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Boss MA, Malyarenko D, Partridge S, Obuchowski N, Shukla-Dave A, Winfield JM, Fuller CD, Miller K, Mishra V, Ohliger M, Wilmes LJ, Attariwala R, Andrews T, deSouza NM, Margolis DJ, Chenevert TL, Panzer A. The QIBA Profile for Diffusion-Weighted MRI: Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker. Radiology 2024; 313:e233055. [PMID: 39377680 PMCID: PMC11537247 DOI: 10.1148/radiol.233055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/23/2024] [Accepted: 03/21/2024] [Indexed: 10/09/2024]
Abstract
The apparent diffusion coefficient (ADC) provides a quantitative measure of water mobility that can be used to probe alterations in tissue microstructure due to disease or treatment. Establishment of the accepted level of variance in ADC measurements for each clinical application is critical for its successful implementation. The Diffusion-Weighted Imaging Biomarker Committee of the Quantitative Imaging Biomarkers Alliance (QIBA) has recently advanced the ADC Profile from the consensus to clinically feasible stage for the brain, liver, prostate, and breast. This profile distills multiple studies on ADC repeatability and describes detailed procedures to achieve stated performance claims on an observed ADC change within acceptable confidence limits. In addition to reviewing the current ADC Profile claims, this report has used recent literature to develop proposed updates for establishing metrology benchmarks for mean lesion ADC change that account for measurement variance. Specifically, changes in mean ADC exceeding 8% for brain lesions, 27% for liver lesions, 27% for prostate lesions, and 15% for breast lesions are claimed to represent true changes with 95% confidence. This report also discusses the development of the ADC Profile, highlighting its various stages, and describes the workflow essential to achieving a standardized implementation of advanced quantitative diffusion-weighted MRI in the clinic. The presented QIBA ADC Profile guidelines should enable successful clinical application of ADC as a quantitative imaging biomarker and ensure reproducible ADC measurements that can be used to confidently evaluate longitudinal changes and treatment response for individual patients.
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Affiliation(s)
- Michael A. Boss
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Dariya Malyarenko
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Savannah Partridge
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Nancy Obuchowski
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Amita Shukla-Dave
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Jessica M. Winfield
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Clifton D. Fuller
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Kevin Miller
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Virendra Mishra
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Michael Ohliger
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Lisa J. Wilmes
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Raj Attariwala
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Trevor Andrews
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Nandita M. deSouza
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Daniel J. Margolis
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Thomas L. Chenevert
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
| | - Ariane Panzer
- From the Center for Research and Innovation, American College of
Radiology, 50 S 16th St, Philadelphia, PA 19102 (M.A.B.); Department of
Radiology, University of Michigan, Ann Arbor, Mich (D.M., T.L.C.); Department of
Radiology, University of Washington, Seattle, Wash (S.P.); Department of
Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (N.O.);
Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer
Center, New York, NY (A.S.D.); The Institute of Cancer Research, London, UK
(J.M.W., N.M.d.S.); The Royal Marsden NHS Foundation Trust, London, UK (J.M.W.,
N.M.d.S.); Department of Radiation Oncology, The University of Texas MD Anderson
Cancer Center, Houston, Tex (C.D.F.); CaliberMRI, Boulder, Colo (K.M.);
Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala
(V.M.); Department of Radiology and Biomedical Imaging, University of
California, San Francisco, San Francisco, Calif (M.O., L.J.W.); Aim Medical
Imaging, Vancouver, Canada (R.A.); Mallinckrodt Institute of Radiology,
Washington University School of Medicine, St Louis, Mo (T.A.); and Department of
Radiology, Weill Cornell Medical College, New York, NY (D.J.M.)
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Subashi E, LoCastro E, Burleson S, Apte A, Zelefsky M, Tyagi N. Feasibility of quantitative relaxometry for prostate target localization and response assessment in magnetic resonance-guided online adaptive stereotactic body radiotherapy. Phys Imaging Radiat Oncol 2024; 32:100678. [PMID: 39717186 PMCID: PMC11665667 DOI: 10.1016/j.phro.2024.100678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2024] [Revised: 11/12/2024] [Accepted: 11/14/2024] [Indexed: 12/25/2024] Open
Abstract
Purpose Multiparametric magnetic resonance imaging (MRI) is known to provide predictors for malignancy and treatment outcome. The inclusion of these datasets in workflows for online adaptive planning remains under investigation. We demonstrate the feasibility of longitudinal relaxometry in online MR-guided adaptive stereotactic body radiotherapy (SBRT) to the prostate and dominant intra-prostatic lesion (DIL). Methods Fifty patients with intermediate-risk prostate cancer were included in the study. The clinical target volume (CTV) was defined as the prostate gland plus 1 cm of seminal vesicles. The gross tumor volume (GTV) was defined as the DIL identified on multiparametric MRI. Online adaptive radiotherapy was delivered in a 1.5 T MR-Linac using a prescription of 800 cGy/900 cGy × 5 fractions to the CTV + 3 mm/GTV + 2 mm. Relaxometry and diffusion-weighted imaging were implemented using clinically available sequences. Test-retest measurements were performed in eight patients, at each treatment fraction. Bias and uncertainty in relaxometry measurements were also assessed using a reference phantom. Results The bias in longitudinal/transverse relaxation times was negligible while uncertainty was within 3 %. Test-retest measurements demonstrate that bias/uncertainty in patient T1 and T2 were comparable to bias/uncertainty estimated in the phantom. Mean T1 and T2 relaxation were significantly different between the prostate and DIL. The correlation between T1, T2, and diffusion was significant in the DIL, but not in the prostate. During treatment, mean T1 in the DIL approaches mean T1 in the prostate. Conclusions Longitudinal relaxometry for online MR-guided adaptive SBRT is feasible in a high-field MR-Linac and may provide complementary information for target delineation and response assessment.
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Affiliation(s)
- Ergys Subashi
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Sarah Burleson
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Aditya Apte
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Michael Zelefsky
- Department of Radiation Oncology, New York University School of Medicine, New York, NY, United States
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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Wallimann P, Piccirelli M, Nowakowska S, Armstrong T, Mayinger M, Boss A, Bink A, Guckenberger M, Tanadini-Lang S, Andratschke N, Pouymayou B. Validation of echo planar imaging based diffusion-weighted magnetic resonance imaging on a 0.35 T MR-Linac. Phys Imaging Radiat Oncol 2024; 30:100579. [PMID: 38707628 PMCID: PMC11068927 DOI: 10.1016/j.phro.2024.100579] [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: 12/05/2023] [Revised: 03/08/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Background and Purpose The feasibility of acquiring diffusion-weighted imaging (DWI) images on an MR-Linac for quantitative response assessment during radiotherapy was explored. DWI data obtained with a Spin Echo Echo Planar Imaging sequence adapted for a 0.35 T MR-Linac were examined and compared with DWI data from a conventional 3 T scanner. Materials and Methods Apparent diffusion coefficient (ADC) measurements and a distortion correction technique were investigated using DWI-calibrated phantoms and in the brains of seven volunteers. All DWI utilized two phase-encoding directions for distortion correction and off-resonance field estimation. ADC maps in the brain were analyzed for automatically segmented normal tissues. Results Phantom ADC measurements on the MR-Linac were within a 3 % margin of those recorded by the 3 T scanner. The maximum distortion observed in the phantom was 2.0 mm prior to correction and 1.1 mm post-correction on the MR-Linac, compared to 6.0 mm before correction and 3.6 mm after correction at 3 T. In vivo, the average ADC values for gray and white matter exhibited variations of 14 % and 4 %, respectively, for different selections of b-values on the MR-Linac. Distortions in brain images before correction, estimated through the off-resonance field, reached 2.7 mm on the MR-Linac and 12 mm at 3 T. Conclusion Accurate ADC measurements are achievable on a 0.35 T MR-Linac, both in phantom and in vivo. The selection of b-values significantly influences ADC values in vivo. DWI on the MR-Linac demonstrated lower distortion levels, with a maximum distortion reduced to 1.1 mm after correction.
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Affiliation(s)
- Philipp Wallimann
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Marco Piccirelli
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Sylwia Nowakowska
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Tess Armstrong
- ViewRay Inc., 2 Thermo Fisher Way, Oakwood Village, OH 44146, USA
| | - Michael Mayinger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andreas Boss
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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Rabe M, Dietrich O, Forbrig R, Niyazi M, Belka C, Corradini S, Landry G, Kurz C. Repeatability quantification of brain diffusion-weighted imaging for future clinical implementation at a low-field MR-linac. Radiat Oncol 2024; 19:31. [PMID: 38448888 PMCID: PMC10916154 DOI: 10.1186/s13014-024-02424-7] [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: 01/31/2024] [Accepted: 02/26/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Longitudinal assessments of apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging (DWI) during intracranial radiotherapy at magnetic resonance imaging-guided linear accelerators (MR-linacs) could enable early response assessment by tracking tumor diffusivity changes. However, DWI pulse sequences are currently unavailable in clinical practice at low-field MR-linacs. Quantifying the in vivo repeatability of ADC measurements is a crucial step towards clinical implementation of DWI sequences but has not yet been reported on for low-field MR-linacs. This study assessed ADC measurement repeatability in a phantom and in vivo at a 0.35 T MR-linac. METHODS Eleven volunteers and a diffusion phantom were imaged on a 0.35 T MR-linac. Two echo-planar imaging DWI sequence variants, emphasizing high spatial resolution ("highRes") and signal-to-noise ratio ("highSNR"), were investigated. A test-retest study with an intermediate outside-scanner-break was performed to assess repeatability in the phantom and volunteers' brains. Mean ADCs within phantom vials, cerebrospinal fluid (CSF), and four brain tissue regions were compared to literature values. Absolute relative differences of mean ADCs in pre- and post-break scans were calculated for the diffusion phantom, and repeatability coefficients (RC) and relative RC (relRC) with 95% confidence intervals were determined for each region-of-interest (ROI) in volunteers. RESULTS Both DWI sequence variants demonstrated high repeatability, with absolute relative deviations below 1% for water, dimethyl sulfoxide, and polyethylene glycol in the diffusion phantom. RelRCs were 7% [5%, 12%] (CSF; highRes), 12% [9%, 22%] (CSF; highSNR), 9% [8%, 12%] (brain tissue ROIs; highRes), and 6% [5%, 7%] (brain tissue ROIs; highSNR), respectively. ADCs measured with the highSNR variant were consistent with literature values for volunteers, while smaller mean values were measured for the diffusion phantom. Conversely, the highRes variant underestimated ADCs compared to literature values, indicating systematic deviations. CONCLUSIONS High repeatability of ADC measurements in a diffusion phantom and volunteers' brains were measured at a low-field MR-linac. The highSNR variant outperformed the highRes variant in accuracy and repeatability, at the expense of an approximately doubled voxel volume. The observed high in vivo repeatability confirms the potential utility of DWI at low-field MR-linacs for early treatment response assessment.
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Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
| | - Olaf Dietrich
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Robert Forbrig
- Institute of Neuroradiology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Maximilian Niyazi
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, a Partnership Between DKFZ and LMU University Hospital Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Claus Belka
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, a Partnership Between DKFZ and LMU University Hospital Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
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5
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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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Wu TC, Smith LM, Woolf D, Faivre-Finn C, Lee P. Exploring the Advantages and Challenges of MR-Guided Radiotherapy in Non-Small-Cell Lung Cancer: Who are the Optimal Candidates? Semin Radiat Oncol 2024; 34:56-63. [PMID: 38105094 DOI: 10.1016/j.semradonc.2023.10.007] [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
The landscape of lung radiotherapy (RT) has rapidly evolved over the past decade with modern RT and surgical techniques, systemic therapies, and expanding indications for RT. To date, 2 MRI-guided RT (MRgRT) units, 1 using a 0.35T magnet and 1 using a 1.5T magnet, are available for commercial use with more systems in the pipeline. MRgRT offers distinct advantages such as real-time target tracking, margin reduction, and on-table treatment adaptation, which may help overcome many of the common challenges associated with thoracic RT. Nonetheless, the use of MRI for image guidance and the current MRgRT units also have intrinsic limitations. In this review article, we will discuss clinical experiences to date, advantages, challenges, and future directions of MRgRT to the lung.
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Affiliation(s)
- Trudy C Wu
- Department of Radiation Oncology, University of California, Los Angeles, CA
| | - Lauren M Smith
- Department of Radiation Oncology, University of California, Los Angeles, CA
| | - David Woolf
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom.; Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom
| | - Corinne Faivre-Finn
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom.; Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom
| | - Percy Lee
- Department of Radiation Oncology, City of Hope National Medical Center, Los Angeles, CA..
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7
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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 PMCID: PMC11372437 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] [Grants] [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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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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Lawrence LSP, Chan RW, Chen H, Stewart J, Ruschin M, Theriault A, Myrehaug S, Detsky J, Maralani PJ, Tseng CL, Soliman H, Jane Lim-Fat M, Das S, Stanisz GJ, Sahgal A, Lau AZ. Diffusion-weighted imaging on an MRI-linear accelerator to identify adversely prognostic tumour regions in glioblastoma during chemoradiation. Radiother Oncol 2023; 188:109873. [PMID: 37640160 DOI: 10.1016/j.radonc.2023.109873] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/12/2023] [Accepted: 08/20/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND AND PURPOSE Survival in glioblastoma might be extended by escalating the radiotherapy dose to treatment-resistant tumour and adapting to tumour changes. Diffusion-weighted imaging (DWI) on MRI-linear accelerators (MR-Linacs) could be used to identify a dose escalation target, but its prognostic value must be demonstrated. The purpose of this study was to determine whether MR-Linac DWI can assess treatment response in glioblastoma and whether changes in DWI show greater prognostic value than changes in the contrast-enhancing gross tumour volume (GTV). MATERIALS AND METHODS Seventy-five patients with glioblastoma were treated with chemoradiotherapy, of which 32 were treated on a 1.5 T MRI-linear accelerator (MR-Linac). Patients were imaged with simulation MRI scanners (MR-sim) at treatment planning and weeks 2, 4, and 10 after treatment start. Twenty-eight patients had additional MR-Linac DWI sequences. Cox modelling was used to evaluate the correlation of overall and progression-free survival (OS and PFS) with clinical variables and volumetric changes in the GTV and low-ADC regions (ADC < 1.25 µm2/ms within GTV). RESULTS In total, 479 MR-Linac DWI and 289 MR-sim DWI datasets were analyzed. MR-Linac low-ADC changes between weeks 2 and 5 inclusive were prognostic for OS (hazard ratio lower limits ≥ 1.2, p-values ≤ 0.02). MR-sim low-ADC changes showed greater correlation with OS and PFS than GTV changes (e.g., OS hazard ratio at week 2 was 3.4 (p <0.001) for low-ADC versus 2.0 (p = 0.022) for GTV). CONCLUSION MR-Linac DWI can measure low-ADC tumour volumes that correlate with OS and PFS better than contrast-enhancing GTV. Low-ADC regions could serve as dose escalation targets.
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Affiliation(s)
| | - Rachel W Chan
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Hanbo Chen
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - James Stewart
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Mark Ruschin
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Aimee Theriault
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Pejman J Maralani
- Medical Imaging, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Mary Jane Lim-Fat
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Sunit Das
- Keenan Chair in Surgery, St. Michael's Hospital, Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
| | - Greg J Stanisz
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Neurosurgery and Paediatric Neurosurgery, Medical University, Lublin, Poland
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Angus Z Lau
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
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Weygand J, Armstrong T, Bryant JM, Andreozzi JM, Oraiqat IM, Nichols S, Liveringhouse CL, Latifi K, Yamoah K, Costello JR, Frakes JM, Moros EG, El Naqa IM, Naghavi AO, Rosenberg SA, Redler G. Accurate, repeatable, and geometrically precise diffusion-weighted imaging on a 0.35 T magnetic resonance imaging-guided linear accelerator. Phys Imaging Radiat Oncol 2023; 28:100505. [PMID: 38045642 PMCID: PMC10692914 DOI: 10.1016/j.phro.2023.100505] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/04/2023] [Accepted: 10/30/2023] [Indexed: 12/05/2023] Open
Abstract
Background and purpose Diffusion weighted imaging (DWI) allows for the interrogation of tissue cellularity, which is a surrogate for cellular proliferation. Previous attempts to incorporate DWI into the workflow of a 0.35 T MR-linac (MRL) have lacked quantitative accuracy. In this study, accuracy, repeatability, and geometric precision of apparent diffusion coefficient (ADC) maps produced using an echo planar imaging (EPI)-based DWI protocol on the MRL system is illustrated, and in vivo potential for longitudinal patient imaging is demonstrated. Materials and methods Accuracy and repeatability were assessed by measuring ADC values in a diffusion phantom at three timepoints and comparing to reference ADC values. System-dependent geometric distortion was quantified by measuring the distance between 93 pairs of phantom features on ADC maps acquired on a 0.35 T MRL and a 3.0 T diagnostic scanner and comparing to spatially precise CT images. Additionally, for five sarcoma patients receiving radiotherapy on the MRL, same-day in vivo ADC maps were acquired on both systems, one of which at multiple timepoints. Results Phantom ADC quantification was accurate on the 0.35 T MRL with significant discrepancies only seen at high ADC. Average geometric distortions were 0.35 (±0.02) mm and 0.85 (±0.02) mm in the central slice and 0.66 (±0.04) mm and 2.14 (±0.07) mm at 5.4 cm off-center for the MRL and diagnostic system, respectively. In the sarcoma patients, a mean pretreatment ADC of 910x10-6 (±100x10-6) mm2/s was measured on the MRL. Conclusions The acquisition of accurate, repeatable, and geometrically precise ADC maps is possible at 0.35 T with an EPI approach.
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Affiliation(s)
- Joseph Weygand
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | | | | | | | - Steven Nichols
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Kujtim Latifi
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Kosj Yamoah
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Jessica M. Frakes
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Eduardo G. Moros
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Issam M. El Naqa
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
| | - Arash O. Naghavi
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Gage Redler
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
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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: 13] [Impact Index Per Article: 6.5] [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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Almansour H, Schick F, Nachbar M, Afat S, Fritz V, Thorwarth D, Zips D, Bertram F, Müller AC, Nikolaou K, Othman AE, Wegener D. Longitudinal monitoring of Apparent Diffusion Coefficient (ADC) in patients with prostate cancer undergoing MR-guided radiotherapy on an MR-Linac at 1.5 T: a prospective feasibility study. Radiol Oncol 2023; 57:184-190. [PMID: 37341194 DOI: 10.2478/raon-2023-0020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/30/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND Hybrid MRI linear accelerators (MR-Linac) might enable individualized online adaptation of radiotherapy using quantitative MRI sequences as diffusion-weighted imaging (DWI). The purpose of this study was to investigate the dynamics of lesion apparent diffusion coefficient (ADC) in patients with prostate cancer undergoing MR-guided radiation therapy (MRgRT) on a 1.5T MR-Linac. The ADC values at a diagnostic 3T MRI scanner were used as the reference standard. PATIENTS AND AND METHODS In this prospective single-center study, patients with biopsy-confirmed prostate cancer who underwent both an MRI exam at a 3T scanner (MRI3T) and an exam at a 1.5T MR-Linac (MRL) at baseline and during radiotherapy were included. Lesion ADC values were measured by a radiologist and a radiation oncologist on the slice with the largest lesion. ADC values were compared before vs. during radiotherapy (during the second week) on both systems via paired t-tests. Furthermore, Pearson correlation coefficient and inter-reader agreement were computed. RESULTS A total of nine male patients aged 67 ± 6 years [range 60 - 67 years] were included. In seven patients, the cancerous lesion was in the peripheral zone, and in two patients the lesion was in the transition zone. Inter-reader reliability regarding lesion ADC measurement was excellent with an intraclass correlation coefficient of (ICC) > 0.90 both at baseline and during radiotherapy. Thus, the results of the first reader will be reported. In both systems, there was a statistically significant elevation of lesion ADC during radiotherapy (mean MRL-ADC at baseline was 0.97 ± 0.18 × 10-3 mm2/s vs. mean MRL-ADC during radiotherapy 1.38 ± 0.3 × 10-3 mm2/s, yielding a mean lesion ADC elevation of 0.41 ± 0.20 × 10-3 mm2/s, p < 0.001). Mean MRI3T-ADC at baseline was 0.78 ± 0.165 × 10-3 mm2/s vs. mean MRI3T-ADC during radiotherapy 0.99 ± 0.175 × 10-3 mm2/s, yielding a mean lesion ADC elevation of 0.21 ± 0.96 × 10-3 mm2/s p < 0.001). The absolute ADC values from MRL were consistently significantly higher than those from MRI3T at baseline and during radiotherapy (p < = 0.001). However, there was a strong positive correlation between MRL-ADC and MRI3T-ADC at baseline (r = 0.798, p = 0.01) and during radiotherapy (r = 0.863, p = 0.003). CONCLUSIONS Lesion ADC as measured on MRL increased significantly during radiotherapy and ADC measurements of lesions on both systems showed similar dynamics. This indicates that lesion ADC as measured on the MRL may be used as a biomarker for evaluation of treatment response. In contrast, absolute ADC values as calculated by the algorithm of the manufacturer of the MRL showed systematic deviations from values obtained on a diagnostic 3T MRI system. These preliminary findings are promising but need large-scale validation. Once validated, lesion ADC on MRL might be used for real-time assessment of tumor response in patients with prostate cancer undergoing MR-guided radiation therapy.
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Affiliation(s)
- Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Tuebingen, Germany
| | - Fritz Schick
- Section for Experimental Radiology, Department of Radiology, Eberhard-Karls University, Tuebingen, Germany
| | - Marcel Nachbar
- Department of Radiation Oncology, Charité University Medicine Berlin, Berlin, Germany
- Section for Biomedical Physics, Department of Radiation Oncology, Eberhard-Karls University, Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Tuebingen, Germany
| | - Victor Fritz
- Section for Experimental Radiology, Department of Radiology, Eberhard-Karls University, Tuebingen, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, Eberhard-Karls University, Tuebingen, Germany
- German Cancer Consortium (DKTK), Partner Site Tuebingen and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel Zips
- Department of Radiation Oncology, Charité University Medicine Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Tuebingen and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Radiation Oncology, Eberhard-Karls University, Tuebingen, Germany
| | - Felix Bertram
- Department of Radiation Oncology, Eberhard-Karls University, Tuebingen, Germany
| | - Arndt-Christian Müller
- Department of Radiation Oncology, Eberhard-Karls University, Tuebingen, Germany
- Department of Radiation Oncology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tuebingen, Tuebingen, Germany
| | - Ahmed E Othman
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Tuebingen, Germany
- Department of Neuroradiology, University Medical Center Mainz, Mainz, Germany
| | - Daniel Wegener
- Department of Radiation Oncology, Eberhard-Karls University, Tuebingen, Germany
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Klaar R, Rabe M, Gaass T, Schneider MJ, Benlala I, Eze C, Corradini S, Belka C, Landry G, Kurz C, Dinkel J. Ventilation and perfusion MRI at a 0.35 T MR-Linac: feasibility and reproducibility study. Radiat Oncol 2023; 18:58. [PMID: 37013541 PMCID: PMC10069152 DOI: 10.1186/s13014-023-02244-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/07/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Hybrid devices that combine radiation therapy and MR-imaging have been introduced in the clinical routine for the treatment of lung cancer. This opened up not only possibilities in terms of accurate tumor tracking, dose delivery and adapted treatment planning, but also functional lung imaging. The aim of this study was to show the feasibility of Non-uniform Fourier Decomposition (NuFD) MRI at a 0.35 T MR-Linac as a potential treatment response assessment tool, and propose two signal normalization strategies for enhancing the reproducibility of the results. METHODS Ten healthy volunteers (median age 28 ± 8 years, five female, five male) were repeatedly scanned at a 0.35 T MR-Linac using an optimized 2D+t balanced steady-state free precession (bSSFP) sequence for two coronal slice positions. Image series were acquired in normal free breathing with breaks inside and outside the scanner as well as deep and shallow breathing. Ventilation- and perfusion-weighted maps were generated for each image series using NuFD. For intra-volunteer ventilation map reproducibility, a normalization factor was defined based on the linear correlation of the ventilation signal and diaphragm position of each scan as well as the diaphragm motion amplitude of a reference scan. This allowed for the correction of signal dependency on the diaphragm motion amplitude, which varies with breathing patterns. The second strategy, which can be used for ventilation and perfusion, eliminates the dependency on the signal amplitude by normalizing the ventilation/perfusion maps with the average ventilation/perfusion signal within a selected region-of-interest (ROI). The position and size dependency of this ROI was analyzed. To evaluate the performance of both approaches, the normalized ventilation/perfusion-weighted maps were compared and the deviation of the mean ventilation/perfusion signal from the reference was calculated for each scan. Wilcoxon signed-rank tests were performed to test whether the normalization methods can significantly improve the reproducibility of the ventilation/perfusion maps. RESULTS The ventilation- and perfusion-weighted maps generated with the NuFD algorithm demonstrated a mostly homogenous distribution of signal intensity as expected for healthy volunteers regardless of the breathing maneuver and slice position. Evaluation of the ROI's size and position dependency showed small differences in the performance. Applying both normalization strategies improved the reproducibility of the ventilation by reducing the median deviation of all scans to 9.1%, 5.7% and 8.6% for the diaphragm-based, the best and worst performing ROI-based normalization, respectively, compared to 29.5% for the non-normalized scans. The significance of this improvement was confirmed by the Wilcoxon signed rank test with [Formula: see text] at [Formula: see text]. A comparison of the techniques against each other revealed a significant difference in the performance between best ROI-based normalization and worst ROI ([Formula: see text]) and between best ROI-based normalization and scaling factor ([Formula: see text]), but not between scaling factor and worst ROI ([Formula: see text]). Using the ROI-based approach for the perfusion-maps, the uncorrected deviation of 10.2% was reduced to 5.3%, which was shown to be significant ([Formula: see text]). CONCLUSIONS Using NuFD for non-contrast enhanced functional lung MRI at a 0.35 T MR-Linac is feasible and produces plausible ventilation- and perfusion-weighted maps for volunteers without history of chronic pulmonary diseases utilizing different breathing patterns. The reproducibility of the results in repeated scans significantly benefits from the introduction of the two normalization strategies, making NuFD a potential candidate for fast and robust early treatment response assessment of lung cancer patients during MR-guided radiotherapy.
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Affiliation(s)
- Rabea Klaar
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Thomas Gaass
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Moritz J. Schneider
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
- Antaros Medical AB, BioVenture Hub, Mölndal, Sweden
| | - Ilyes Benlala
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
- Univ. Bordeaux, Centre de Recherche Cardio-thoracique de Bordeaux, F-33600 Pessac, France
- CHU Bordeaux, Service d’Imagerie Thoracique et Cardiovasculaire, Service des Maladies Respiratoires, Service d’Exploration Fonctionnelle Respiratoire, Unité de Pneumologie Pédiatrique, CIC 1401, F-33600 Pessac, France
- INSERM, U1045, Centre de Recherche Cardio-thoracique de Bordeaux, F-33600 Pessac, France
| | - Chukwuka Eze
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Julien Dinkel
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
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Ng J, Gregucci F, Pennell RT, Nagar H, Golden EB, Knisely JPS, Sanfilippo NJ, Formenti SC. MRI-LINAC: A transformative technology in radiation oncology. Front Oncol 2023; 13:1117874. [PMID: 36776309 PMCID: PMC9911688 DOI: 10.3389/fonc.2023.1117874] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/16/2023] [Indexed: 01/28/2023] Open
Abstract
Advances in radiotherapy technologies have enabled more precise target guidance, improved treatment verification, and greater control and versatility in radiation delivery. Amongst the recent novel technologies, Magnetic Resonance Imaging (MRI) guided radiotherapy (MRgRT) may hold the greatest potential to improve the therapeutic gains of image-guided delivery of radiation dose. The ability of the MRI linear accelerator (LINAC) to image tumors and organs with on-table MRI, to manage organ motion and dose delivery in real-time, and to adapt the radiotherapy plan on the day of treatment while the patient is on the table are major advances relative to current conventional radiation treatments. These advanced techniques demand efficient coordination and communication between members of the treatment team. MRgRT could fundamentally transform the radiotherapy delivery process within radiation oncology centers through the reorganization of the patient and treatment team workflow process. However, the MRgRT technology currently is limited by accessibility due to the cost of capital investment and the time and personnel allocation needed for each fractional treatment and the unclear clinical benefit compared to conventional radiotherapy platforms. As the technology evolves and becomes more widely available, we present the case that MRgRT has the potential to become a widely utilized treatment platform and transform the radiation oncology treatment process just as earlier disruptive radiation therapy technologies have done.
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Affiliation(s)
- John Ng
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States,*Correspondence: John Ng,
| | - Fabiana Gregucci
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States,Department of Radiation Oncology, Miulli General Regional Hospital, Acquaviva delle Fonti, Bari, Italy
| | - Ryan T. Pennell
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States
| | - Himanshu Nagar
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States
| | - Encouse B. Golden
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States
| | | | | | - Silvia C. Formenti
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States
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15
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Guerini AE, Nici S, Magrini SM, Riga S, Toraci C, Pegurri L, Facheris G, Cozzaglio C, Farina D, Liserre R, Gasparotti R, Ravanelli M, Rondi P, Spiazzi L, Buglione M. Adoption of Hybrid MRI-Linac Systems for the Treatment of Brain Tumors: A Systematic Review of the Current Literature Regarding Clinical and Technical Features. Technol Cancer Res Treat 2023; 22:15330338231199286. [PMID: 37774771 PMCID: PMC10542234 DOI: 10.1177/15330338231199286] [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: 04/28/2023] [Revised: 07/24/2023] [Accepted: 08/08/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND Possible advantages of magnetic resonance (MR)-guided radiation therapy (MRgRT) for the treatment of brain tumors include improved definition of treatment volumes and organs at risk (OARs) that could allow margin reductions, resulting in limited dose to the OARs and/or dose escalation to target volumes. Recently, hybrid systems integrating a linear accelerator and an magnetic resonance imaging (MRI) scan (MRI-linacs, MRL) have been introduced, that could potentially lead to a fully MRI-based treatment workflow. METHODS We performed a systematic review of the published literature regarding the adoption of MRL for the treatment of primary or secondary brain tumors (last update November 3, 2022), retrieving a total of 2487 records; after a selection based on title and abstracts, the full text of 74 articles was analyzed, finally resulting in the 52 papers included in this review. RESULTS AND DISCUSSION Several solutions have been implemented to achieve a paradigm shift from CT-based radiotherapy to MRgRT, such as the management of geometric integrity and the definition of synthetic CT models that estimate electron density. Multiple sequences have been optimized to acquire images with adequate quality with on-board MR scanner in limited times. Various sophisticated algorithms have been developed to compensate the impact of magnetic field on dose distribution and calculate daily adaptive plans in a few minutes with satisfactory dosimetric parameters for the treatment of primary brain tumors and cerebral metastases. Dosimetric studies and preliminary clinical experiences demonstrated the feasibility of treating brain lesions with MRL. CONCLUSIONS The adoption of an MRI-only workflow is feasible and could offer several advantages for the treatment of brain tumors, including superior image quality for lesions and OARs and the possibility to adapt the treatment plan on the basis of daily MRI. The growing body of clinical data will clarify the potential benefit in terms of toxicity and response to treatment.
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Affiliation(s)
- Andrea Emanuele Guerini
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
- Co-first authors
| | - Stefania Nici
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
- Co-first authors
| | - Stefano Maria Magrini
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
| | - Stefano Riga
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
| | - Cristian Toraci
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
| | - Ludovica Pegurri
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
| | - Giorgio Facheris
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
| | - Claudia Cozzaglio
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
| | - Davide Farina
- Radiology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Roberto Liserre
- Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy
| | - Roberto Gasparotti
- Neuroradiology Unit, Department of Medical-Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Marco Ravanelli
- Radiology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Paolo Rondi
- Radiology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Luigi Spiazzi
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
- Co-last author
| | - Michela Buglione
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
- Co-last author
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16
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Tseng CL, Chen H, Stewart J, Lau AZ, Chan RW, Lawrence LSP, Myrehaug S, Soliman H, Detsky J, Lim-Fat MJ, Lipsman N, Das S, Heyn C, Maralani PJ, Binda S, Perry J, Keller B, Stanisz GJ, Ruschin M, Sahgal A. High grade glioma radiation therapy on a high field 1.5 Tesla MR-Linac - workflow and initial experience with daily adapt-to-position (ATP) MR guidance: A first report. Front Oncol 2022; 12:1060098. [PMID: 36518316 PMCID: PMC9742425 DOI: 10.3389/fonc.2022.1060098] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 11/10/2022] [Indexed: 07/30/2023] Open
Abstract
Purpose This study reports the workflow and initial clinical experience of high grade glioma (HGG) radiotherapy on the 1.5 T MR-Linac (MRL), with a focus on the temporal variations of the tumor and feasibility of multi-parametric image (mpMRI) acquisition during routine treatment workflow. Materials and methods Ten HGG patients treated with radiation within the first year of the MRL's clinical operation, between October 2019 and August 2020, were identified from a prospective database. Workflow timings were recorded and online adaptive plans were generated using the Adapt-To-Position (ATP) workflow. Temporal variation within the FLAIR hyperintense region (FHR) was assessed by the relative FHR volumes (n = 281 contours) and migration distances (maximum linear displacement of the volume). Research mpMRIs were acquired on the MRL during radiation and changes in selected functional parameters were investigated within the FHR. Results All patients completed radiotherapy to a median dose of 60 Gy (range, 54-60 Gy) in 30 fractions (range, 30-33), receiving a total of 287 fractions on the MRL. The mean in-room time per fraction with or without post-beam research imaging was 42.9 minutes (range, 25.0-69.0 minutes) and 37.3 minutes (range, 24.0-51.0 minutes), respectively. Three patients (30%) required re-planning between fractions 9 to 12 due to progression of tumor and/or edema identified on daily MRL imaging. At the 10, 20, and 30-day post-first fraction time points 3, 3, and 4 patients, respectively, had a FHR volume that changed by at least 20% relative to the first fraction. Research mpMRIs were successfully acquired on the MRL. The median apparent diffusion coefficient (ADC) within the FHR and the volumes of FLAIR were significantly correlated when data from all patients and time points were pooled (R=0.68, p<.001). Conclusion We report the first clinical series of HGG patients treated with radiotherapy on the MRL. The ATP workflow and treatment times were clinically acceptable, and daily online MRL imaging triggered adaptive re-planning for selected patients. Acquisition of mpMRIs was feasible on the MRL during routine treatment workflow. Prospective clinical outcomes data is anticipated from the ongoing UNITED phase 2 trial to further refine the role of MR-guided adaptive radiotherapy.
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Affiliation(s)
- Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Hanbo Chen
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - James Stewart
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Angus Z. Lau
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Rachel W. Chan
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada
| | | | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mary Jane Lim-Fat
- Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Nir Lipsman
- Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sunit Das
- Division of Neurosurgery, St. Michael’s Hospital, University of Toronto, Toronto, ON, Canada
| | - Chinthaka Heyn
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Pejman J. Maralani
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Shawn Binda
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - James Perry
- Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Brian Keller
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Greg J. Stanisz
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Neurosurgery and Paediatric Neurosurgery, Medical University, Lublin, Poland
| | - Mark Ruschin
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
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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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18
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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: 19] [Impact Index Per Article: 6.3] [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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19
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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: 10] [Impact Index Per Article: 3.3] [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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