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Meng Y, Lee MD, Berger A, Wiggins R, O'Callaghan J, Bernstein K, Santhumayor B, Block KT, Fatterpekar G, Kondziolka D. Understanding Permeability Changes in Vestibular Schwannomas as Part of the Dynamic Response to Radiosurgery Using Golden-Angle Radial Sparse Parallel Imaging: A Retrospective Study. Neurosurgery 2024:00006123-990000000-01460. [PMID: 39625281 DOI: 10.1227/neu.0000000000003288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 09/30/2024] [Indexed: 12/06/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Vestibular schwannomas demonstrate different responses after stereotactic radiosurgery (SRS), commonly including a transient loss of internal enhancement on postcontrast T1-weighted MRI thought to be due to an early reduction in tumor vascularity. We used dynamic contrast-enhanced based golden-angle radial sparse parallel (GRASP) MRI to characterize the vascular permeability changes underlying this phenomenon, with correlations to long-term tumor regression. METHODS Consecutive patients with vestibular schwannoma who underwent SRS between 2017 and 2019, had a transient loss of enhancement after SRS, and had long-term longitudinal GRASP studies (6, 18, and 30 months) were included in this retrospective cohort analysis (n = 19). Using GRAVIS ( https://gravis-imaging.org/gravis/ ), an analysis pipeline for GRASP studies, we extracted the key parameters normalized to the venous sinus from a region of interest within the tumor. RESULTS The peak, area under the curve (AUC), and wash-in phase slope were significantly reduced at 6, 18, and 30 months after SRS (corrected P < .05), even while the internal enhancement returned in the tumors. Larger pre-SRS tumors were more likely to have a greater reduction in peak ( P = .013) and AUC ( P = .029) at 6 months. In a subset of patients (N = 13) with long-term follow-up, the median percentage reduction in tumor volume was 58% at a median of 62 months. These patients showed a strong correlation between peak, AUC, and wash-in phase slope changes at 6 months and tumor volume at the last follow-up. CONCLUSION After SRS and loss of internal contrast uptake within vestibular schwannomas, a slow vascular permeability dynamic persisted, suggesting the presence of postradiation processes such as fibrosis. We show for the first time, using GRASP, a quantitative assessment of the vascular radiobiological effect.
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Affiliation(s)
- Ying Meng
- Department of Neurosurgery, NYU Grossman School of Medicine, New York , New York , USA
| | - Matthew D Lee
- Department of Radiology, NYU Grossman School of Medicine, New York , New York , USA
| | - Assaf Berger
- Department of Neurosurgery, NYU Grossman School of Medicine, New York , New York , USA
| | - Roy Wiggins
- Department of Radiology, NYU Grossman School of Medicine, New York , New York , USA
| | - James O'Callaghan
- Department of Radiology, NYU Grossman School of Medicine, New York , New York , USA
| | - Kenneth Bernstein
- Department of Neurosurgery, NYU Grossman School of Medicine, New York , New York , USA
- Department of Radiation Oncology, NYU Grossman School of Medicine, New York , New York , USA
| | - Brandon Santhumayor
- Department of Neurosurgery, NYU Grossman School of Medicine, New York , New York , USA
| | - Kai Tobias Block
- Department of Radiology, NYU Grossman School of Medicine, New York , New York , USA
| | - Girish Fatterpekar
- Department of Radiology, NYU Grossman School of Medicine, New York , New York , USA
| | - Douglas Kondziolka
- Department of Neurosurgery, NYU Grossman School of Medicine, New York , New York , USA
- Department of Radiation Oncology, NYU Grossman School of Medicine, New York , New York , USA
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Conte M, Woodall RT, Gutova M, Chen BT, Shiroishi MS, Brown CE, Munson JM, Rockne RC. Structural and practical identifiability of contrast transport models for DCE-MRI. PLoS Comput Biol 2024; 20:e1012106. [PMID: 38748755 PMCID: PMC11132485 DOI: 10.1371/journal.pcbi.1012106] [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: 12/21/2023] [Revised: 05/28/2024] [Accepted: 04/24/2024] [Indexed: 05/28/2024] Open
Abstract
Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.
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Affiliation(s)
- Martina Conte
- Department of Mathematical Sciences “G. L. Lagrange”, Politecnico di Torino, Torino, Italy
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Ryan T. Woodall
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Margarita Gutova
- Department of Stem Cell Biology and Regenerative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Bihong T. Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, California, United States of America
| | - Mark S. Shiroishi
- Department of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, California, United States of America
| | - Christine E. Brown
- Departments of Hematology & Hematopoietic Cell Transplantation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center Duarte, California, United States of America
| | - Jennifer M. Munson
- Fralin Biomedical Research Institute, Virginia Tech, Roanoke, Virginia, United States of America
| | - Russell C. Rockne
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
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3
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Conte M, Woodall RT, Gutova M, Chen BT, Shiroishi MS, Brown CE, Munson JM, Rockne RC. Structural and practical identifiability of contrast transport models for DCE-MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.19.572294. [PMID: 38187554 PMCID: PMC10769233 DOI: 10.1101/2023.12.19.572294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Compartment models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.
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4
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LoCastro E, Paudyal R, Konar AS, LaViolette PS, Akin O, Hatzoglou V, Goh AC, Bochner BH, Rosenberg J, Wong RJ, Lee NY, Schwartz LH, Shukla-Dave A. A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology. Tomography 2023; 9:2052-2066. [PMID: 37987347 PMCID: PMC10661267 DOI: 10.3390/tomography9060161] [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: 09/14/2023] [Revised: 10/12/2023] [Accepted: 10/18/2023] [Indexed: 11/22/2023] Open
Abstract
There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.
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Affiliation(s)
- Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Peter S. LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA;
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Alvin C. Goh
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Bernard H. Bochner
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Jonathan Rosenberg
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Richard J. Wong
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Nancy Y. Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Lawrence H. Schwartz
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
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5
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Sandulache VC, Hernandez DJ. Response to letter to the editor: Early detection of mandible osteoradionecrosis risk in a high comorbidity veteran population. Am J Otolaryngol 2023; 44:103892. [PMID: 37068321 DOI: 10.1016/j.amjoto.2023.103892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 04/05/2023] [Indexed: 04/19/2023]
Affiliation(s)
- Vlad C Sandulache
- Department of Otolaryngology - Head and Neck Surgery, Baylor College of Medicine, Houston, TX, United States of America; ENT Section, Operative Care Line, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, United States of America.
| | - David J Hernandez
- Department of Otolaryngology - Head and Neck Surgery, Baylor College of Medicine, Houston, TX, United States of America; ENT Section, Operative Care Line, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, United States of America
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Sinno N, Taylor E, Hompland T, Milosevic M, Jaffray DA, Coolens C. Incorporating cross-voxel exchange for the analysis of dynamic contrast-enhanced imaging data: pre-clinical results. Phys Med Biol 2022; 67. [PMID: 36541560 DOI: 10.1088/1361-6560/aca512] [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: 12/10/2021] [Accepted: 11/22/2022] [Indexed: 11/23/2022]
Abstract
Tumours exhibit abnormal interstitial structures and vasculature function often leading to impaired and heterogeneous drug delivery. The disproportionate spatial accumulation of a drug in the interstitium is determined by several microenvironmental properties (blood vessel distribution and permeability, gradients in the interstitial fluid pressure). Predictions of tumour perfusion are key determinants of drug delivery and responsiveness to therapy. Pharmacokinetic models allow for the quantification of tracer perfusion based on contrast enhancement measured with non-invasive imaging techniques. An advanced cross-voxel exchange model (CVXM) was recently developed to provide a comprehensive description of tracer extravasation as well as advection and diffusion based on cross-voxel tracer kinetics (Sinnoet al2021). Transport parameters were derived from DCE-MRI of twenty TS-415 human cervical carcinoma xenografts by using CVXM. Tracer velocity flows were measured at the tumour periphery (mean 1.78-5.82μm.s-1) pushing the contrast outward towards normal tissue. These elevated velocity measures and extravasation rates explain the heterogeneous distribution of tracer across the tumour and its accumulation at the periphery. Significant values for diffusivity were deduced across the tumours (mean 152-499μm2.s-1). CVXM resulted in generally smaller values for the extravasation parameterKext(mean 0.01-0.04 min-1) and extravascular extracellular volume fractionve(mean 0.05-0.17) compared to the standard Tofts parameters, suggesting that Toft model underestimates the effects of inter-voxel exchange. The ratio of Tofts' extravasation parameters over CVXM's was significantly positively correlated to the cross-voxel diffusivity (P< 0.0001) and velocity (P= 0.0005). Tofts' increasedvemeasurements were explained using Sinnoet al(2021)'s theoretical work. Finally, a scan time of 15 min renders informative estimations of the transport parameters. However, a duration as low as 7.5 min is acceptable to recognize the spatial variation of transport parameters. The results demonstrate the potential of utilizing CVXM for determining metrics characterizing the exchange of tracer between the vasculature and the tumour tissue. Like for many earlier models, additional work is strongly recommended, in terms of validation, to develop more confidence in the results, motivating future laboratory work in this regard.
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Affiliation(s)
- Noha Sinno
- The Institute of Biomedical Engineering (BME), University of Toronto, Toronto, Canada.,The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Edward Taylor
- The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada
| | - Tord Hompland
- Department of Radiation Biology, Oslo University Hospital, Oslo, Norway
| | - Michael Milosevic
- The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Institute of Medical Science, University of Toronto, Toronto, Canada
| | - David A Jaffray
- The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,University of Texas, MD Anderson Cancer Centre, Texas, United States of America
| | - Catherine Coolens
- The Institute of Biomedical Engineering (BME), University of Toronto, Toronto, Canada.,The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
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7
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Gouel P, Hapdey S, Dumouchel A, Gardin I, Torfeh E, Hinault P, Vera P, Thureau S, Gensanne D. Synthetic MRI for Radiotherapy Planning for Brain and Prostate Cancers: Phantom Validation and Patient Evaluation. Front Oncol 2022; 12:841761. [PMID: 35515105 PMCID: PMC9065558 DOI: 10.3389/fonc.2022.841761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose We aimed to evaluate the accuracy of T1 and T2 mappings derived from a multispectral pulse sequence (magnetic resonance image compilation, MAGiC®) on 1.5-T MRI and with conventional sequences [gradient echo with variable flip angle (GRE-VFA) and multi-echo spin echo (ME-SE)] compared to the reference values for the purpose of radiotherapy treatment planning. Methods The accuracy of T1 and T2 measurements was evaluated with 2 coils [head and neck unit (HNU) and BODY coils] on phantoms using descriptive statistics and Bland–Altman analysis. The reproducibility and repeatability of T1 and T2 measurements were performed on 15 sessions with the HNU coil. The T1 and T2 synthetic sequences obtained by both methods were evaluated according to quality assurance (QA) requirements for radiotherapy. T1 and T2in vivo measurements of the brain or prostate tissues of two groups of five subjects were also compared. Results The phantom results showed good agreement (mean bias, 8.4%) between the two measurement methods for T1 values between 490 and 2,385 ms and T2 values between 25 and 400 ms. MAGiC® gave discordant results for T1 values below 220 ms (bias with the reference values, from 38% to 1,620%). T2 measurements were accurately estimated below 400 ms (mean bias, 8.5%) by both methods. The QA assessments are in agreement with the recommendations of imaging for contouring purposes for radiotherapy planning. On patient data of the brain and prostate, the measurements of T1 and T2 by the two quantitative MRI (qMRI) methods were comparable (max difference, <7%). Conclusion This study shows that the accuracy, reproducibility, and repeatability of the multispectral pulse sequence (MAGiC®) were compatible with its use for radiotherapy treatment planning in a range of values corresponding to soft tissues. Even validated for brain imaging, MAGiC® could potentially be used for prostate qMRI.
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Affiliation(s)
- Pierrick Gouel
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Sebastien Hapdey
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Arthur Dumouchel
- Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Isabelle Gardin
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - Eva Torfeh
- Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - Pauline Hinault
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France
| | - Pierre Vera
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Sebastien Thureau
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - David Gensanne
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
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Woodall RT, Sahoo P, Cui Y, Chen BT, Shiroishi MS, Lavini C, Frankel P, Gutova M, Brown CE, Munson JM, Rockne RC. Repeatability of tumor perfusion kinetics from dynamic contrast-enhanced MRI in glioblastoma. Neurooncol Adv 2022; 3:vdab174. [PMID: 34988454 PMCID: PMC8715899 DOI: 10.1093/noajnl/vdab174] [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] [Indexed: 11/14/2022] Open
Abstract
Background Dynamic contrast-enhanced MRI (DCE-MRI) parameters have been shown to be biomarkers for treatment response in glioblastoma (GBM). However, variations in analysis and measurement methodology complicate determination of biological changes measured via DCE. The aim of this study is to quantify DCE-MRI variations attributable to analysis methodology and image quality in GBM patients. Methods The Extended Tofts model (eTM) and Leaky Tracer Kinetic Model (LTKM), with manually and automatically segmented vascular input functions (VIFs), were used to calculate perfusion kinetic parameters from 29 GBM patients with double-baseline DCE-MRI data. DCE-MRI images were acquired 2-5 days apart with no change in treatment. Repeatability of kinetic parameters was quantified with Bland-Altman and percent repeatability coefficient (%RC) analysis. Results The perfusion parameter with the least RC was the plasma volume fraction (v p ), with a %RC of 53%. The extra-cellular extra-vascular volume fraction (v e ) %RC was 82% and 81%, for extended Tofts-Kety Model (eTM) and LTKM respectively. The %RC of the volume transfer rate constant (K trans ) was 72% for the eTM, and 82% for the LTKM, respectively. Using an automatic VIF resulted in smaller %RCs for all model parameters, as compared to manual VIF. Conclusions As much as 72% change in K trans (eTM, autoVIF) can be attributable to non-biological changes in the 2-5 days between double-baseline imaging. Poor K trans repeatability may result from inferior temporal resolution and short image acquisition time. This variation suggests DCE-MRI repeatability studies should be performed institutionally, using an automatic VIF method and following quantitative imaging biomarkers alliance guidelines.
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Affiliation(s)
- Ryan T Woodall
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Prativa Sahoo
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Yujie Cui
- Division of Biostatistics, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope, Duarte, California, USA
| | - Mark S Shiroishi
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Cristina Lavini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Paul Frankel
- Division of Biostatistics, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Margarita Gutova
- Department of Stem Cell Biology and Regenerative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Christine E Brown
- Department of Hematology & Hematopoietic Cell Transplantation, Beckman Research Institute, City of Hope, Duarte, California, USA.,Department of Immuno-Oncology, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Jennifer M Munson
- Department of Biomedical Engineering & Mechanics, Fralin Biomedical Research Institute, Virginia Tech, Roanoke, Virginia, USA
| | - Russell C Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
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Wu CH, Lirng JF, Wu HM, Ling YH, Wang YF, Fuh JL, Lin CJ, Ling K, Wang SJ, Chen SP. Blood-Brain Barrier Permeability in Patients With Reversible Cerebral Vasoconstriction Syndrome Assessed With Dynamic Contrast-Enhanced MRI. Neurology 2021; 97:e1847-e1859. [PMID: 34504032 DOI: 10.1212/wnl.0000000000012776] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 08/23/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Blood-brain barrier (BBB) disruption has been proposed to be important in the pathogenesis of reversible cerebral vasoconstriction syndrome (RCVS), but not all patients present an identifiable macroscopic BBB disruption; that is, visible contrast leakage on contrast-enhanced T2 fluid-attenuated inversion recovery imaging. This study aimed to evaluate microscopic BBB permeability and its dynamic change in patients with RCVS. METHODS This prospective cohort implemented 3T dynamic contrast-enhanced MRI. We measured microscopic BBB permeability by determining the whole-brain and white matter hyperintensity (WMH) Ktrans values and evaluated the correlation of whole-brain Ktrans permeability with clinical and vascular measures in transcranial color-coded sonography. RESULTS In total, 176 patients (363 scans) were analyzed and separated into acute (≦30 days) and remission (≧90 days) groups based on the onset-to-examination time. Whole-brain Ktrans values were similar between patients with and without macroscopic BBB disruption in either acute or remission stage. The whole-brain Ktrans was significantly decreased (p < 0.001) from acute to remission stages. The WMH Ktrans was significantly higher than mirror references and decreased from acute to remission stages (p < 0.001). Whole-brain Ktrans correlated with mean pulsatility index (r s = 0.5, p = 0.029), mean resistance index (r s = 0.662, p = 0.002), and distal-to-proximal ratio of resistance index (r s = 0.801, p < 0.001) of M1 segment of middle cerebral arteries at around 10-15 days after onset. The time-trend curve of whole-brain Ktrans depicted dynamic changes during disease course, similar to temporal trends of vasoconstrictions and WMH. DISCUSSION Patients with RCVS presented increased microscopic brain permeability during acute stage, even without discernible macroscopic BBB disruption. The dynamic changes in BBB permeability may be related to impaired cerebral microvascular compliance and WMH formation.
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Affiliation(s)
- Chia-Hung Wu
- From the Department of Radiology (C.-H.W., J.-F.L., H.-M.W., C.-J.L., K.L.), Department of Neurology, Neurological Institute (Y.-H.L., Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), and Division of Translational Research, Department of Medical Research (S.-P.C.), Taipei Veterans General Hospital; and Institute of Clinical Medicine (C.-H.W., S.-P.C.), School of Medicine (C.-H.W., J.-F.L., H.-M.W., Y.-H.L., Y.-F.W., J.-L.F., C.-J.L., K.L., S.-J.W., S.-P.C.), and Brain Research Center (Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jiing-Feng Lirng
- From the Department of Radiology (C.-H.W., J.-F.L., H.-M.W., C.-J.L., K.L.), Department of Neurology, Neurological Institute (Y.-H.L., Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), and Division of Translational Research, Department of Medical Research (S.-P.C.), Taipei Veterans General Hospital; and Institute of Clinical Medicine (C.-H.W., S.-P.C.), School of Medicine (C.-H.W., J.-F.L., H.-M.W., Y.-H.L., Y.-F.W., J.-L.F., C.-J.L., K.L., S.-J.W., S.-P.C.), and Brain Research Center (Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hsiu-Mei Wu
- From the Department of Radiology (C.-H.W., J.-F.L., H.-M.W., C.-J.L., K.L.), Department of Neurology, Neurological Institute (Y.-H.L., Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), and Division of Translational Research, Department of Medical Research (S.-P.C.), Taipei Veterans General Hospital; and Institute of Clinical Medicine (C.-H.W., S.-P.C.), School of Medicine (C.-H.W., J.-F.L., H.-M.W., Y.-H.L., Y.-F.W., J.-L.F., C.-J.L., K.L., S.-J.W., S.-P.C.), and Brain Research Center (Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Hsiang Ling
- From the Department of Radiology (C.-H.W., J.-F.L., H.-M.W., C.-J.L., K.L.), Department of Neurology, Neurological Institute (Y.-H.L., Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), and Division of Translational Research, Department of Medical Research (S.-P.C.), Taipei Veterans General Hospital; and Institute of Clinical Medicine (C.-H.W., S.-P.C.), School of Medicine (C.-H.W., J.-F.L., H.-M.W., Y.-H.L., Y.-F.W., J.-L.F., C.-J.L., K.L., S.-J.W., S.-P.C.), and Brain Research Center (Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen-Feng Wang
- From the Department of Radiology (C.-H.W., J.-F.L., H.-M.W., C.-J.L., K.L.), Department of Neurology, Neurological Institute (Y.-H.L., Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), and Division of Translational Research, Department of Medical Research (S.-P.C.), Taipei Veterans General Hospital; and Institute of Clinical Medicine (C.-H.W., S.-P.C.), School of Medicine (C.-H.W., J.-F.L., H.-M.W., Y.-H.L., Y.-F.W., J.-L.F., C.-J.L., K.L., S.-J.W., S.-P.C.), and Brain Research Center (Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jong-Ling Fuh
- From the Department of Radiology (C.-H.W., J.-F.L., H.-M.W., C.-J.L., K.L.), Department of Neurology, Neurological Institute (Y.-H.L., Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), and Division of Translational Research, Department of Medical Research (S.-P.C.), Taipei Veterans General Hospital; and Institute of Clinical Medicine (C.-H.W., S.-P.C.), School of Medicine (C.-H.W., J.-F.L., H.-M.W., Y.-H.L., Y.-F.W., J.-L.F., C.-J.L., K.L., S.-J.W., S.-P.C.), and Brain Research Center (Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chung-Jung Lin
- From the Department of Radiology (C.-H.W., J.-F.L., H.-M.W., C.-J.L., K.L.), Department of Neurology, Neurological Institute (Y.-H.L., Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), and Division of Translational Research, Department of Medical Research (S.-P.C.), Taipei Veterans General Hospital; and Institute of Clinical Medicine (C.-H.W., S.-P.C.), School of Medicine (C.-H.W., J.-F.L., H.-M.W., Y.-H.L., Y.-F.W., J.-L.F., C.-J.L., K.L., S.-J.W., S.-P.C.), and Brain Research Center (Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Kan Ling
- From the Department of Radiology (C.-H.W., J.-F.L., H.-M.W., C.-J.L., K.L.), Department of Neurology, Neurological Institute (Y.-H.L., Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), and Division of Translational Research, Department of Medical Research (S.-P.C.), Taipei Veterans General Hospital; and Institute of Clinical Medicine (C.-H.W., S.-P.C.), School of Medicine (C.-H.W., J.-F.L., H.-M.W., Y.-H.L., Y.-F.W., J.-L.F., C.-J.L., K.L., S.-J.W., S.-P.C.), and Brain Research Center (Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shuu-Jiun Wang
- From the Department of Radiology (C.-H.W., J.-F.L., H.-M.W., C.-J.L., K.L.), Department of Neurology, Neurological Institute (Y.-H.L., Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), and Division of Translational Research, Department of Medical Research (S.-P.C.), Taipei Veterans General Hospital; and Institute of Clinical Medicine (C.-H.W., S.-P.C.), School of Medicine (C.-H.W., J.-F.L., H.-M.W., Y.-H.L., Y.-F.W., J.-L.F., C.-J.L., K.L., S.-J.W., S.-P.C.), and Brain Research Center (Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shih-Pin Chen
- From the Department of Radiology (C.-H.W., J.-F.L., H.-M.W., C.-J.L., K.L.), Department of Neurology, Neurological Institute (Y.-H.L., Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), and Division of Translational Research, Department of Medical Research (S.-P.C.), Taipei Veterans General Hospital; and Institute of Clinical Medicine (C.-H.W., S.-P.C.), School of Medicine (C.-H.W., J.-F.L., H.-M.W., Y.-H.L., Y.-F.W., J.-L.F., C.-J.L., K.L., S.-J.W., S.-P.C.), and Brain Research Center (Y.-F.W., J.-L.F., S.-J.W., S.-P.C.), National Yang Ming Chiao Tung University, Taipei, Taiwan.
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10
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Bendinger AL, Welzel T, Huang L, Babushkina I, Peschke P, Debus J, Glowa C, Karger CP, Saager M. DCE-MRI detected vascular permeability changes in the rat spinal cord do not explain shorter latency times for paresis after carbon ions relative to photons. Radiother Oncol 2021; 165:126-134. [PMID: 34634380 DOI: 10.1016/j.radonc.2021.09.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND PURPOSE Radiation-induced myelopathy, an irreversible complication occurring after a long symptom-free latency time, is preceded by a fixed sequence of magnetic resonance- (MR-) visible morphological alterations. Vascular degradation is assumed the main reason for radiation-induced myelopathy. We used dynamic contrast-enhanced (DCE-) MRI to identify different vascular changes after photon and carbon ion irradiation, which precede or coincide with morphological changes. MATERIALS AND METHODS The cervical spinal cord of rats was irradiated with iso-effective photon or carbon (12C-)ion doses. Afterwards, animals underwent frequent DCE-MR imaging until they developed symptomatic radiation-induced myelopathy (paresis II). Measurements were performed at certain time points: 1 month, 2 months, 3 months, 4 months, and 6 months after irradiation, and when animals showed morphological (such as edema/syrinx/contrast agent (CA) accumulation) or neurological alterations (such as, paresis I, and paresis II). DCE-MRI data was analyzed using the extended Toft's model. RESULTS Fit quality improved with gradual disintegration of the blood spinal cord barrier (BSCB) towards paresis II. Vascular permeability increased three months after photon irradiation, and rapidly escalated after animals showed MR-visible morphological changes until paresis II. After 12C-ion irradiation, vascular permeability increased when animals showed morphological alterations and increased further until animals had paresis II. The volume transfer constant and the plasma volume showed no significant changes. CONCLUSION Only after photon irradiation, DCE-MRI provides a temporal advantage in detecting early physiological signs in radiation-induced myelopathy compared to morphological MRI. As a generally lower level of vascular permeability after 12C-ions led to an earlier development of paresis as compared to photons, we conclude that other mechanisms dominate the development of paresis II.
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Affiliation(s)
- Alina L Bendinger
- Dept. of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany.
| | - Thomas Welzel
- Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany; Dept. of Radiation Oncology and Radiotherapy, University Hospital of Heidelberg, Heidelberg, Germany
| | - Lifi Huang
- Dept. of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany; Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Inna Babushkina
- Core Facility Small Animal Imaging Center, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter Peschke
- Dept. of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Dept. of Radiation Oncology and Radiotherapy, University Hospital of Heidelberg, Heidelberg, Germany
| | - Jürgen Debus
- Dept. of Radiation Oncology and Radiotherapy, University Hospital of Heidelberg, Heidelberg, Germany; Clinical Cooperation Unit Radiation Therapy, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christin Glowa
- Dept. of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany; Dept. of Radiation Oncology and Radiotherapy, University Hospital of Heidelberg, Heidelberg, Germany
| | - Christian P Karger
- Dept. of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
| | - Maria Saager
- Dept. of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
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11
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Wu C, Hormuth DA, Easley T, Eijkhout V, Pineda F, Karczmar GS, Yankeelov TE. An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom. Med Image Anal 2021; 73:102186. [PMID: 34329903 PMCID: PMC8453106 DOI: 10.1016/j.media.2021.102186] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 07/08/2021] [Accepted: 07/16/2021] [Indexed: 10/20/2022]
Abstract
Quantitative evaluation of an image processing method to perform as designed is central to both its utility and its ability to guide the data acquisition process. Unfortunately, these tasks can be quite challenging due to the difficulty of experimentally obtaining the "ground truth" data to which the output of a given processing method must be compared. One way to address this issue is via "digital phantoms", which are numerical models that provide known biophysical properties of a particular object of interest. In this contribution, we propose an in silico validation framework for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition and analysis methods that employs a novel dynamic digital phantom. The phantom provides a spatiotemporally-resolved representation of blood-interstitial flow and contrast agent delivery, where the former is solved by a 1D-3D coupled computational fluid dynamic system, and the latter described by an advection-diffusion equation. Furthermore, we establish a virtual simulator which takes as input the digital phantom, and produces realistic DCE-MRI data with controllable acquisition parameters. We assess the performance of a simulated standard-of-care acquisition (Protocol A) by its ability to generate contrast-enhanced MR images that separate vasculature from surrounding tissue, as measured by the contrast-to-noise ratio (CNR). We find that the CNR significantly decreases as the spatial resolution (SRA, where the subscript indicates Protocol A) or signal-to-noise ratio (SNRA) decreases. Specifically, with an SNRA / SRA = 75 dB / 30 μm, the median CNR is 77.30, whereas an SNRA / SRA = 5 dB / 300 μm reduces the CNR to 6.40. Additionally, we assess the performance of simulated ultra-fast acquisition (Protocol B) by its ability to generate DCE-MR images that capture contrast agent pharmacokinetics, as measured by error in the signal-enhancement ratio (SER) compared to ground truth (PESER). We find that PESER significantly decreases the as temporal resolution (TRB) increases. Similar results are reported for the effects of spatial resolution and signal-to-noise ratio on PESER. For example, with an SNRB / SRB / TRB = 5 dB / 300 μm / 10 s, the median PESER is 21.00%, whereas an SNRB / SRB / TRB = 75 dB / 60 μm / 1 s, yields a median PESER of 0.90%. These results indicate that our in silico framework can generate virtual MR images that capture effects of acquisition parameters on the ability of generated images to capture morphological or pharmacokinetic features. This validation framework is not only useful for investigations of perfusion-based MRI techniques, but also for the systematic evaluation and optimization new MRI acquisition, reconstruction, and image processing techniques.
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Affiliation(s)
- Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712, United States.
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712, United States; Livestrong Cancer Institutes, United States
| | - Ty Easley
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
| | | | - Federico Pineda
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Gregory S Karczmar
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712, United States; Livestrong Cancer Institutes, United States; Departments of Biomedical Engineering, United States; Departments of Diagnostic Medicine, United States; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, United States; Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77030, United States
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12
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Martín-Noguerol T, Kirsch CFE, Montesinos P, Luna A. Arterial spin labeling for head and neck lesion assessment: technical adjustments and clinical applications. Neuroradiology 2021; 63:1969-1983. [PMID: 34427708 DOI: 10.1007/s00234-021-02772-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 07/12/2021] [Indexed: 12/21/2022]
Abstract
PURPOSE Despite, currently, "state-of-the-art" magnetic resonance imaging (MRI) protocols for head and neck (H&N) lesion assessment incorporate perfusion sequences, these acquisitions require the intravenous injection of exogenous gadolinium-based contrast agents (GBCAs), which may have potential risks. Alternative techniques such as arterial spin labeling (ASL) can provide quantitative microvascular information similar to conventional perfusion sequences for H&N lesions evaluation, as a potential alternative without GBCA administration. METHODS We review the existing literature and analyze the latest evidence regarding ASL in H&N area highlighting the technical adjustments needed for a proper ASL acquisition in this challenging region for lesion characterization, treatment monitoring, and tumor recurrence detection. RESULTS ASL techniques, widely used for central nervous system lesions evaluation, can be also applied to the H&N region. Technical adjustments, especially regarding post-labeling delay, are mandatory to obtain robust and reproducible results. Several studies have demonstrated the feasibility of ASL in the H&N area including the orbits, skull base, paranasal sinuses, upper airway, salivary glands, and thyroid. CONCLUSION ASL is a feasible technique for the assessment of H&N lesions without the need of GBCAs. This manuscript reviews ASL's physical basis, emphasizing the technical adjustments necessary for proper ASL acquisition in this unique and challenging anatomical region, and the main applications in evaluating H&N lesions.
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Affiliation(s)
| | - Claudia F E Kirsch
- Department of Radiology, Northwell Health, Zucker Hofstra School of Medicine At Northwell, North Shore University Hospital, 300 Community Drive, Manhasset, NY, 11030, USA
| | - Paula Montesinos
- Philips Iberia, Calle de María de Portugal, 1, 28050, Madrid, Spain
| | - Antonio Luna
- MRI Unit, Radiology Department, HT Medica, Carmelo Torres 2, 23007, Jaén, Spain
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13
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Hormuth DA, Phillips CM, Wu C, Lima EABF, Lorenzo G, Jha PK, Jarrett AM, Oden JT, Yankeelov TE. Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data. Cancers (Basel) 2021; 13:3008. [PMID: 34208448 PMCID: PMC8234316 DOI: 10.3390/cancers13123008] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/07/2021] [Accepted: 06/13/2021] [Indexed: 01/03/2023] Open
Abstract
Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2-3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.
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Affiliation(s)
- David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Ernesto A. B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78758, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Prashant K. Jha
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Angela M. Jarrett
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
| | - J. Tinsley Oden
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Mathematics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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14
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Wang YF, Tadimalla S, Hayden AJ, Holloway L, Haworth A. Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment. J Med Imaging Radiat Oncol 2021; 65:612-626. [PMID: 34060219 DOI: 10.1111/1754-9485.13242] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/18/2021] [Accepted: 05/02/2021] [Indexed: 12/15/2022]
Abstract
Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.
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Affiliation(s)
- Yu-Feng Wang
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Sirisha Tadimalla
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Amy J Hayden
- Sydney West Radiation Oncology, Westmead Hospital, Wentworthville, New South Wales, Australia
- Faculty of Medicine, Western Sydney University, Sydney, New South Wales, Australia
- Faculty of Medicine, Health & Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
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15
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Coolens C, Gwilliam MN, Alcaide-Leon P, de Freitas Faria IM, Ynoe de Moraes F. Transformational Role of Medical Imaging in (Radiation) Oncology. Cancers (Basel) 2021; 13:cancers13112557. [PMID: 34070984 PMCID: PMC8197089 DOI: 10.3390/cancers13112557] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Onboard, imaging techniques have brought about a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables us to better visualize where to deliver lethal doses of radiation and thus allows the shrinking of necessary geometric margins leading to reduced toxicities. Alongside improvements in treatment delivery, advances in medical imaging have also allowed us to better define the volumes we wish to target. The development of imaging techniques that can capture aspects of the tumor’s biology before, during and after therapy is transforming how treatment can be delivered. Technological changes have further made these biological imaging techniques available in real-time providing the opportunity to monitor a patient’s response to treatment closely and often before any volume changes are visible on conventional radiological images. Here we discuss the development of robust quantitative imaging biomarkers and how they can personalize therapy towards meaningful clinical endpoints. Abstract Onboard, real-time, imaging techniques, from the original megavoltage planar imaging devices, to the emerging combined MRI-Linear Accelerators, have brought a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables lethal doses of radiation to be delivered to target volumes with progressively more accuracy and thus allows shrinking of necessary geometric margins, leading to reduced toxicities. Alongside these improvements in treatment delivery, advances in medical imaging, e.g., PET, and MRI, have also allowed target volumes themselves to be better defined. The development of functional and molecular imaging is now driving a conceptually larger step transformation to both better understand the cancer target and disease to be treated, as well as how tumors respond to treatment. A biological description of the tumor microenvironment is now accepted as an essential component of how to personalize and adapt treatment. This applies not only to radiation oncology but extends widely in cancer management from surgical oncology planning and interventional radiology, to evaluation of targeted drug delivery efficacy in medical oncology/immunotherapy. Here, we will discuss the role and requirements of functional and metabolic imaging techniques in the context of brain tumors and metastases to reliably provide multi-parametric imaging biomarkers of the tumor microenvironment.
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Affiliation(s)
- Catherine Coolens
- Department of Medical Physics, Princess Margaret Cancer Centre & University Health Network, Toronto, ON M5G 1Z5, Canada;
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- TECHNA Institute, University Health Network, Toronto, ON M5G 1Z5, Canada
- Correspondence:
| | - Matt N. Gwilliam
- Department of Medical Physics, Princess Margaret Cancer Centre & University Health Network, Toronto, ON M5G 1Z5, Canada;
| | - Paula Alcaide-Leon
- Joint Department of Medical Imaging, University Health Network, Toronto, ON M5G 1Z5, Canada;
| | | | - Fabio Ynoe de Moraes
- Department of Oncology, Division of Radiation Oncology, Queen’s University, Kingston, ON K7L 5P9, Canada;
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McGee KP, Hwang KP, Sullivan DC, Kurhanewicz J, Hu Y, Wang J, Li W, Debbins J, Paulson E, Olsen JR, Hua CH, Warner L, Ma D, Moros E, Tyagi N, Chung C. Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294. Med Phys 2021; 48:e697-e732. [PMID: 33864283 PMCID: PMC8361924 DOI: 10.1002/mp.14884] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/24/2021] [Accepted: 03/28/2021] [Indexed: 12/16/2022] Open
Abstract
A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.
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Affiliation(s)
- Kiaran P McGee
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, Division of Diagnostic Imaging, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Daniel C Sullivan
- Department of Radiology, Duke University, Durham, North Carolina, USA
| | - John Kurhanewicz
- Department of Radiology, University of California, San Francisco, California, USA
| | - Yanle Hu
- Department of Radiation Oncology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Jihong Wang
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Wen Li
- Department of Radiation Oncology, University of Arizona, Tucson, Arizona, USA
| | - Josef Debbins
- Department of Radiology, Barrow Neurologic Institute, Phoenix, Arizona, USA
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jeffrey R Olsen
- Department of Radiation Oncology, University of Colorado Denver - Anschutz Medical Campus, Denver, Colorado, USA
| | - Chia-Ho Hua
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | | | - Daniel Ma
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Eduardo Moros
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
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17
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Quantitative MRI: Defining repeatability, reproducibility and accuracy for prostate cancer imaging biomarker development. Magn Reson Imaging 2021; 77:169-179. [PMID: 33388362 DOI: 10.1016/j.mri.2020.12.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/25/2020] [Accepted: 12/29/2020] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Quantitative MRI (qMRI) parameters have been increasingly used to develop predictive models to accurately monitor treatment response in prostate cancer after radiotherapy. To reliably detect changes in signal due to treatment response, predictive models require qMRI parameters with high repeatability and reproducibility. The purpose of this study was to measure qMRI parameter uncertainties in both commercial and in-house developed phantoms to guide the development of robust predictive models for monitoring treatment response. MATERIALS AND METHODS ADC, T1, and R2* values were acquired across three 3 T scanners with a prostate-specific qMRI protocol using the NIST/ISMRM system phantom, RSNA/NIST diffusion phantom, and an in-house phantom. A B1 field map was acquired to correct for flip angle inhomogeneity in T1 maps. All sequences were repeated in each scan to assess within-session repeatability. Weekly scans were acquired on one scanner for three months with the in-house phantom. Between-session repeatability was measured with test-retest scans 6-months apart on all scanners with all phantoms. Accuracy, defined as percentage deviation from reference value for ADC and T1, was evaluated using the system and diffusion phantoms. Repeatability and reproducibility coefficients of variation (%CV) were calculated for all qMRI parameters on all phantoms. RESULTS Overall, repeatability CV of ADC was <2.40%, reproducibility CV was <3.98%, and accuracy ranged between -8.0% to 2.7% across all scanners. Applying B1 correction on T1 measurements significantly improved the repeatability and reproducibility (p<0.05) but increased error in accuracy (p<0.001). Repeatability and reproducibility of R2* was <4.5% and <7.3% respectively in the system phantom across all scanners. CONCLUSION Repeatability, reproducibility, and accuracy in qMRI parameters from a prostate-specific protocol was estimated using both commercial and in-house phantoms. Results from this work will be used to identify robust qMRI parameters for use in the development of predictive models to longitudinally monitor treatment response for prostate cancer in current and future clinical trials.
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Paterson C, Hargreaves S, Rumley CN. Functional Imaging to Predict Treatment Response in Head and Neck Cancer: How Close are We to Biologically Adaptive Radiotherapy? Clin Oncol (R Coll Radiol) 2020; 32:861-873. [PMID: 33127234 DOI: 10.1016/j.clon.2020.10.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/28/2020] [Accepted: 10/05/2020] [Indexed: 02/07/2023]
Abstract
It is increasingly recognised that head and neck cancer represents a spectrum of disease with a differential response to standard treatments. Although prognostic factors are well established, they do not reliably predict response. The ability to predict response early during radiotherapy would allow adaptation of treatment: intensifying treatment for those not responding adequately or de-intensifying remaining therapy for those likely to achieve a complete response. Functional imaging offers such an opportunity. Changes in parameters obtained with functional magnetic resonance imaging or positron emission tomography-computed tomography during treatment have been found to be predictive of disease control in head and neck cancer. Although many questions remain unanswered regarding the optimal implementation of these techniques, current, maturing and future studies may provide the much-needed homogeneous cohorts with larger sample sizes and external validation of parameters. With a stepwise and collaborative approach, we may be able to develop imaging biomarkers that allow us to deliver personalised, biologically adaptive radiotherapy for head and neck cancer.
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Affiliation(s)
- C Paterson
- Beatson West of Scotland Cancer Centre, Glasgow, UK.
| | | | - C N Rumley
- Department of Radiation Oncology, Townsville University Hospital, Douglas, Australia; South Western Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia
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Mohamed ASR, He R, Ding Y, Wang J, Fahim J, Elgohari B, Elhalawani H, Kim AD, Ahmed H, Garcia JA, Johnson JM, Stafford RJ, Bankson JA, Chambers MS, Sandulache VC, Fuller CD, Lai SY. Quantitative Dynamic Contrast-Enhanced MRI Identifies Radiation-Induced Vascular Damage in Patients With Advanced Osteoradionecrosis: Results of a Prospective Study. Int J Radiat Oncol Biol Phys 2020; 108:1319-1328. [PMID: 32712257 DOI: 10.1016/j.ijrobp.2020.07.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 06/20/2020] [Accepted: 07/15/2020] [Indexed: 11/17/2022]
Abstract
PURPOSE We aim to characterize the quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters associated with advanced mandibular osteoradionecrosis (ORN) compared with the contralateral normal mandible. METHODS AND MATERIALS Patients with a diagnosis of advanced ORN after curative-intent radiation treatment of head and neck cancer were prospectively enrolled after institutional review board approval and study-specific informed consent were obtained. Quantitative maps generated with the Tofts and extended Tofts pharmacokinetic models were used for analysis. Manual segmentation of advanced ORN 3-dimensional volume was done using anatomic sequences to create ORN volumes of interest (VOIs). Subsequently, normal mandibular VOIs were segmented on the contralateral healthy mandible of similar volume and anatomic location to create control VOIs. Finally, anatomic sequences were coregistered to DCE sequences, and contours were propagated to the respective parameter maps. RESULTS Thirty patients were included. The median time to ORN diagnosis after completion of IMRT was 38 months (range, 6-184 months), whereas median time to ORN progression to advanced grade after initial diagnosis was 5.6 months (range, 0-128 months). There were statistically significant higher Ktrans and Ve in ORN-VOIs compared with controls (0.23 vs 0.07 min-1, and 0.34 vs 0.15; P < .0001 for both). The average relative increase of Ktrans in ORN-VOIs was 3.2-fold higher than healthy mandibular control VOIs. Moreover, the corresponding rise of Ve in ORN-VOIs was 2.7-fold higher than in the controls. Using combined Ktrans and Ve parameters, 27 patients (90%) had at least a 200% increase of either of the studied parameters in the ORN-VOIs compared with their healthy mandible VOIs. CONCLUSIONS Our results confirm that there is a quantitatively significant higher degree of leakiness in the mandibular vasculature as measured using DCE-MRI parameters of areas with advanced ORN versus healthy mandible.
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Affiliation(s)
- Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Renjie He
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Yao Ding
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Jihong Wang
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Joly Fahim
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Andrew D Kim
- Department of Head and Neck Surgery, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Hoda Ahmed
- Department of Head and Neck Surgery, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Jose A Garcia
- Department of Head and Neck Surgery, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Jason M Johnson
- Department of Neuroradiology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - R Jason Stafford
- Department of Imaging Physics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - James A Bankson
- Department of Imaging Physics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Mark S Chambers
- Department of Head and Neck Surgery, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Vlad C Sandulache
- Department of Otolaryngology, Head and Neck Surgery, Baylor College of Medicine, Houston, Texas
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Stephen Y Lai
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas; Department of Head and Neck Surgery, The University of Texas, MD Anderson Cancer Center, Houston, Texas.
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20
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Dikaios N. Stochastic Gradient Langevin dynamics for joint parameterization of tracer kinetic models, input functions, and T1 relaxation-times from undersampled k-space DCE-MRI. Med Image Anal 2020; 62:101690. [DOI: 10.1016/j.media.2020.101690] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 02/16/2020] [Accepted: 03/13/2020] [Indexed: 02/04/2023]
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Multi-contrast imaging information of coronary artery wall based on magnetic resonance angiography. J Infect Public Health 2019; 13:2025-2031. [PMID: 31289006 DOI: 10.1016/j.jiph.2019.06.025] [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: 04/29/2019] [Revised: 06/16/2019] [Accepted: 06/21/2019] [Indexed: 11/23/2022] Open
Abstract
In order to explore the most suitable image acquisition method for coronary artery wall, the display ability and image quality of segmentation breath-holding target volume acquisition method (the breath-holding method) and real-time navigation whole-hearted acquisition method (the navigation method) of coronary artery magnetic resonance angiography (CMRA) were compared. 26 healthy volunteers were selected to accept the CMRA in 1.5 tunnels magneto-resistance (TMR) equipment by the 2 acquisition methods respectively. The arteries were divided into 9 segments according to the standards of the American Heart Association (AHA). The images were evaluated by 2 magnetic resonance physicians. Satisfaction rate and success rate of each segment of the coronary artery were counted. The results showed that the signal to noise ratio (SNR) and the carrier to noise ratio (CNR) of the images obtained by the breath-holding method were higher than those obtained by the navigation method (P<0.05). Therefore, the segmentation breath-holding target volume acquisition method is proved to have a higher image quality and the simpler and more convenient operations, which is more suitable for the acquisition of positioning images of CMRA.
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22
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Cao J, Pickup S, Clendenin C, Blouw B, Choi H, Kang D, Rosen M, O'Dwyer PJ, Zhou R. Dynamic Contrast-enhanced MRI Detects Responses to Stroma-directed Therapy in Mouse Models of Pancreatic Ductal Adenocarcinoma. Clin Cancer Res 2019; 25:2314-2322. [PMID: 30587546 PMCID: PMC6445712 DOI: 10.1158/1078-0432.ccr-18-2276] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 11/20/2018] [Accepted: 12/19/2018] [Indexed: 12/18/2022]
Abstract
PURPOSE The dense stroma underlies the drug resistance of pancreatic ductal adenocarcinoma (PDA) and has motivated the development of stroma-directed drugs. Our objective is to test the concept that dynamic contrast-enhanced (DCE) MRI using FDA-approved contrast media, an imaging method sensitive to the tumor microenvironment, can detect early responses to stroma-directed drug. EXPERIMENTAL DESIGN Imaging studies were performed in three mouse models exhibiting high desmoplastic reactions: the autochthonous PDA in genetically engineered mice (KPC), an orthotopic model in syngeneic mice, and a xenograft model of human PDA in athymic mice. An investigational drug, PEGPH20 (pegvorhyaluronidase alfa), which degrades hyaluronan (HA) in the stroma of PDA, was injected alone or in combination with gemcitabine. RESULTS At 24 hours after a single injection of PEGPH20, Ktrans , a DCE-MRI-derived marker that measures how fast a unit volume of contrast media is transferred from capillaries to interstitial space, increased 56% and 50% from baseline in the orthotopic and xenograft tumors, respectively, compared with a 4% and 6% decrease in vehicle groups (both P < 0.05). Similarly, after three combined treatments, Ktrans in KPC mice increased 54%, whereas it decreased 4% in controls treated with gemcitabine alone (P < 0.05). Consistently, after a single injection of PEGPH20, tumor HA content assessed by IHC was reduced substantially in all three models while drug delivery (measured by paclitaxel accumulation in tumor) was increased by 2.6-fold. CONCLUSIONS These data demonstrated a DCE-MRI marker, Ktrans , can detect early responses to stroma-directed drug and reveal the sustained effect of combination treatment (PEGPH20+ gemcitabine).
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Affiliation(s)
- Jianbo Cao
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
- Medical College, Xiamen University, Xiamen, Fujian, P.R. China
| | - Stephen Pickup
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Cynthia Clendenin
- Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, Pennsylvania
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Hoon Choi
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - David Kang
- Halozyme Therapeutics, San Diego, California
| | - Mark Rosen
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter J O'Dwyer
- Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, Pennsylvania.
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Rong Zhou
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania.
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
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Hormuth DA, Jarrett AM, Lima EA, McKenna MT, Fuentes DT, Yankeelov TE. Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data. JCO Clin Cancer Inform 2019; 3:1-10. [PMID: 30807209 PMCID: PMC6535803 DOI: 10.1200/cci.18.00055] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2018] [Indexed: 12/19/2022] Open
Abstract
Multiparametric imaging is a critical tool in the noninvasive study and assessment of cancer. Imaging methods have evolved over the past several decades to provide quantitative measures of tumor and healthy tissue characteristics related to, for example, cell number, blood volume fraction, blood flow, hypoxia, and metabolism. Mechanistic models of tumor growth also have matured to a point where the incorporation of patient-specific measures could provide clinically relevant predictions of tumor growth and response. In this review, we identify and discuss approaches that use multiparametric imaging data, including diffusion-weighted magnetic resonance imaging, dynamic contrast-enhanced magnetic resonance imaging, diffusion tensor imaging, contrast-enhanced computed tomography, [18F]fluorodeoxyglucose positron emission tomography, and [18F]fluoromisonidazole positron emission tomography to initialize and calibrate mechanistic models of tumor growth and response. We focus the discussion on brain and breast cancers; however, we also identify three emerging areas of application in kidney, pancreatic, and lung cancers. We conclude with a discussion of the future directions for incorporating multiparametric imaging data and mechanistic modeling into clinical decision making for patients with cancer.
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Quantitative Imaging for Radiation Oncology. Int J Radiat Oncol Biol Phys 2018; 102:683-686. [DOI: 10.1016/j.ijrobp.2018.06.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 05/25/2018] [Accepted: 06/05/2018] [Indexed: 11/23/2022]
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Ger RB, Yang J, Ding Y, Jacobsen MC, Cardenas CE, Fuller CD, Howell RM, Li H, Stafford RJ, Zhou S, Court LE. Synthetic head and neck and phantom images for determining deformable image registration accuracy in magnetic resonance imaging. Med Phys 2018; 45:10.1002/mp.13090. [PMID: 30007075 PMCID: PMC6331282 DOI: 10.1002/mp.13090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 05/07/2018] [Accepted: 05/15/2018] [Indexed: 02/01/2023] Open
Abstract
PURPOSE Magnetic resonance imaging (MRI) provides noninvasive evaluation of patient's anatomy without using ionizing radiation. Due to this and the high soft-tissue contrast, MRI use has increased and has potential for use in longitudinal studies where changes in patients' anatomy or tumors at different time points are compared. Deformable image registration can be useful for these studies. Here, we describe two datasets that can be used to evaluate the registration accuracy of systems for MR images, as it cannot be assumed to be the same as that measured on CT images. ACQUISITION AND VALIDATION METHODS Two sets of images were created to test registration accuracy. (a) A porcine phantom was created by placing ten 0.35-mm gold markers into porcine meat. The porcine phantom was immobilized in a plastic container with movable dividers. T1-weighted, T2-weighted, and CT images were acquired with the porcine phantom compressed in four different ways. The markers were not visible on the MR images, due to the selected voxel size, so they did not interfere with the measured registration accuracy, while the markers were visible on the CT images and were used to identify the known deformation between positions. (b) Synthetic images were created using 28 head and neck squamous cell carcinoma patients who had MR scans pre-, mid-, and postradiotherapy treatment. An inter- and intrapatient variation model was created using these patient scans. Four synthetic pretreatment images were created using the interpatient model, and four synthetic post-treatment images were created for each of the pretreatment images using the intrapatient model. DATA FORMAT AND USAGE NOTES The T1-weighted, T2-weighted, and CT scans of the porcine phantom in the four positions are provided. Four T1-weighted synthetic pretreatment images each with four synthetic post-treatment images, and four T2-weighted synthetic pretreatment images each with four synthetic post-treatment images are provided. Additionally, the applied deformation vector fields to generate the synthetic post-treatment images are provided. The data are available on TCIA under the collection MRI-DIR. POTENTIAL APPLICATIONS The proposed database provides two sets of images (one acquired and one computer generated) for use in evaluating deformable image registration accuracy. T1- and T2-weighted images are available for each technique as the different image contrast in the two types of images may impact the registration accuracy.
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Affiliation(s)
- Rachel B. Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Yao Ding
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Megan C. Jacobsen
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Carlos E. Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Clifton D. Fuller
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rebecca M. Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Heng Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - R. Jason Stafford
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Shouhao Zhou
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Laurence E. Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, Lindsay WD, Aerts HJWL, Agrimson B, Deville C, Rosenthal SA, Yu JB, Thomas CR. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiother Oncol 2018; 129:421-426. [PMID: 29907338 DOI: 10.1016/j.radonc.2018.05.030] [Citation(s) in RCA: 131] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/29/2018] [Accepted: 05/30/2018] [Indexed: 12/16/2022]
Abstract
Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.
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Affiliation(s)
- Reid F Thompson
- Oregon Health & Science University, Portland, USA; VA Portland Health Care System, Portland, USA.
| | - Gilmer Valdes
- University of California San Francisco, San Francisco, USA
| | | | | | - Olivier Morin
- University of California San Francisco, San Francisco, USA
| | | | | | - Hugo J W L Aerts
- Brigham and Women's Hospital, Boston, USA; Dana Farber Cancer Institute, Boston, USA
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Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers. Sci Data 2018; 5:180008. [PMID: 29437167 PMCID: PMC5810424 DOI: 10.1038/sdata.2018.8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 12/18/2017] [Indexed: 02/05/2023] Open
Abstract
Dynamic myraidpro contrast-enhanced magnetic resonance imaging (DCE-MRI) has been correlated with prognosis in head and neck squamous cell carcinoma as well as with changes in normal tissues. These studies implement different software, either commercial or in-house, and different scan protocols. Thus, the generalizability of the results is not confirmed. To assist in the standardization of quantitative metrics to confirm the generalizability of these previous studies, this data descriptor delineates in detail the DCE-MRI digital imaging and communications in medicine (DICOM) files with DICOM radiation therapy (RT) structure sets and digital reference objects (DROs), as well as, relevant clinical data that encompass a data set that can be used by all software for comparing quantitative metrics. Variable flip angle (VFA) with six flip angles and DCE-MRI scans with a temporal resolution of 5.5 s were acquired in the axial direction on a 3T MR scanner with a field of view of 25.6 cm, slice thickness of 4 mm, and 256×256 matrix size.
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