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Mesny E, Leporq B, Chapet O, Beuf O. Intravoxel incoherent motion magnetic resonance imaging to assess early tumor response to radiation therapy: Review and future directions. Magn Reson Imaging 2024; 108:129-137. [PMID: 38354843 DOI: 10.1016/j.mri.2024.02.008] [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: 04/20/2023] [Revised: 02/08/2024] [Accepted: 02/10/2024] [Indexed: 02/16/2024]
Abstract
Early prediction of radiation response by imaging is a dynamic field of research and it can be obtained using a variety of noninvasive magnetic resonance imaging methods. Recently, intravoxel incoherent motion (IVIM) has gained interest in cancer imaging. IVIM carries both diffusion and perfusion information, making it a promising tool to assess tumor response. Here, we briefly introduced the basics of IVIM, reviewed existing studies of IVIM in various type of tumors during radiotherapy in order to show whether IVIM is a useful technique for an early assessment of radiation response. 31/40 studies reported an increase of IVIM parameters during radiotherapy compared to baseline. In 27 studies, this increase was higher in patients with good response to radiotherapy. Future directions including implementation of IVIM on MR-Linac and its limitation are discussed. Obtaining new radiologic biomarkers of radiotherapy response could open the way for a more personalized, biology-guided radiation therapy.
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Affiliation(s)
- Emmanuel Mesny
- Radiation Oncology Department, Center Hospitalier Lyon Sud, Pierre Benite, France; Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France.
| | - Benjamin Leporq
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France
| | - Olivier Chapet
- Radiation Oncology Department, Center Hospitalier Lyon Sud, Pierre Benite, France
| | - Olivier Beuf
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France
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Mesny E, Leporq B, Chapet O, Beuf O. Towards tumour hypoxia imaging: Incorporating relative oxygen extraction fraction mapping of prostate with multi-parametric quantitative MRI on a 1.5T MR-linac. J Med Imaging Radiat Oncol 2024. [PMID: 38415384 DOI: 10.1111/1754-9485.13626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 02/03/2024] [Indexed: 02/29/2024]
Abstract
Hypoxia plays a central role in tumour radioresistance. Reliable tumour hypoxia imaging would allow the monitoring of tumour response and a more personalized adaptation of radiotherapy planning. Here, we showed a proof of concept of the feasibility and repeatability of relative oxygen extraction fraction (rOEF) mapping of prostate using multi-parametric quantitative MRI (qMRI) achieved for the first time on a 1.5T MR-linac. T2, T2* relaxation times maps, and intra-voxel incoherent motion (IVIM) parametric maps mapping were computed on a 29 years old healthy volunteer. R2' and rOEF maps were calculated based on a multi-parametric model. Long-term repeatability and repeatability coefficient (RC) were determined for each parameter according to QIBA recommendations. Mean values for the entire healthy prostate were 0.99 ± 0.14 × 10-3 mm/s2 , 81 ± 2.1 × 10-3 mm/s2 , 21.6 ± 3.6%, 92.7 ± 19.7 ms and 62.4 ± 17.3 ms for Dslow , Dfast , f, T2 and T2*, respectively. R2' and rOEF in the prostate were 6.1 ± 3.4 s-1 and 18.2 ± 10.1% respectively. The RC of rOEF was 4.43%. Long-term repeatability of quantitative parameters based on a test-retest ranged from 2 to 18%. qMRI parameters are measurable and repeatable on 1.5T MR LINAC. From T2, T2* and IVIM parameters maps, we were able to obtain a rOEF mapping of the prostate. These results are the first step to a non-invasive imaging of tumour hypoxia during radiotherapy leading to a biological image-guided adaptive radiotherapy.
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Affiliation(s)
- Emmanuel Mesny
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France
- Radiation Oncology Department, Center Hospitalier Lyon Sud, Lyon, France
| | - Benjamin Leporq
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France
| | - Olivier Chapet
- Radiation Oncology Department, Center Hospitalier Lyon Sud, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
| | - Olivier Beuf
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France
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3
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Habrich J, Boeke S, Fritz V, Koerner E, Nikolaou K, Schick F, Gani C, Zips D, Thorwarth D. Reproducibility of diffusion-weighted magnetic resonance imaging in head and neck cancer assessed on a 1.5 T MR-Linac and comparison to parallel measurements on a 3 T diagnostic scanner. Radiother Oncol 2024; 191:110046. [PMID: 38070687 DOI: 10.1016/j.radonc.2023.110046] [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: 05/25/2023] [Revised: 11/27/2023] [Accepted: 12/03/2023] [Indexed: 02/02/2024]
Abstract
BACKGROUND AND PURPOSE Before quantitative imaging biomarkers (QIBs) acquired with magnetic resonance imaging (MRI) can be used for interventional trials in radiotherapy (RT), technical validation of these QIBs is necessary. The aim of this study was to assess the reproducibility of apparent diffusion coefficient (ADC) values, derived from diffusion-weighted (DW) MRI, in head and neck cancer using a 1.5 T MR-Linac (MRL) by comparison to a 3 T diagnostic scanner (DS). MATERIAL AND METHODS DW-MRIs were acquired on MRL and DS for 15 head and neck cancer patients before RT and in week 2 and rigidly registered to the planning computed tomography. Mean ADC values were calculated for submandibular (SG) and parotid (PG) glands as well as target volumes (TV, gross tumor volume and lymph nodes), which were delineated based on computed tomography. Mean absolute ADC differences as well as within-subject coefficient of variation (wCV) and intraclass correlation coefficients (ICCs) were calculated for all volumes of interest. RESULTS A total of 23 datasets were analyzed. Mean ADC difference (DS-MRL) for SG, PG and TV resulted in 142, 254 and 93·10-6 mm2/s. wCVs/ICCs, comparing MRL and DS, were determined as 13.7 %/0.26, 24.4 %/0.23 and 16.1 %/0.73 for SG, PG and TV, respectively. CONCLUSION ADC values, measured on the 1.5 T MRL, showed reasonable reproducibility with an ADC underestimation in contrast to the DS. This ADC shift must be validated in further experiments and considered for future translation of QIB candidates from DS to MRL for response adaptive RT.
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Affiliation(s)
- Jonas Habrich
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.
| | - Simon Boeke
- German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany
| | - Victor Fritz
- Section for Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Germany
| | - Elisa Koerner
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, University of Tübingen, Germany
| | - Fritz Schick
- Section for Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Germany
| | - Cihan Gani
- Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany
| | - Daniel Zips
- German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany; Department of Radiation Oncology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany
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4
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García-Figueiras R, Baleato-González S, Luna A, Padhani AR, Vilanova JC, Carballo-Castro AM, Oleaga-Zufiria L, Vallejo-Casas JA, Marhuenda A, Gómez-Caamaño A. How Imaging Advances Are Defining the Future of Precision Radiation Therapy. Radiographics 2024; 44:e230152. [PMID: 38206833 DOI: 10.1148/rg.230152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Radiation therapy is fundamental in the treatment of cancer. Imaging has always played a central role in radiation oncology. Integrating imaging technology into irradiation devices has increased the precision and accuracy of dose delivery and decreased the toxic effects of the treatment. Although CT has become the standard imaging modality in radiation therapy, the development of recently introduced next-generation imaging techniques has improved diagnostic and therapeutic decision making in radiation oncology. Functional and molecular imaging techniques, as well as other advanced imaging modalities such as SPECT, yield information about the anatomic and biologic characteristics of tumors for the radiation therapy workflow. In clinical practice, they can be useful for characterizing tumor phenotypes, delineating volumes, planning treatment, determining patients' prognoses, predicting toxic effects, assessing responses to therapy, and detecting tumor relapse. Next-generation imaging can enable personalization of radiation therapy based on a greater understanding of tumor biologic factors. It can be used to map tumor characteristics, such as metabolic pathways, vascularity, cellular proliferation, and hypoxia, that are known to define tumor phenotype. It can also be used to consider tumor heterogeneity by highlighting areas at risk for radiation resistance for focused biologic dose escalation, which can impact the radiation planning process and patient outcomes. The authors review the possible contributions of next-generation imaging to the treatment of patients undergoing radiation therapy. In addition, the possible roles of radio(geno)mics in radiation therapy, the limitations of these techniques, and hurdles in introducing them into clinical practice are discussed. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.
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Affiliation(s)
- Roberto García-Figueiras
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Sandra Baleato-González
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Antonio Luna
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Anwar R Padhani
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Joan C Vilanova
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Ana M Carballo-Castro
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Laura Oleaga-Zufiria
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Juan Antonio Vallejo-Casas
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Ana Marhuenda
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Antonio Gómez-Caamaño
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
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5
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Abstract
Magnetic resonance imaging-guided radiation therapy (MRIgRT) has improved soft tissue contrast over computed tomography (CT) based image-guided RT. Superior visualization of the target and surrounding radiosensitive structures has the potential to improve oncological outcomes partly due to safer dose-escalation and adaptive planning. In this review, we highlight the workflow of adaptive MRIgRT planning, which includes simulation imaging, daily MRI, identifying isocenter shifts, contouring, plan optimization, quality control, and delivery. Increased utilization of MRIgRT will depend on addressing technical limitations of this technology, while addressing treatment efficacy, cost-effectiveness, and workflow training.
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Affiliation(s)
- Cecil M Benitez
- Department of Radiation Oncology, UCLA Medical Center, Los Angeles, CA
| | - Michael D Chuong
- Department of Radiation Oncology, Miami Cancer Institute, Baptist Health South Florida; Miami, FL
| | - Luise A Künzel
- National Center for Tumor Diseases (NCT), Dresden; German Cancer Research Center (DKFZ), Heidelberg; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.; OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden Rossendorf, Dresden, Germany
| | - Daniela Thorwarth
- Department of Radiation Oncology, Section for Biomedical Physics, University of Tübingen, Tübingen, Germany..
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6
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McDonald BA, Dal Bello R, Fuller CD, Balermpas P. The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance. Semin Radiat Oncol 2024; 34:69-83. [PMID: 38105096 DOI: 10.1016/j.semradonc.2023.10.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Although magnetic resonance imaging (MRI) has become standard diagnostic workup for head and neck malignancies and is currently recommended by most radiological societies for pharyngeal and oral carcinomas, its utilization in radiotherapy has been heterogeneous during the last decades. However, few would argue that implementing MRI for annotation of target volumes and organs at risk provides several advantages, so that implementation of the modality for this purpose is widely accepted. Today, the term MR-guidance has received a much broader meaning, including MRI for adaptive treatments, MR-gating and tracking during radiotherapy application, MR-features as biomarkers and finally MR-only workflows. First studies on treatment of head and neck cancer on commercially available dedicated hybrid-platforms (MR-linacs), with distinct common features but also differences amongst them, have also been recently reported, as well as "biological adaptation" based on evaluation of early treatment response via functional MRI-sequences such as diffusion weighted ones. Yet, all of these approaches towards head and neck treatment remain at their infancy, especially when compared to other radiotherapy indications. Moreover, the lack of standardization for reporting MR-guided radiotherapy is a major obstacle both to further progress in the field and to conduct and compare clinical trials. Goals of this article is to present and explain all different aspects of MR-guidance for radiotherapy of head and neck cancer, summarize evidence, as well as possible advantages and challenges of the method and finally provide a comprehensive reporting guidance for use in clinical routine and trials.
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Affiliation(s)
- Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Panagiotis Balermpas
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
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7
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van Houdt PJ, Li S, Yang Y, van der Heide UA. Quantitative MRI on MR-Linacs: Towards Biological Image-Guided Adaptive Radiotherapy. Semin Radiat Oncol 2024; 34:107-119. [PMID: 38105085 DOI: 10.1016/j.semradonc.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Recognizing the potential of quantitative imaging biomarkers (QIBs) in radiotherapy, many studies have investigated the prognostic value of quantitative MRI (qMRI). With the introduction of MRI-guided radiotherapy systems, the practical challenges of repeated imaging have been substantially reduced. Since patients are treated inside an MRI scanner, acquisition of qMRI can be done during each fraction with limited or no prolongation of the fraction duration. In this review paper, we identify the steps that need been taken to move from MR as an imaging technique to a useful biomarker for MRI-guided radiotherapy (MRgRT).
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Shaolei Li
- SJTU-Ruijing, UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.; Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yingli Yang
- SJTU-Ruijing, UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.; Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands..
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8
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Liao D, Liu YC, Liu JY, Wang D, Liu XF. Differentiating tumour progression from pseudoprogression in glioblastoma patients: a monoexponential, biexponential, and stretched-exponential model-based DWI study. BMC Med Imaging 2023; 23:119. [PMID: 37697237 PMCID: PMC10494379 DOI: 10.1186/s12880-023-01082-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 08/19/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND To investigate the diagnostic performance of parameters derived from monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging models in differentiating tumour progression from pseudoprogression in glioblastoma patients. METHODS Forty patients with pathologically confirmed glioblastoma exhibiting enhancing lesions after completion of chemoradiation therapy were enrolled in the study, which were then classified as tumour progression and pseudoprogression. All patients underwent conventional and multi-b diffusion-weighted MRI. The apparent diffusion coefficient (ADC) from a monoexponential model, the true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) from a biexponential model, and the distributed diffusion coefficient (DDC) and intravoxel heterogeneity index (α) from a stretched-exponential model were compared between tumour progression and pseudoprogression groups. Receiver operating characteristic curves (ROC) analysis was used to investigate the diagnostic performance of different DWI parameters. Interclass correlation coefficient (ICC) was used to evaluate the consistency of measurements. RESULTS The values of ADC, D, DDC, and α values were lower in tumour progression patients than that in pseudoprogression patients (p < 0.05). The values of D* and f were higher in tumour progression patients than that in pseudoprogression patients (p < 0.05). Diagnostic accuracy for differentiating tumour progression from pseudoprogression was highest for α(AUC = 0.94) than that for ADC (AUC = 0.91), D (AUC = 0.92), D* (AUC = 0.81), f (AUC = 0.75), and DDC (AUC = 0.88). CONCLUSIONS Multi-b DWI is a promising method for differentiating tumour progression from pseudoprogression with high diagnostic accuracy. In addition, the α derived from stretched-exponential model is the most promising DWI parameter for the prediction of tumour progression in glioblastoma patients.
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Affiliation(s)
- Dan Liao
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou 550002 China
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010 China
| | - Yuan-Cheng Liu
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou 550002 China
| | - Jiang-Yong Liu
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou 550002 China
| | - Di Wang
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou 550002 China
| | - Xin-Feng Liu
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou 550002 China
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9
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McDonald BA, Salzillo T, Mulder S, Ahmed S, Dresner A, Preston K, He R, Christodouleas J, Mohamed ASR, Philippens M, van Houdt P, Thorwarth D, Wang J, Shukla Dave A, Boss M, Fuller CD. Prospective evaluation of in vivo and phantom repeatability and reproducibility of diffusion-weighted MRI sequences on 1.5 T MRI-linear accelerator (MR-Linac) and MR simulator devices for head and neck cancers. Radiother Oncol 2023; 185:109717. [PMID: 37211282 PMCID: PMC10527507 DOI: 10.1016/j.radonc.2023.109717] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 05/12/2023] [Accepted: 05/13/2023] [Indexed: 05/23/2023]
Abstract
INTRODUCTION Diffusion-weighted imaging (DWI) on MRI-linear accelerator (MR-linac) systems can potentially be used for monitoring treatment response and adaptive radiotherapy in head and neck cancers (HNC) but requires extensive validation. We performed technical validation to compare six total DWI sequences on an MR-linac and MR simulator (MR sim) in patients, volunteers, and phantoms. METHODS Ten human papillomavirus-positive oropharyngeal cancer patients and ten healthy volunteers underwent DWI on a 1.5 T MR-linac with three DWI sequences: echo planar imaging (EPI), split acquisition of fast spin echo signals (SPLICE), and turbo spin echo (TSE). Volunteers were also imaged on a 1.5 T MR sim with three sequences: EPI, BLADE (vendor tradename), and readout segmentation of long variable echo trains (RESOLVE). Participants underwent two scan sessions per device and two repeats of each sequence per session. Repeatability and reproducibility within-subject coefficient of variation (wCV) of mean ADC were calculated for tumors and lymph nodes (patients) and parotid glands (volunteers). ADC bias, repeatability/reproducibility metrics, SNR, and geometric distortion were quantified using a phantom. RESULTS In vivo repeatability/reproducibility wCV for parotids were 5.41%/6.72%, 3.83%/8.80%, 5.66%/10.03%, 3.44%/5.70%, 5.04%/5.66%, 4.23%/7.36% for EPIMR-linac, SPLICE, TSE, EPIMR sim, BLADE, RESOLVE. Repeatability/reproducibility wCV for EPIMR-linac, SPLICE, TSE were 9.64%/10.28%, 7.84%/8.96%, 7.60%/11.68% for tumors and 7.80%/9.95%, 7.23%/8.48%, 10.82%/10.44% for nodes. All sequences except TSE had phantom ADC biases within ± 0.1x10-3 mm2/s for most vials (EPIMR-linac, SPLICE, and BLADE had 2, 3, and 1 vials out of 13 with larger biases, respectively). SNR of b = 0 images was 87.3, 180.5, 161.3, 171.0, 171.9, 130.2 for EPIMR-linac, SPLICE, TSE, EPIMR sim, BLADE, RESOLVE. CONCLUSION MR-linac DWI sequences demonstrated near-comparable performance to MR sim sequences and warrant further clinical validation for treatment response assessment in HNC.
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Affiliation(s)
| | | | - Samuel Mulder
- The University of Texas MD Anderson Cancer Center, USA
| | - Sara Ahmed
- The University of Texas MD Anderson Cancer Center, USA
| | | | | | - Renjie He
- The University of Texas MD Anderson Cancer Center, USA
| | | | | | | | | | | | - Jihong Wang
- The University of Texas MD Anderson Cancer Center, USA
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10
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Dubec MJ, Buckley DL, Berks M, Clough A, Gaffney J, Datta A, McHugh DJ, Porta N, Little RA, Cheung S, Hague C, Eccles CL, Hoskin PJ, Bristow RG, Matthews JC, van Herk M, Choudhury A, Parker GJM, McPartlin A, O'Connor JPB. First-in-human technique translation of oxygen-enhanced MRI to an MR Linac system in patients with head and neck cancer. Radiother Oncol 2023; 183:109592. [PMID: 36870608 DOI: 10.1016/j.radonc.2023.109592] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 03/06/2023]
Abstract
BACKGROUND AND PURPOSE Tumour hypoxia is prognostic in head and neck cancer (HNC), associated with poor loco-regional control, poor survival and treatment resistance. The advent of hybrid MRI - radiotherapy linear accelerator or 'MR Linac' systems - could permit imaging for treatment adaptation based on hypoxic status. We sought to develop oxygen-enhanced MRI (OE-MRI) in HNC and translate the technique onto an MR Linac system. MATERIALS AND METHODS MRI sequences were developed in phantoms and 15 healthy participants. Next, 14 HNC patients (with 21 primary or local nodal tumours) were evaluated. Baseline tissue longitudinal relaxation time (T1) was measured alongside the change in 1/T1 (termed ΔR1) between air and oxygen gas breathing phases. We compared results from 1.5 T diagnostic MR and MR Linac systems. RESULTS Baseline T1 had excellent repeatability in phantoms, healthy participants and patients on both systems. Cohort nasal concha oxygen-induced ΔR1 significantly increased (p < 0.0001) in healthy participants demonstrating OE-MRI feasibility. ΔR1 repeatability coefficients (RC) were 0.023-0.040 s-1 across both MR systems. The tumour ΔR1 RC was 0.013 s-1 and the within-subject coefficient of variation (wCV) was 25% on the diagnostic MR. Tumour ΔR1 RC was 0.020 s-1 and wCV was 33% on the MR Linac. ΔR1 magnitude and time-course trends were similar on both systems. CONCLUSION We demonstrate first-in-human translation of volumetric, dynamic OE-MRI onto an MR Linac system, yielding repeatable hypoxia biomarkers. Data were equivalent on the diagnostic MR and MR Linac systems. OE-MRI has potential to guide future clinical trials of biology guided adaptive radiotherapy.
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Affiliation(s)
- Michael J Dubec
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK.
| | - David L Buckley
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK; Biomedical Imaging, University of Leeds, Leeds, UK
| | - Michael Berks
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Abigael Clough
- Radiotherapy, The Christie NHS Foundation Trust, Manchester, UK
| | - John Gaffney
- Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - Anubhav Datta
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Radiology, The Christie NHS Foundation Trust, Manchester, UK
| | - Damien J McHugh
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Nuria Porta
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, UK
| | - Ross A Little
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Susan Cheung
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Christina Hague
- Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - Cynthia L Eccles
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Radiotherapy, The Christie NHS Foundation Trust, Manchester, UK
| | - Peter J Hoskin
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Clinical Oncology, Mount Vernon Cancer Centre, Northwood, UK
| | - Robert G Bristow
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - Julian C Matthews
- Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Marcel van Herk
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Ananya Choudhury
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - Geoff J M Parker
- Bioxydyn Ltd, Manchester, UK; Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Andrew McPartlin
- Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK; Radiation Oncology, Princess Margaret Cancer Center, Toronto, Canada
| | - James P B O'Connor
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Radiology, The Christie NHS Foundation Trust, Manchester, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, UK
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11
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Hu W, Chen L, Lin L, Wang J, Wang N, Liu A. Three-dimensional amide proton transfer-weighted and intravoxel incoherent motion imaging for predicting bone metastasis in patients with prostate cancer: A pilot study. Magn Reson Imaging 2023; 96:8-16. [PMID: 36375760 DOI: 10.1016/j.mri.2022.11.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 10/25/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022]
Abstract
PURPOSE To explore the value of 3-dimensional amide proton transfer-weighted (APTw) and intravoxel incoherent motion (IVIM) imaging in predicting bone metastasis (BM) of prostate cancer (PCa) in addition to routine diffusion-weighted imaging (DWI). METHODS The clinical and imaging data of 39 PCa patients who were pathologically confirmed in our hospital from March 2019 to February 2022 were retrospectively analyzed, and they were divided into BM-negative (27 patients) and BM-positive (12 patients) groups. MR examination included APTw, DWI and IVIM imaging. The IVIM data was fitted by single-exponential IVIM model (IVIMmono) and double-exponential IVIM model (IVIMbi), respectively. The APTw, ADC, IVIMmono (Dmono, D*mono, and fmono), and IVIMbi (Dbi, D*bi, and fbi) parameters were independently measured by two radiologists. The synthetic minority oversampling technique (SMOTE) was conducted to balance the minority group. Mann-Whitney U test or Student's t-test was used to compare above values between the BM-negative and BM-positive groups. The diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis of each parameter and their combination. The Delong test was used for ROC curve comparison.The relationship between APTw and IVIM was explored through Spearman's rank correlation analysis. RESULTS The APTw and D*mono values were higher, and the ADC, fmono, and fbi values were lower in the BM-positive group than in the BM-negative group (all P < 0.05). Among the individual parameters, the AUC of fmono was the highest (AUC = 0.865), and AUC (fmono) was significantly higher than AUC (fbi), AUC (D*mono), and AUC (ADC) (all P < 0.05). The AUC (IVIMmono) was higher than the AUC (IVIMbi) (P = 0.0068). The combination of APTw and IVIMmono further improved diagnostic capability, and the AUC of APTw+IVIMmono was significantly higher than those of APTw and DWI (all P < 0.05). No correlation was found between IVIM-derived parameters and APTw value. CONCLUSION Both 3D APTw and IVIM imaging could predict BM of PCa. IVIM showed better performance than APTw and DWI, and the single-exponential IVIM model was superior to the double-exponential IVIM model. The combination of APTw and IVIM could further improve diagnostic performance.
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Affiliation(s)
- Wenjun Hu
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, 116011, PR China
| | - Lihua Chen
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, 116011, PR China; Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging, Dalian, Liaoning, 116011, PR China
| | | | | | - Nan Wang
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, 116011, PR China; Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging, Dalian, Liaoning, 116011, PR China
| | - Ailian Liu
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, 116011, PR China; Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging, Dalian, Liaoning, 116011, PR China.
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12
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Li S, Zheng T, Fan Z, Qu H, Wang J, Bi J, Lv Q, Zhang G, Cui X, Zhao Y. A dynamic-static combination model based on radiomics features for prostate cancer using multiparametric MRI. Phys Med Biol 2022; 68. [PMID: 36541844 DOI: 10.1088/1361-6560/aca954] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 12/06/2022] [Indexed: 12/12/2022]
Abstract
Objective. To propose a new dynamic multiparametric magnetic resonance imaging (mpMRI) radiomics method for the detection of prostate cancer (PCa), and establish a combined model using dynamic and static radiomics features based on this method.Approach. A total of 166 patients (82 PCa patients and 84 non-PCa patients) were enrolled in the study, and 31 872 mpMRI images were performed in a radiomics workflow. The whole prostate segmentation and traditional static radiomics features extraction were performed on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI,bvalue of 10, 50, 100, 150, 200, 400, 600, 800, 1000, 1500 s mm-2respectively), apparent diffusion coefficient (ADC), and T2-weighted imaging (T2WI) sequences respectively. Through the building of eachb-value DWI model and the analysis of the static key radiomics features, three types of dynamic features called standard discrete (SD), parameter (P) and relative change rate (RCR) were constructed. And the b-value parameters used to construct dynamic features were divided into three groups ('Df_', 'Db_' and 'Da_'): the front part (10-200 s mm-2), the back part (400-1500 s mm-2), and all (10-1500 s mm-2) of theb-values set, respectively. Afterwards, the dynamic mpMRI model and combined model construction were constructed, and the PCa discrimination performance of each model was evaluated.Main results.The models based on dynamic features showed good potential for PCa identification. Where, the results of Db_SD, Da_P and Db_P models were encouraging (test cohort AUCs: 90.78%, 87.60%, 86.3%), which was better than the commonly used ADC model (AUC of ADC was 75.48%). Among the combined models, the models using static features of T2WI and dynamic features performed the best. The AUC of Db_SD + T2WI, Db_P + T2WI and Db_RCR + T2WI model was 92.90%, 91.29% and 81.46%.Significance.The dynamic-static combination model based on dynamic mpMRI radiomics method has a good effect on the identification of PCa. This method has broad application prospects in PCa individual diagnosis management.
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Affiliation(s)
- Shuqin Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China
| | - Tingting Zheng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China
| | - Zhou Fan
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China
| | - Hui Qu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China
| | - Jianfeng Wang
- Department of Urology Surgery, The First Hospital of China Medical University, No.155 Nanjing North Street, Heping District, Shenyang, People's Republic of China
| | - Jianbin Bi
- Department of Urology Surgery, The First Hospital of China Medical University, No.155 Nanjing North Street, Heping District, Shenyang, People's Republic of China
| | - Qingjie Lv
- Department of Pathology, Shengjing Hospital of China Medical University, Sanhao Street 36, Shenyang, 110001, People's Republic of China
| | - Gejun Zhang
- Department of Urology Surgery, The First Hospital of China Medical University, No.155 Nanjing North Street, Heping District, Shenyang, People's Republic of China
| | - Xiaoyu Cui
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China.,Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110169, People's Republic of China
| | - Yue Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China.,National and Local Joint Engineering Research Center of Immunodermatological Theranostics, No.155 Nanjing Bei Street, Heping District, Shenyang, 110001, People's Republic of China
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13
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Gurney-Champion OJ, Landry G, Redalen KR, Thorwarth D. Potential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapy. Semin Radiat Oncol 2022; 32:377-388. [DOI: 10.1016/j.semradonc.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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14
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Mui AW, Lee AW, Ng W, Lee VH, Vardhanabhuti V, Man SS, Chua DT, Guan X. Correlations of tumour permeability parameters with apparent diffusion coefficient in nasopharyngeal carcinoma. Phys Imaging Radiat Oncol 2022; 24:30-35. [PMID: 36148154 PMCID: PMC9485900 DOI: 10.1016/j.phro.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 09/06/2022] [Accepted: 09/06/2022] [Indexed: 11/03/2022] Open
Abstract
Vascular permeability is associated with diffusability in nasopharyngeal tumour. Both influx and reflux rates have inverse linear correlations with ADC. Reflux rate has the strongest inverse linear correlation with ADC.
Background and Purpose Functional imaging has an established role in therapeutic monitoring of cancer treatments. This study evaluated the correlations of tumour permeability parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and tumour cellularity derived from apparent diffusion coefficient (ADC) in nasopharyngeal carcinoma (NPC). Material and Methods Twenty NPC patients were examined with DCE-MRI and RESOLVE diffusion-weighted MRI (DW-MRI). Tumour permeability parameters were quantitatively measured with Tofts compartment model. Volume transfer constant (Ktrans), volume of extravascular extracellular space (EES) per unit volume of tissue (Ve), and the flux rate constant between EES and plasma (Kep) from DCE-MRI scan were measured. The time-intensity curve was plotted from the 60 dynamic phases of DCE-MRI. The initial area under the curve for the first 60 s of the contrast agent arrival (iAUC60) was also calculated. They were compared with the ADC value derived from DW-MRI with Pearson correlation analyses. Results Among the DCE-MRI permeability parameters, Kep had higher linearity in inverse correlation with ADC value (r = −0.69, p = <0.05). Ktrans (r = −0.60, p=<0.05) and iAUC60 (r = −0.64, p = <0.05) also had significant inverse correlations with ADC. Ve showed a significant positive correlation with ADC (r = 0.63, p = <0.05). Conclusions Nasopharyngeal tumour vascular permeability parameters derived from DCE-MRI scan were correlated linearly with tumour cellularity measured by free water diffusability with ADC. The clinical implementations of these linear correlations in the quantitative assessments of therapeutic response for NPC patients may be worth to further explore.
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15
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Hall WA, Kishan AU, Hall E, Nagar H, Vesprini D, Paulson E, Van der Heide UA, Lawton CAF, Kerkmeijer LGW, Tree AC. Adaptive magnetic resonance image guided radiation for intact localized prostate cancer how to optimally test a rapidly emerging technology. Front Oncol 2022; 12:962897. [PMID: 36132128 PMCID: PMC9484536 DOI: 10.3389/fonc.2022.962897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/04/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction Prostate cancer is a common malignancy for which radiation therapy (RT) provides an excellent management option with high rates of control and low toxicity. Historically RT has been given with CT based image guidance. Recently, magnetic resonance (MR) imaging capabilities have been successfully integrated with RT delivery platforms, presenting an appealing, yet complex, expensive, and time-consuming method of adapting and guiding RT. The precise benefits of MR guidance for localized prostate cancer are unclear. We sought to summarize optimal strategies to test the benefits of MR guidance specifically in localized prostate cancer. Methods A group of radiation oncologists, physicists, and statisticians were identified to collectively address this topic. Participants had a history of treating prostate cancer patients with the two commercially available MRI-guided RT devices. Participants also had a clinical focus on randomized trials in localized prostate cancer. The goal was to review both ongoing trials and present a conceptual focus on MRI-guided RT specifically in the definitive treatment of prostate cancer, along with developing and proposing novel trials for future consideration. Trial hypotheses, endpoints, and areas for improvement in localized prostate cancer that specifically leverage MR guided technology are presented. Results Multiple prospective trials were found that explored the potential of adaptive MRI-guided radiotherapy in the definitive treatment of prostate cancer. Different primary areas of improvement that MR guidance may offer in prostate cancer were summarized. Eight clinical trial design strategies are presented that summarize options for clinical trials testing the potential benefits of MRI-guided RT. Conclusions The number and scope of trials evaluating MRI-guided RT for localized prostate cancer is limited. Yet multiple promising opportunities to test this technology and potentially improve outcomes for men with prostate cancer undergoing definitive RT exist. Attention, in the form of multi-institutional randomized trials, is needed.
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Affiliation(s)
- William A. Hall
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Amar U. Kishan
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Emma Hall
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Himanshu Nagar
- Depart of Radiation Oncology, Weill Cornell Medicine, Department of Radiation Oncology, New York, NY, United States
| | - Danny Vesprini
- Department of Radiation Oncology, Sunnybrook Hospital, University of Toronto, Toronto, ON, Canada
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Uulke A. Van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Colleen A. F. Lawton
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Linda G. W. Kerkmeijer
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Alison C. Tree
- The Royal Marsden NHS Foundation Trust, and the Institute of Cancer Research, Sutton, United Kingdom
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16
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Habrich J, Boeke S, Nachbar M, Nikolaou K, Schick F, Gani C, Zips D, Thorwarth D. Repeatability of diffusion-weighted magnetic resonance imaging in head and neck cancer at a 1.5 T MR-Linac. Radiother Oncol 2022; 174:141-148. [PMID: 35902042 DOI: 10.1016/j.radonc.2022.07.020] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND PURPOSE Functional information acquired through diffusion-weighted magnetic resonance imaging (DW-MRI) may be beneficial for personalized head and neck cancer (HNC) radiotherapy. Technical validation is required before DW-MRI based radiotherapy interventions can be realized clinically. The aim of this study was to assess the repeatability of apparent diffusion coefficients (ADC) derived from DW-MRI in HNC using echo-planar imaging (EPI) on a 1.5 T MR-Linac. MATERIAL AND METHODS A total of eleven HNC patients underwent test/retest DW-MRI scans at least once per week during fractionated radiotherapy at the MR-Linac. An EPI DW-MRI test scan (b=0, 150, 500 s/mm2) was acquired before the start of adaptive MR-guided radiotherapy in addition to an identical retest scan after irradiation. Volumes-of-interest (VOI) were defined manually for parotid (PTs) and submandibular glands (SMs), gross tumor volume (GTV) and lymph nodes (LNs). Mean ADC was calculated for all VOI in all test/retest scans. Absolute/relative repeatability coefficients (RCs/relRCs) as well as intraclass correlation coefficients (ICCs) were determined for all VOI. RESULTS A total of 81 datasets were analyzed. Mean test ADC values were 1380/1416, 950/1010, 1520 and 1344·10-6 mm2/s for left/right SM and PT, GTV and LNs, respectively. Accordingly, RC (relRC) values were determined as 271/281 (19.4/21.8%) and 138/155 (13.3/15.2%), 457 (31.3%) and 310·10-6 mm2/s (23.5%). ICC resulted in 0.80/0.87, 0.97/0.94, 0.75 and 0.83 for left/right SM and PT, GTV and LNs, respectively. CONCLUSION The repeatability of ADC derived from EPI DW-MRI at the 1.5 T MR-Linac appears reasonable to be used for future biologically adapted MR-guided radiotherapy.
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Affiliation(s)
- Jonas Habrich
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany.
| | - Simon Boeke
- German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiation Oncology, University of Tübingen, Germany
| | - Marcel Nachbar
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, University of Tübingen, Germany
| | - Fritz Schick
- Section for Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Germany
| | - Cihan Gani
- Department of Radiation Oncology, University of Tübingen, Germany
| | - Daniel Zips
- German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiation Oncology, University of Tübingen, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany
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17
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Keall PJ, Brighi C, Glide-Hurst C, Liney G, Liu PZY, Lydiard S, Paganelli C, Pham T, Shan S, Tree AC, van der Heide UA, Waddington DEJ, Whelan B. Integrated MRI-guided radiotherapy - opportunities and challenges. Nat Rev Clin Oncol 2022; 19:458-470. [PMID: 35440773 DOI: 10.1038/s41571-022-00631-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/31/2022] [Indexed: 12/25/2022]
Abstract
MRI can help to categorize tissues as malignant or non-malignant both anatomically and functionally, with a high level of spatial and temporal resolution. This non-invasive imaging modality has been integrated with radiotherapy in devices that can differentially target the most aggressive and resistant regions of tumours. The past decade has seen the clinical deployment of treatment devices that combine imaging with targeted irradiation, making the aspiration of integrated MRI-guided radiotherapy (MRIgRT) a reality. The two main clinical drivers for the adoption of MRIgRT are the ability to image anatomical changes that occur before and during treatment in order to adapt the treatment approach, and to image and target the biological features of each tumour. Using motion management and biological targeting, the radiation dose delivered to the tumour can be adjusted during treatment to improve the probability of tumour control, while simultaneously reducing the radiation delivered to non-malignant tissues, thereby reducing the risk of treatment-related toxicities. The benefits of this approach are expected to increase survival and quality of life. In this Review, we describe the current state of MRIgRT, and the opportunities and challenges of this new radiotherapy approach.
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Affiliation(s)
- Paul J Keall
- ACRF Image X Institute, The University of Sydney, Sydney, New South Wales, Australia.
| | - Caterina Brighi
- ACRF Image X Institute, The University of Sydney, Sydney, New South Wales, Australia
| | - Carri Glide-Hurst
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Gary Liney
- Ingham Institute of Applied Medical Research, Sydney, New South Wales, Australia
| | - Paul Z Y Liu
- ACRF Image X Institute, The University of Sydney, Sydney, New South Wales, Australia
| | - Suzanne Lydiard
- ACRF Image X Institute, The University of Sydney, Sydney, New South Wales, Australia
| | - Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Trang Pham
- Faculty of Medicine and Health, The University of New South Wales, Sydney, New South Wales, Australia
| | - Shanshan Shan
- ACRF Image X Institute, The University of Sydney, Sydney, New South Wales, Australia
| | - Alison C Tree
- The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, London, UK
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - David E J Waddington
- ACRF Image X Institute, The University of Sydney, Sydney, New South Wales, Australia
| | - Brendan Whelan
- ACRF Image X Institute, The University of Sydney, Sydney, New South Wales, Australia
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Kooreman ES, van Pelt V, Nowee ME, Pos F, van der Heide UA, van Houdt PJ. Longitudinal Correlations Between Intravoxel Incoherent Motion (IVIM) and Dynamic Contrast-Enhanced (DCE) MRI During Radiotherapy in Prostate Cancer Patients. Front Oncol 2022; 12:897130. [PMID: 35747819 PMCID: PMC9210504 DOI: 10.3389/fonc.2022.897130] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose Intravoxel incoherent motion (IVIM) is a promising technique that can acquire perfusion information without the use of contrast agent, contrary to the more established dynamic contrast-enhanced (DCE) technique. This is of interest for treatment response monitoring, where patients can be imaged on each treatment fraction. In this study, longitudinal correlations between IVIM- and DCE parameters were assessed in prostate cancer patients receiving radiation treatment. Materials and Methods 20 prostate cancer patients were treated on a 1.5 T MR-linac with 20 x 3 or 3.1 Gy. Weekly IVIM and DCE scans were acquired. Tumors, the peripheral zone (PZ), and the transition zone (TZ) were delineated on a T2-weighted scan acquired on the first fraction. IVIM and DCE scans were registered to this scan and the delineations were propagated. Median values from these delineations were used for further analysis. The IVIM parameters D, f, D* and the product fD* were calculated. The Tofts model was used to calculate the DCE parameters Ktrans, kep and ve. Pearson correlations were calculated for the IVIM and DCE parameters on values from the first fraction for each region of interest (ROI). For longitudinal analysis, the repeated measures correlation coefficient was used to determine correlations between IVIM and DCE parameters in each ROI. Results When averaging over patients, an increase during treatment in all IVIM and DCE parameters was observed in all ROIs, except for D in the PZ and TZ. No significant Pearson correlations were found between any pair of IVIM and DCE parameters measured on the first fraction. Significant but low longitudinal correlations were found for some combinations of IVIM and DCE parameters in the PZ and TZ, while no significant longitudinal correlations were found in the tumor. Notably in the TZ, for both f and fD*, significant longitudinal correlations with all DCE parameters were found. Conclusions The increase in IVIM- and DCE parameters when averaging over patients indicates a measurable response to radiation treatment with both techniques. Although low, significant longitudinal correlations were found which suggests that IVIM could potentially be used as an alternative to DCE for treatment response monitoring.
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Quantification of Tumor Hypoxia through Unsupervised Modelling of Consumption and Supply Hypoxia MR Imaging in Breast Cancer. Cancers (Basel) 2022; 14:cancers14051326. [PMID: 35267636 PMCID: PMC8909402 DOI: 10.3390/cancers14051326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/25/2022] [Accepted: 03/02/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Hypoxia in solid tumors is common in most solid cancers and is associated with treatment resistance to both chemo- and radiation-therapy. There is also reason to believe that hypoxia is an important determinant of metastic disease. Identifying hypoxia in solid tumors is important in treatment planning and decision making. In 2018 Hompland et al. proposed a method, based on quantifying consumption and supply of oxygen from diffusion weighted magnetic resonance imaging, to estimate the hypoxic fraction of a solid tumor. The method was based on training model parameters on a known hypoxia state in prostate cancer. In the present study we verified the validity of the consumption and supply concept in breast cancer. Furthermore, we developed and validated a new approach to the concept that does not require a ground truth to train the parameters. Abstract The purpose of the present study is to investigate if consumption and supply hypoxia (CSH) MR-imaging can depict breast cancer hypoxia, using the CSH-method initially developed for prostate cancer. Furthermore, to develop a generalized pan-cancer application of the CSH-method that doesn’t require a hypoxia reference standard for training the CSH-parameters. In a cohort of 69 breast cancer patients, we generated, based on the principles of intravoxel incoherent motion modelling, images reflecting cellular density (apparent diffusion coefficient; ADC) and vascular density (perfusion fraction; fp). Combinations of the information in these images were compared to a molecular hypoxia score made from gene expression data, aiming to identify a way to apply the CSH-methodology in breast cancer. Attempts to adapt previously proposed models for prostate cancer included direct transfers and model parameter rescaling. A novel approach, based on rescaling ADC and fp data to give more nuanced response in the relevant physiologic range, was also introduced. The new CSH-method was validated in a prostate cancer cohort with known hypoxia status. The proposed CSH-method gave estimates of hypoxia that was strongly correlated to the molecular hypoxia score in breast cancer, and hypoxia as measured in pathology slices stained with pimonidazole in prostate cancer. The generalized approach to CSH-imaging depicted hypoxia in both breast and prostate cancers and requires no model training. It is easy to implement using readily available technology and encourages further investigation of CSH-imaging in other cancer entities and in other settings, with the goal being to overcome hypoxia-induced resistance to treatment.
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