske M, Berdel C, Fleckenstein J, Melchior P, Palm J, Nuesken FG, Hunt A, Engwer C, Surulescu C, Yilmaz U, Reith W, Rübe C. Feasibility and clinical usefulness of modelling glioblastoma migration in adjuvant radiotherapy. Z Med Phys 2021; 32:149-158. [PMID: 33966944 PMCID: PMC9948823 DOI: 10.1016/j.zemedi.2021.03.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 03/01/2021] [Accepted: 03/18/2021] [Indexed: 11/16/2022]
Abstract
Glioblastoma (GBM) is one of the most common primary brain tumours in adults, with a dismal prognosis despite aggressive multimodality treatment by a combination of surgery and adjuvant radiochemotherapy. A detailed knowledge of the spreading of glioma cells in the brain might allow for more targeted escalated radiotherapy, aiming to reduce locoregional relapse. Recent years have seen the development of a large variety of mathematical modelling approaches to predict glioma migration. The aim of this study is hence to evaluate the clinical applicability of a detailed micro- and meso-scale mathematical model in radiotherapy. First and foremost, a clinical workflow is established, in which the tumour is automatically segmented as input data and then followed in time mathematically based on the diffusion tensor imaging data. The influence of several free model parameters is individually evaluated, then the full model is retrospectively validated for a collective of 3 GBM patients treated at our institution by varying the most important model parameters to achieve optimum agreement with the tumour development during follow-up. Agreement of the model predictions with the real tumour growth as defined by manual contouring based on the follow-up MRI images is analyzed using the dice coefficient. The tumour evolution over 103-212 days follow-up could be predicted by the model with a dice coefficient better than 60% for all three patients. In all cases, the final tumour volume was overestimated by the model by a factor between 1.05 and 1.47. To evaluate the quality of the agreement between the model predictions and the ground truth, we must keep in mind that our gold standard relies on a single observer's (CB) manually-delineated tumour contours. We therefore decided to add a short validation of the stability and reliability of these contours by an inter-observer analysis including three other experienced radiation oncologists from our department. In total, a dice coefficient between 63% and 89% is achieved between the four different observers. Compared with this value, the model predictions (62-66%) perform reasonably well, given the fact that these tumour volumes were created based on the pre-operative segmentation and DTI.
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Affiliation(s)
- Sven Knobe
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany.
| | - Yvonne Dzierma
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Michael Wenske
- Institute for Analysis and Numerics, University of Muenster, Muenster, Germany
| | - Christian Berdel
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Jochen Fleckenstein
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Patrick Melchior
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Jan Palm
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Frank G. Nuesken
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | | | - Christian Engwer
- Institute for Analysis and Numerics, University of Muenster, Muenster, Germany
| | - Christina Surulescu
- Felix Klein Centre for Mathematics, University of Kaiserslautern, Kaiserslautern, Germany
| | - Umut Yilmaz
- Department of Diagnostic and Interventional Radiology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Wolfgang Reith
- Department of Diagnostic and Interventional Radiology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Christian Rübe
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
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Jaspers JPM, Méndez Romero A, Wiggenraad R, Compter I, Eekers DBP, Nout RA, van den Bent M. Pattern of failure in IDH mutated, low grade glioma after radiotherapy - Implications for margin reduction. Radiother Oncol 2020; 156:43-48. [PMID: 33245948 DOI: 10.1016/j.radonc.2020.11.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/26/2020] [Accepted: 11/16/2020] [Indexed: 10/22/2022]
Affiliation(s)
- J P M Jaspers
- Department of Radiotherapy, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
| | - A Méndez Romero
- Department of Radiotherapy, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - R Wiggenraad
- Department of Radiotherapy, Haaglanden Medisch Centrum, Leidschendam, the Netherlands
| | - I Compter
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, the Netherlands
| | - D B P Eekers
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, the Netherlands
| | - R A Nout
- Department of Radiotherapy, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - M van den Bent
- Department of Neuro-Oncology/Neurology, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, Rotterdam, the Netherlands
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Affiliation(s)
- Jens Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Ludvig Paul Muren
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Morten Høyer
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Cai Grau
- Department of Oncology and Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
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