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Shen M, Lin X, Yang C, Zhou Z, Chen S, Yin Y, Long L, Huang L, Yang Z, Wang R, Kang M. Potential predictive value of IVIM MR for xerostomia in nasopharyngeal carcinoma. Radiother Oncol 2024; 197:110323. [PMID: 38734144 DOI: 10.1016/j.radonc.2024.110323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 04/24/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND AND PURPOSE Xerostomia, caused by radiation-induced parotid damage, is the most commonly reported radiotherapy (RT) complication for nasopharyngeal carcinoma (NPC). The purpose of this study was to evaluate the value of intravoxel incoherent motion (IVIM) MR in monitoring radiation-induced parotid gland damage and predicting the risk of xerostomia. METHODS Fifty-four NPC patients were enrolled and underwent at least three IVIM MR scans: before (pre-RT), after 5 fractions of (5th-RT), halfway through (mid-RT), and after RT (post-RT). The degree of xerostomia patients was assessed before each MR examination. Furthermore, the time when patients first reported xerostomia symptoms was recorded. The changes in IVIM parameters throughout RT, as well as the relationships between IVIM parameters and xerostomia, were analysed. RESULT All IVIM parameters increased significantly from pre-RT to post-RT (p < 0.001). The rates of D, D* and f increase increased significantly from pre-RT to mid-RT (p < 0.001), indicating that cell necrosis mainly occurs in the first half of RT. In multivariate analysis, N3 (p = 0.014), pre-D (p = 0.007) and pre-D* (p = 0.003) were independent factors influencing xerostomia. D and f were significantly higher at 5th-RT than at pre-RT (both p < 0.05). IVIM detected parotid gland injury at 5th-RT at an average scanning time of 6.18 ± 1.07 days, earlier than the 11.94 ± 2.61 days when the patient first complained of xerostomia according to the RTOG scale (p < 0.001). CONCLUSIONS IVIM MR can dynamically monitor radiation-induced parotid gland damage and assess it earlier and more objectively than RTOG toxicity. Moreover, IVIM can screen people at risk of more severe xerostomia early.
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Affiliation(s)
- Mingjun Shen
- Department of Radiation Oncology, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, 530021, Guangxi, China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, 530021, Guangxi, China; Guangxi Medical University, Nanning, 530021, Guangxi, China; Department of Radiation Oncology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China
| | - Xiangying Lin
- Department of Radiation Oncology, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China; Department of Radiation Oncology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, 570311,Hainan, China
| | - Chaolin Yang
- Department of Radiation Oncology, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, 530021, Guangxi, China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, 530021, Guangxi, China; Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Ziyan Zhou
- Department of Radiation Oncology, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, 530021, Guangxi, China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, 530021, Guangxi, China; Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Sixia Chen
- Department of Radiation Oncology, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, 530021, Guangxi, China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, 530021, Guangxi, China; Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Yuanxiu Yin
- Department of Radiation Oncology, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, 530021, Guangxi, China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, 530021, Guangxi, China; Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Liling Long
- Guangxi Medical University, Nanning, 530021, Guangxi, China; Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Lixuan Huang
- Guangxi Medical University, Nanning, 530021, Guangxi, China; Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Zongxiang Yang
- Guangxi Medical University, Nanning, 530021, Guangxi, China; Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Rensheng Wang
- Department of Radiation Oncology, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, 530021, Guangxi, China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, 530021, Guangxi, China; Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Min Kang
- Department of Radiation Oncology, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, 530021, Guangxi, China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, 530021, Guangxi, China; Guangxi Medical University, Nanning, 530021, Guangxi, China.
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van Houdt PJ, Ragunathan S, Berks M, Ahmed Z, Kershaw LE, Gurney-Champion OJ, Tadimalla S, Arvidsson J, Sun Y, Kallehauge J, Dickie B, Lévy S, Bell L, Sourbron S, Thrippleton MJ. Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91:1774-1786. [PMID: 37667526 DOI: 10.1002/mrm.29826] [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: 03/24/2023] [Revised: 06/30/2023] [Accepted: 07/25/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. METHODS A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. RESULTS The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. CONCLUSIONS The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Michael Berks
- Quantitative Biomedical Imaging Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Zaki Ahmed
- Corewell Health William Beaumont University Hospital, Diagnostic Radiology, Royal Oak, USA
| | - Lucy E Kershaw
- Edinburgh Imaging and Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Oliver J Gurney-Champion
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Sirisha Tadimalla
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jonathan Arvidsson
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Yu Sun
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jesper Kallehauge
- Aarhus University Hospital, Danish Centre for Particle Therapy, Aarhus, Denmark
- Aarhus University, Department of Clinical Medicine, Aarhus, Denmark
| | - Ben Dickie
- Division of Informatics, Imaging, and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance NHS Group, The University of Manchester, Manchester, UK
| | - Simon Lévy
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Laura Bell
- Genentech, Inc, Clinical Imaging Group, South San Francisco, USA
| | - Steven Sourbron
- University of Sheffield, Department of Infection, Immunity and Cardiovascular Disease, Sheffield, UK
| | - Michael J Thrippleton
- University of Edinburgh, Edinburgh Imaging and Centre for Clinical Brain Sciences, Edinburgh, UK
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Mulyadi R, Putri PP, Handoko, Zairinal RA, Prihartono J. Dynamic contrast-enhanced magnetic resonance imaging parameter changes as an early biomarker of tumor responses following radiation therapy in patients with spinal metastases: a systematic review. Radiat Oncol J 2023; 41:225-236. [PMID: 38185927 PMCID: PMC10772591 DOI: 10.3857/roj.2023.00290] [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: 04/12/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 01/09/2024] Open
Abstract
PURPOSE This systematic review aims to assess and summarize the clinical values of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameter changes as early biomarkers of tumor responses following radiation therapy (RT) in patients with spinal metastases. MATERIALS AND METHODS A systematic search was conducted on five electronic databases: PubMed, Scopus, Science Direct, Cochrane, and Embase. Studies were included if they mentioned DCE-MRI parameter changes before and after RT in patients with spinal metastases with a correlation to tumor responses based on clinical and imaging criteria. The Quality Assessment of Diagnostic Accuracy Studies 2 was used to assess study quality. RESULTS This systematic review included seven studies involving 107 patients. All seven studies evaluated the transfer constant (Ktrans), six studies evaluated the plasma volume fraction (Vp), three studies evaluated the extravascular extracellular space volume fraction, and two studies evaluated the rate constant. There were variations in the type of primary cancer, RT techniques used, post-treatment scan time, and median follow-up time. Despite the variations, however, the collected evidence generally suggested that significant differences could be detected in DCE-MRI parameters between before and after RT, which might reflect treatment success or failures in long-term follow-up. Responders showed higher reduction and lower values of Ktrans and Vp after RT. DCE-MRI parameters showed changes and detectable recurrences significantly earlier (up to 6 months) than conventional MRI with favorable diagnostic values. CONCLUSION The results of this systematic review suggested that DCE-MRI parameter changes in patients with spinal metastases could be a promising tool for treatment-response assessment following RT. Lower values and higher reduction of Ktrans and Vp after treatment demonstrated good prediction of local control. Compared to conventional MRI, DCE-MRI showed more rapid changes and earlier prediction of treatment failure.
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Affiliation(s)
- Rahmad Mulyadi
- Department of Radiology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Pungky Permata Putri
- Department of Radiology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Handoko
- Department of Radiation Oncology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | | | - Joedo Prihartono
- Department of Community Medicine Pre Clinic, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
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Gibbs WN, Basha MM, Chazen JL. Management Algorithm for Osseous Metastatic Disease: What the Treatment Teams Want to Know. Neuroimaging Clin N Am 2023; 33:487-497. [PMID: 37356864 DOI: 10.1016/j.nic.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
Radiologists play a primary role in identifying, characterizing, and classifying spinal metastases and can play a lifesaving role in the care of these patients by triaging those with instability to urgent spine surgery consultation. For this reason, an understanding of current treatment algorithms and principles of spinal stability in patients with cancer is vital for all who interpret spine studies. In addition, advances in imaging allow radiologists to provide more accurate diagnoses and characterize pathology, thereby improving patient safety.
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Affiliation(s)
- Wende N Gibbs
- Barrow Neurological Institute, Department of Neuroradiology, St. Joseph's Hospital and Medical Center, 350 West Thomas Road, Phoenix, AZ 85013, USA.
| | - Mahmud Mossa Basha
- University of Washington School of Medicine, 1959 Northeast Pacific Street, Seattle, WA 98195, USA
| | - J Levi Chazen
- Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
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Zhang L, Yin FF, Lu K, Moore B, Han S, Cai J. Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric MRI fusion. PRECISION RADIATION ONCOLOGY 2022; 6:190-198. [PMID: 36590077 PMCID: PMC9797133 DOI: 10.1002/pro6.1167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/23/2022] [Indexed: 01/05/2023] Open
Abstract
Purpose Multiparametric MRI contains rich and complementary anatomical and functional information, which is often utilized separately. This study aims to propose an adaptive multiparametric MRI (mpMRI) fusion method and examine its capability in improving tumor contrast and synthesizing novel tissue contrasts among liver cancer patients. Methods An adaptive mpMRI fusion method was developed with five components: image pre-processing, fusion algorithm, database, adaptation rules, and fused MRI. Linear-weighted summation algorithm was used for fusion. Weight-driven and feature-driven adaptations were designed for different applications. A clinical-friendly graphic-user-interface (GUI) was developed in Matlab and used for mpMRI fusion. Twelve liver cancer patients and a digital human phantom were included in the study. Synthesis of novel image contrast and enhancement of image signal and contrast were examined in patient cases. Tumor contrast-to-noise ratio (CNR) and liver signal-to-noise ratio (SNR) were evaluated and compared before and after mpMRI fusion. Results The fusion platform was applicable in both XCAT phantom and patient cases. Novel image contrasts, including enhancement of soft-tissue boundary, vertebral body, tumor, and composition of multiple image features in a single image were achieved. Tumor CNR improved from -1.70 ± 2.57 to 4.88 ± 2.28 (p < 0.0001) for T1-w, from 3.39 ± 1.89 to 7.87 ± 3.47 (p < 0.01) for T2-w, and from 1.42 ± 1.66 to 7.69 ± 3.54 (p < 0.001) for T2/T1-w MRI. Liver SNR improved from 2.92 ± 2.39 to 9.96 ± 8.60 (p < 0.05) for DWI. The coefficient of variation (CV) of tumor CNR lowered from 1.57, 0.56, and 1.17 to 0.47, 0.44, and 0.46 for T1-w, T2-w and T2/T1-w MRI, respectively. Conclusion A multiparametric MRI fusion method was proposed and a prototype was developed. The method showed potential in improving clinically relevant features such as tumor contrast and liver signal. Synthesis of novel image contrasts including the composition of multiple image features into single image set was achieved.
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Affiliation(s)
- Lei Zhang
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, 215316 China
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, 215316 China
| | - Ke Lu
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
| | - Brittany Moore
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
| | - Silu Han
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
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de Ridder M, Raaijmakers CPJ, Pameijer FA, de Bree R, Reinders FCJ, Doornaert PAH, Terhaard CHJ, Philippens MEP. Target Definition in MR-Guided Adaptive Radiotherapy for Head and Neck Cancer. Cancers (Basel) 2022; 14:3027. [PMID: 35740691 PMCID: PMC9220977 DOI: 10.3390/cancers14123027] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 02/01/2023] Open
Abstract
In recent years, MRI-guided radiotherapy (MRgRT) has taken an increasingly important position in image-guided radiotherapy (IGRT). Magnetic resonance imaging (MRI) offers superior soft tissue contrast in anatomical imaging compared to computed tomography (CT), but also provides functional and dynamic information with selected sequences. Due to these benefits, in current clinical practice, MRI is already used for target delineation and response assessment in patients with head and neck squamous cell carcinoma (HNSCC). Because of the close proximity of target areas and radiosensitive organs at risk (OARs) during HNSCC treatment, MRgRT could provide a more accurate treatment in which OARs receive less radiation dose. With the introduction of several new radiotherapy techniques (i.e., adaptive MRgRT, proton therapy, adaptive cone beam computed tomography (CBCT) RT, (daily) adaptive radiotherapy ensures radiation dose is accurately delivered to the target areas. With the integration of a daily adaptive workflow, interfraction changes have become visible, which allows regular and fast adaptation of target areas. In proton therapy, adaptation is even more important in order to obtain high quality dosimetry, due to its susceptibility for density differences in relation to the range uncertainty of the protons. The question is which adaptations during radiotherapy treatment are oncology safe and at the same time provide better sparing of OARs. For an optimal use of all these new tools there is an urgent need for an update of the target definitions in case of adaptive treatment for HNSCC. This review will provide current state of evidence regarding adaptive target definition using MR during radiotherapy for HNSCC. Additionally, future perspectives for adaptive MR-guided radiotherapy will be discussed.
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Affiliation(s)
- Mischa de Ridder
- Department of Radiotherapy, University Medical Center Utrecht, 3584 Utrecht, The Netherlands; (C.P.J.R.); (F.C.J.R.); (P.A.H.D.); (C.H.J.T.); (M.E.P.P.)
| | - Cornelis P. J. Raaijmakers
- Department of Radiotherapy, University Medical Center Utrecht, 3584 Utrecht, The Netherlands; (C.P.J.R.); (F.C.J.R.); (P.A.H.D.); (C.H.J.T.); (M.E.P.P.)
| | - Frank A. Pameijer
- Department of Radiology, University Medical Center Utrecht, 3584 Utrecht, The Netherlands;
| | - Remco de Bree
- Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, 3584 Utrecht, The Netherlands;
| | - Floris C. J. Reinders
- Department of Radiotherapy, University Medical Center Utrecht, 3584 Utrecht, The Netherlands; (C.P.J.R.); (F.C.J.R.); (P.A.H.D.); (C.H.J.T.); (M.E.P.P.)
| | - Patricia A. H. Doornaert
- Department of Radiotherapy, University Medical Center Utrecht, 3584 Utrecht, The Netherlands; (C.P.J.R.); (F.C.J.R.); (P.A.H.D.); (C.H.J.T.); (M.E.P.P.)
| | - Chris H. J. Terhaard
- Department of Radiotherapy, University Medical Center Utrecht, 3584 Utrecht, The Netherlands; (C.P.J.R.); (F.C.J.R.); (P.A.H.D.); (C.H.J.T.); (M.E.P.P.)
| | - Marielle E. P. Philippens
- Department of Radiotherapy, University Medical Center Utrecht, 3584 Utrecht, The Netherlands; (C.P.J.R.); (F.C.J.R.); (P.A.H.D.); (C.H.J.T.); (M.E.P.P.)
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Kooreman ES, van Pelt V, Nowee ME, Pos F, van der Heide UA, van Houdt PJ. Longitudinal Correlations Between Intravoxel Incoherent Motion (IVIM) and Dynamic Contrast-Enhanced (DCE) MRI During Radiotherapy in Prostate Cancer Patients. Front Oncol 2022; 12:897130. [PMID: 35747819 PMCID: PMC9210504 DOI: 10.3389/fonc.2022.897130] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose Intravoxel incoherent motion (IVIM) is a promising technique that can acquire perfusion information without the use of contrast agent, contrary to the more established dynamic contrast-enhanced (DCE) technique. This is of interest for treatment response monitoring, where patients can be imaged on each treatment fraction. In this study, longitudinal correlations between IVIM- and DCE parameters were assessed in prostate cancer patients receiving radiation treatment. Materials and Methods 20 prostate cancer patients were treated on a 1.5 T MR-linac with 20 x 3 or 3.1 Gy. Weekly IVIM and DCE scans were acquired. Tumors, the peripheral zone (PZ), and the transition zone (TZ) were delineated on a T2-weighted scan acquired on the first fraction. IVIM and DCE scans were registered to this scan and the delineations were propagated. Median values from these delineations were used for further analysis. The IVIM parameters D, f, D* and the product fD* were calculated. The Tofts model was used to calculate the DCE parameters Ktrans, kep and ve. Pearson correlations were calculated for the IVIM and DCE parameters on values from the first fraction for each region of interest (ROI). For longitudinal analysis, the repeated measures correlation coefficient was used to determine correlations between IVIM and DCE parameters in each ROI. Results When averaging over patients, an increase during treatment in all IVIM and DCE parameters was observed in all ROIs, except for D in the PZ and TZ. No significant Pearson correlations were found between any pair of IVIM and DCE parameters measured on the first fraction. Significant but low longitudinal correlations were found for some combinations of IVIM and DCE parameters in the PZ and TZ, while no significant longitudinal correlations were found in the tumor. Notably in the TZ, for both f and fD*, significant longitudinal correlations with all DCE parameters were found. Conclusions The increase in IVIM- and DCE parameters when averaging over patients indicates a measurable response to radiation treatment with both techniques. Although low, significant longitudinal correlations were found which suggests that IVIM could potentially be used as an alternative to DCE for treatment response monitoring.
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Kruis MF. Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT. J Appl Clin Med Phys 2021; 23:e13468. [PMID: 34743405 PMCID: PMC8803285 DOI: 10.1002/acm2.13468] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/13/2021] [Accepted: 10/19/2021] [Indexed: 12/11/2022] Open
Abstract
Over the past decade, spectral or dual‐energy CT has gained relevancy, especially in oncological radiology. Nonetheless, its use in the radiotherapy (RT) clinic remains limited. This review article aims to give an overview of the current state of spectral CT and to explore opportunities for applications in RT. In this article, three groups of benefits of spectral CT over conventional CT in RT are recognized. Firstly, spectral CT provides more information of physical properties of the body, which can improve dose calculation. Furthermore, it improves the visibility of tumors, for a wide variety of malignancies as well as organs‐at‐risk OARs, which could reduce treatment uncertainty. And finally, spectral CT provides quantitative physiological information, which can be used to personalize and quantify treatment.
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Abstract
PURPOSE OF REVIEW This review aims to cover current MRI techniques for assessing treatment response in brain tumors, with a focus on radio-induced lesions. RECENT FINDINGS Pseudoprogression and radionecrosis are common radiological entities after brain tumor irradiation and are difficult to distinguish from real progression, with major consequences on daily patient care. To date, shortcomings of conventional MRI have been largely recognized but morphological sequences are still used in official response assessment criteria. Several complementary advanced techniques have been proposed but none of them have been validated, hampering their clinical use. Among advanced MRI, brain perfusion measures increase diagnostic accuracy, especially when added with spectroscopy and susceptibility-weighted imaging. However, lack of reproducibility, because of several hard-to-control variables, is still a major limitation for their standardization in routine protocols. Amide Proton Transfer is an emerging molecular imaging technique that promises to offer new metrics by indirectly quantifying intracellular mobile proteins and peptide concentration. Preliminary studies suggest that this noncontrast sequence may add key biomarkers in tumor evaluation, especially in posttherapeutic settings. SUMMARY Benefits and pitfalls of conventional and advanced imaging on posttreatment assessment are discussed and the potential added value of APT in this clinicoradiological evolving scenario is introduced.
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Affiliation(s)
- Lucia Nichelli
- Department of Neuroradiology, Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix
- Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, boulevard de l’Hôpital, Paris
| | - Stefano Casagranda
- Department of Research & Innovation, Olea Medical, avenue des Sorbiers, La Ciotat, France
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Li M, Zhang Q, Yang K. Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment. Front Oncol 2021; 11:645177. [PMID: 34513659 PMCID: PMC8429950 DOI: 10.3389/fonc.2021.645177] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 07/30/2021] [Indexed: 02/05/2023] Open
Abstract
Advances in radiation technology, such as intensity-modulated radiation therapy (IMRT), have largely enabled a biological dose escalation of the target volume (TV) and reduce the dose to adjacent tissues or organs at risk (OARs). However, the risk of radiation-induced injury increases as more radiation dose utilized during radiation therapy (RT), which predominantly limits further increases in TV dose distribution and reduces the local control rate. Thus, the accurate target delineation is crucial. Recently, technological improvements for precise target delineation have obtained more attention in the field of RT. The addition of functional imaging to RT can provide a more accurate anatomy of the tumor and normal tissues (such as location and size), along with biological information that aids to optimize the therapeutic index (TI) of RT. In this review, we discuss the application of some common MRI-based functional imaging techniques in clinical practice. In addition, we summarize the main challenges and prospects of these imaging technologies, expecting more inspiring developments and more productive research paths in the near future.
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Affiliation(s)
- Mei Li
- Department of Gynecology and Obstetrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Qin Zhang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Kaixuan Yang
- Department of Gynecology and Obstetrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
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11
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Kooreman ES, van Houdt PJ, Keesman R, van Pelt VWJ, Nowee ME, Pos F, Sikorska K, Wetscherek A, Müller AC, Thorwarth D, Tree AC, van der Heide UA. Daily Intravoxel Incoherent Motion (IVIM) In Prostate Cancer Patients During MR-Guided Radiotherapy-A Multicenter Study. Front Oncol 2021; 11:705964. [PMID: 34485138 PMCID: PMC8415108 DOI: 10.3389/fonc.2021.705964] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/16/2021] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Daily quantitative MR imaging during radiotherapy of cancer patients has become feasible with MRI systems integrated with linear accelerators (MR-linacs). Quantitative images could be used for treatment response monitoring. With intravoxel incoherent motion (IVIM) MRI, it is possible to acquire perfusion information without the use of contrast agents. In this multicenter study, daily IVIM measurements were performed in prostate cancer patients to identify changes that potentially reflect response to treatment. MATERIALS AND METHODS Forty-three patients were included, treated with 20 fractions of 3 Gy on a 1.5 T MR-linac. IVIM measurements were performed on each treatment day. The diffusion coefficient (D), perfusion fraction (f), and pseudo-diffusion coefficient (D*) were calculated based on the median signal intensities in the non-cancerous prostate and the tumor. Repeatability coefficients (RCs) were determined based on the first two treatment fractions. Separate linear mixed-effects models were constructed for the three IVIM parameters. RESULTS In total, 726 fractions were analyzed. Pre-treatment average values, measured on the first fraction before irradiation, were 1.46 × 10-3 mm2/s, 0.086, and 28.7 × 10-3 mm2/s in the non-cancerous prostate and 1.19 × 10-3 mm2/s, 0.088, and 28.9 × 10-3 mm2/s in the tumor, for D, f, and D*, respectively. The repeatability coefficients for D, f, and D* in the non-cancerous prostate were 0.09 × 10-3 mm2/s, 0.05, and 15.3 × 10-3 mm2/s. In the tumor, these values were 0.44 × 10-3 mm2/s, 0.16, and 76.4 × 10-3 mm2/s. The mixed effects analysis showed an increase in D of the tumors over the course of treatment, while remaining stable in the non-cancerous prostate. The f and D* increased in both the non-cancerous prostate and tumor. CONCLUSIONS It is feasible to perform daily IVIM measurements on an MR-linac system. Although the repeatability coefficients were high, changes in IVIM perfusion parameters were measured on a group level, indicating that IVIM has potential for measuring treatment response.
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Affiliation(s)
- Ernst S. Kooreman
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Petra J. van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Rick Keesman
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Vivian W. J. van Pelt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Marlies E. Nowee
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Floris Pos
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Karolina Sikorska
- Department of Biometrics, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Andreas Wetscherek
- Joint Department of Physics, The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, United Kingdom
| | | | - Daniela Thorwarth
- Section of Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Alison C. Tree
- Joint Department of Physics, The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, United Kingdom
| | - Uulke A. van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
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12
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Wang C, Padgett KR, Su MY, Mellon EA, Maziero D, Chang Z. Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy. Med Phys 2021; 49:2794-2819. [PMID: 34374098 DOI: 10.1002/mp.15130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 11/11/2022] Open
Abstract
Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.
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Affiliation(s)
- Chunhao Wang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA.,Department of Radiology, University of Miami, Miami, Florida, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California, USA.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Eric A Mellon
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Danilo Maziero
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Zheng Chang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
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13
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Kim MM, Sun Y, Aryal MP, Parmar HA, Piert M, Rosen B, Mayo CS, Balter JM, Schipper M, Gabel N, Briceño EM, You D, Heth J, Al-Holou W, Umemura Y, Leung D, Junck L, Wahl DR, Lawrence TS, Cao Y. A Phase 2 Study of Dose-intensified Chemoradiation Using Biologically Based Target Volume Definition in Patients With Newly Diagnosed Glioblastoma. Int J Radiat Oncol Biol Phys 2021; 110:792-803. [PMID: 33524546 PMCID: PMC8920120 DOI: 10.1016/j.ijrobp.2021.01.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/14/2021] [Accepted: 01/21/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE We hypothesized that dose-intensified chemoradiation therapy targeting adversely prognostic hypercellular (TVHCV) and hyperperfused (TVCBV) tumor volumes would improve outcomes in patients with glioblastoma. METHODS AND MATERIALS This single-arm, phase 2 trial enrolled adult patients with newly diagnosed glioblastoma. Patients with a TVHCV/TVCBV >1 cm3, identified using high b-value diffusion-weighted magnetic resonance imaging (MRI) and dynamic contrast-enhanced perfusion MRI, were treated over 30 fractions to 75 Gy to the TVHCV/TVCBV with temozolomide. The primary objective was to estimate improvement in 12-month overall survival (OS) versus historical control. Secondary objectives included evaluating the effect of 3-month TVHCV/TVCBV reduction on OS using Cox proportional-hazard regression and characterizing coverage (95% isodose line) of metabolic tumor volumes identified using correlative 11C-methionine positron emission tomography. Clinically meaningful change was assessed for quality of life by the European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire C30, for symptom burden by the MD Anderson Symptom Inventory for brain tumor, and for neurocognitive function (NCF) by the Controlled Oral Word Association Test, the Trail Making Test, parts A and B, and the Hopkins Verbal Learning Test-Revised. RESULTS Between 2016 and 2018, 26 patients were enrolled. Initial patients were boosted to TVHCV alone, and 13 patients were boosted to both TVHCV/TVCBV. Gross or subtotal resection was performed in 87% of patients; 22% were O6-methylguanine-DNA methyltransferase (MGMT) methylated. With 26-month follow-up (95% CI, 19-not reached), the 12-month OS rate among patients boosted to the combined TVHCV/TVCBV was 92% (95% CI, 78%-100%; P = .03) and the median OS was 20 months (95% CI, 18-not reached); the median OS for the whole study cohort was 20 months (95% CI, 14-29 months). Patients whose 3-month TVHCV/TVCBV decreased to less than the median volume (3 cm3) had superior OS (29 vs 12 months; P = .02). Only 5 patients had central or in-field failures, and 93% (interquartile range, 59%-100%) of the 11C-methionine metabolic tumor volumes received high-dose coverage. Late grade 3 neurologic toxicity occurred in 2 patients. Among non-progressing patients, 1-month and 7-month deterioration in quality of life, symptoms, and NCF were similar in incidence to standard therapy. CONCLUSIONS Dose intensification against hypercellular/hyperperfused tumor regions in glioblastoma yields promising OS with favorable outcomes for NCF, symptom burden, and quality of life, particularly among patients with greater tumor reduction 3 months after radiation therapy.
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Affiliation(s)
- Michelle M Kim
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
| | - Yilun Sun
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Madhava P Aryal
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Hemant A Parmar
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Morand Piert
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Benjamin Rosen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Charles S Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - James M Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Matthew Schipper
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Nicolette Gabel
- Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, Michigan
| | - Emily M Briceño
- Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, Michigan
| | - Daekeun You
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Jason Heth
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan
| | - Wajd Al-Holou
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan
| | - Yoshie Umemura
- Department of Neurology, University of Michigan, Ann Arbor, Michigan
| | - Denise Leung
- Department of Neurology, University of Michigan, Ann Arbor, Michigan
| | - Larry Junck
- Department of Neurology, University of Michigan, Ann Arbor, Michigan
| | - Daniel R Wahl
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Radiology, University of Michigan, Ann Arbor, Michigan; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
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14
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Bliesener Y, Lebel RM, Acharya J, Frayne R, Nayak KS. Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma. Radiology 2021; 300:410-420. [PMID: 34100683 PMCID: PMC8328086 DOI: 10.1148/radiol.2021203628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Background Advances in sub-Nyquist–sampled dynamic contrast-enhanced (DCE) MRI enable monitoring of brain tumors with millimeter resolution and whole-brain coverage. Such undersampled quantitative methods need careful characterization regarding achievable test-retest reproducibility. Purpose To demonstrate a fully automated high-resolution whole-brain DCE MRI pipeline with 30-fold sparse undersampling and estimate its reproducibility on the basis of reference regions of stable tissue types during multiple posttreatment time points by using longitudinal clinical images of high-grade glioma. Materials and Methods Two methods for sub-Nyquist–sampled DCE MRI were extended with automatic estimation of vascular input functions. Continuously acquired three-dimensional k-space data with ramped-up flip angles were partitioned to yield high-resolution, whole-brain tracer kinetic parameter maps with matched precontrast-agent T1 and M0 maps. Reproducibility was estimated in a retrospective study in participants with high-grade glioma, who underwent three consecutive standard-of-care examinations between December 2016 and April 2019. Coefficients of variation and reproducibility coefficients were reported for histogram statistics of the tracer kinetic parameters plasma volume fraction and volume transfer constant (Ktrans) on five healthy tissue types. Results The images from 13 participants (mean age ± standard deviation, 61 years ± 10; nine women) with high-grade glioma were evaluated. In healthy tissues, the protocol achieved a coefficient of variation less than 57% for median Ktrans, if Ktrans was estimated consecutively. The maximum reproducibility coefficient for median Ktrans was estimated to be at 0.06 min–1 for large or low-enhancing tissues and to be as high as 0.48 min–1 in smaller or strongly enhancing tissues. Conclusion A fully automated, sparsely sampled DCE MRI reconstruction with patient-specific vascular input function offered high spatial and temporal resolution and whole-brain coverage; in healthy tissues, the protocol estimated median volume transfer constant with maximum reproducibility coefficient of 0.06 min–1 in large, low-enhancing tissue regions and maximum reproducibility coefficient of less than 0.48 min–1 in smaller or more strongly enhancing tissue regions. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Lenkinski in this issue.
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Affiliation(s)
- Yannick Bliesener
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - R Marc Lebel
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - Jay Acharya
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - Richard Frayne
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - Krishna S Nayak
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
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15
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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: 4.0] [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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16
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van Houdt PJ, Yang Y, van der Heide UA. Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy. Front Oncol 2021; 10:615643. [PMID: 33585242 PMCID: PMC7878523 DOI: 10.3389/fonc.2020.615643] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 12/08/2020] [Indexed: 12/20/2022] Open
Abstract
MRI-guided radiotherapy systems have the potential to bring two important concepts in modern radiotherapy together: adaptive radiotherapy and biological targeting. Based on frequent anatomical and functional imaging, monitoring the changes that occur in volume, shape as well as biological characteristics, a treatment plan can be updated regularly to accommodate the observed treatment response. For this purpose, quantitative imaging biomarkers need to be identified that show changes early during treatment and predict treatment outcome. This review provides an overview of the current evidence on quantitative MRI measurements during radiotherapy and their potential as an imaging biomarker on MRI-guided radiotherapy systems.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Yingli Yang
- Department of Radiation Oncology, University of California, Los Angeles, CA, United States
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
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17
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Otazo R, Lambin P, Pignol JP, Ladd ME, Schlemmer HP, Baumann M, Hricak H. MRI-guided Radiation Therapy: An Emerging Paradigm in Adaptive Radiation Oncology. Radiology 2020; 298:248-260. [PMID: 33350894 DOI: 10.1148/radiol.2020202747] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Radiation therapy (RT) continues to be one of the mainstays of cancer treatment. Considerable efforts have been recently devoted to integrating MRI into clinical RT planning and monitoring. This integration, known as MRI-guided RT, has been motivated by the superior soft-tissue contrast, organ motion visualization, and ability to monitor tumor and tissue physiologic changes provided by MRI compared with CT. Offline MRI is already used for treatment planning at many institutions. Furthermore, MRI-guided linear accelerator systems, allowing use of MRI during treatment, enable improved adaptation to anatomic changes between RT fractions compared with CT guidance. Efforts are underway to develop real-time MRI-guided intrafraction adaptive RT of tumors affected by motion and MRI-derived biomarkers to monitor treatment response and potentially adapt treatment to physiologic changes. These developments in MRI guidance provide the basis for a paradigm change in treatment planning, monitoring, and adaptation. Key challenges to advancing MRI-guided RT include real-time volumetric anatomic imaging, addressing image distortion because of magnetic field inhomogeneities, reproducible quantitative imaging across different MRI systems, and biologic validation of quantitative imaging. This review describes emerging innovations in offline and online MRI-guided RT, exciting opportunities they offer for advancing research and clinical care, hurdles to be overcome, and the need for multidisciplinary collaboration.
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Affiliation(s)
- Ricardo Otazo
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Philippe Lambin
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Jean-Philippe Pignol
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Mark E Ladd
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Heinz-Peter Schlemmer
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Michael Baumann
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Hedvig Hricak
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
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de la Pinta C, Barrios-Campo N, Sevillano D. Radiomics in lung cancer for oncologists. J Clin Transl Res 2020; 6:127-134. [PMID: 33521373 PMCID: PMC7837741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/12/2020] [Accepted: 06/08/2020] [Indexed: 11/26/2022] Open
Abstract
UNLABELLED Radiomics has revolutionized the world of medical imaging. The aim of this review is to guide oncologists in radiomics and its applications in diagnosis, prediction of response and damage, prediction of survival, and prognosis in lung cancer. In this review, we analyzed published literature on PubMed and MEDLINE with papers published in the last 10 years. We included papers in English language with information about radiomics features and diagnostic, predictive, and prognosis of radiomics in lung cancer. All citations were evaluated for relevant content and validation. RELEVANCE FOR PATIENTS The evolution of technology allows the development of computer algorithms that facilitate the diagnosis and evaluation of response after different oncological treatments and their non-invasive follow-up.
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Affiliation(s)
- Carolina de la Pinta
- 1Department of Radiation Oncology, Ramón y Cajal Hospital, Madrid, Spain,
Corresponding author: Carolina de la Pinta Department of Radiation Oncology, Ramón y Cajal Hospital, Madrid, Spain
| | - Nuria Barrios-Campo
- 2Department of Biomedical Engineering, Madrid Polytechnic University, Madrid, Spain
| | - David Sevillano
- 3Department of Medical Physics, Ramón y Cajal Hospital, Madrid, Spain
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Bliesener Y, Acharya J, Nayak KS. Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1712-1723. [PMID: 31794389 PMCID: PMC8887912 DOI: 10.1109/tmi.2019.2953901] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease. These maps suffer from many known sources of variability. This variability is expensive to compute using current methods, and is typically not reported. Here, we demonstrate a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time. We train and use a neural network to estimate the approximate joint posterior distribution of tracer-kinetic parameters. Uncertainties are estimated for each voxel and are specific to the patient, exam, and lesion. We demonstrate the methods' ability to produce accurate tracer-kinetic maps. We compare predicted parameter ranges with uncertainties introduced by noise and by differences in post-processing in a digital reference object. The predicted parameter ranges correlate well with tracer-kinetic parameter ranges observed across different noise realizations and regression algorithms. We also demonstrate the value of this approach to differentiate significant from insignificant changes in brain tumor pharmacokinetics over time. This is achieved by enforcing consistency in resolving model singularities in the applied tracer-kinetic model.
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Gurney-Champion OJ, Mahmood F, van Schie M, Julian R, George B, Philippens MEP, van der Heide UA, Thorwarth D, Redalen KR. Quantitative imaging for radiotherapy purposes. Radiother Oncol 2020; 146:66-75. [PMID: 32114268 PMCID: PMC7294225 DOI: 10.1016/j.radonc.2020.01.026] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 01/22/2020] [Accepted: 01/29/2020] [Indexed: 02/07/2023]
Abstract
Quantitative imaging biomarkers show great potential for use in radiotherapy. Quantitative images based on microscopic tissue properties and tissue function can be used to improve contouring of the radiotherapy targets. Furthermore, quantitative imaging biomarkers might be used to predict treatment response for several treatment regimens and hence be used as a tool for treatment stratification, either to determine which treatment modality is most promising or to determine patient-specific radiation dose. Finally, patient-specific radiation doses can be further tailored to a tissue/voxel specific radiation dose when quantitative imaging is used for dose painting. In this review, published standards, guidelines and recommendations on quantitative imaging assessment using CT, PET and MRI are discussed. Furthermore, critical issues regarding the use of quantitative imaging for radiation oncology purposes and resultant pending research topics are identified.
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Affiliation(s)
- Oliver J Gurney-Champion
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Faisal Mahmood
- Department of Oncology, Odense University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Marcel van Schie
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Robert Julian
- Department of Radiotherapy Physics, Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
| | - Ben George
- Radiation Therapy Medical Physics Group, CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, United Kingdom
| | | | - Uulke A van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, Eberhard Karls University of Tübingen, Germany
| | - Kathrine R Redalen
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
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21
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Mouawad M, Biernaski H, Brackstone M, Lock M, Yaremko B, Shmuilovich O, Kornecki A, Ben Nachum I, Muscedere G, Lynn K, Prato FS, Thompson RT, Gaede S, Gelman N. DCE-MRI assessment of response to neoadjuvant SABR in early stage breast cancer: Comparisons of single versus three fraction schemes and two different imaging time delays post-SABR. Clin Transl Radiat Oncol 2020; 21:25-31. [PMID: 32021911 PMCID: PMC6993055 DOI: 10.1016/j.ctro.2019.12.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 12/22/2019] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To determine the effect of dose fractionation and time delay post-neoadjuvant stereotactic ablative radiotherapy (SABR) on dynamic contrast-enhanced (DCE)-MRI parameters in early stage breast cancer patients. MATERIALS AND METHODS DCE-MRI was acquired in 17 patients pre- and post-SABR. Five patients were imaged 6-7 days post-21 Gy/1fraction (group 1), six 16-19 days post-21 Gy/1fraction (group 2), and six 16-18 days post-30 Gy/3 fractions every other day (group 3). DCE-MRI scans were performed using half the clinical dose of contrast agent. Changes in the surrounding tissue were quantified using a signal-enhancement threshold metric that characterizes changes in signal-enhancement volume (SEV). Tumour response was quantified using Ktrans and ve (Tofts model) pre- and post-SABR. Significance was assessed using a Wilcoxin signed-rank test. RESULTS All group 1 and 4/6 group 2 patients' SEV increased post-SABR. All group 3 patients' SEV decreased. The mean Ktrans increased for group 1 by 76% (p = 0.043) while group 2 and 3 decreased 15% (p = 0.028) and 34% (p = 0.028), respectively. For ve, there was no significant change in Group 1 (p = 0.35). Groups 2 showed an increase of 24% (p = 0.043), and Group 3 trended toward an increase (23%, p = 0.08). CONCLUSION Kinetic parameters measured 2.5 weeks post-SABR in both single fraction and three fraction groups were indicative of response but only the single fraction protocol led to enhancement in the surrounding tissue. Our results also suggest that DCE-MRI one-week post-SABR may be too early for response assessment, at least for single fraction SABR, whereas 2.5 weeks appears sufficiently long to minimize confounding acute effects.
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Affiliation(s)
- Matthew Mouawad
- Medical Biophysics, Western University, London, Ontario, Canada
| | | | - Muriel Brackstone
- Medical Biophysics, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
- London Health Sciences Centre, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
| | - Michael Lock
- London Health Sciences Centre, London, Ontario, Canada
- Department of Oncology, Western University, London, Ontario, Canada
| | - Brian Yaremko
- London Health Sciences Centre, London, Ontario, Canada
- Department of Oncology, Western University, London, Ontario, Canada
| | - Olga Shmuilovich
- Lawson Health Research Institute, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
| | - Anat Kornecki
- Lawson Health Research Institute, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
| | - Ilanit Ben Nachum
- Lawson Health Research Institute, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
| | - Giulio Muscedere
- Lawson Health Research Institute, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
| | - Kalan Lynn
- Lawson Health Research Institute, London, Ontario, Canada
- London Health Sciences Centre, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
| | - Frank S. Prato
- Medical Biophysics, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
| | - R. Terry Thompson
- Medical Biophysics, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Stewart Gaede
- Medical Biophysics, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
- London Health Sciences Centre, London, Ontario, Canada
- Department of Oncology, Western University, London, Ontario, Canada
| | - Neil Gelman
- Medical Biophysics, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
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Hormuth DA, Jarrett AM, Yankeelov TE. Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling. Radiat Oncol 2020; 15:4. [PMID: 31898514 PMCID: PMC6941255 DOI: 10.1186/s13014-019-1446-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 12/12/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Intra-and inter-tumoral heterogeneity in growth dynamics and vascularity influence tumor response to radiation therapy. Quantitative imaging techniques capture these dynamics non-invasively, and these data can initialize and constrain predictive models of response on an individual basis. METHODS We have developed a family of 10 biologically-based mathematical models describing the spatiotemporal dynamics of tumor volume fraction, blood volume fraction, and response to radiation therapy. To evaluate this family of models, rats (n = 13) with C6 gliomas were imaged with magnetic resonance imaging (MRI) three times before, and four times following a single fraction of 20 Gy or 40 Gy whole brain irradiation. The first five 3D time series data of tumor volume fraction, estimated from diffusion-weighted (DW-) MRI, and blood volume fraction, estimated from dynamic contrast-enhanced (DCE-) MRI, were used to calibrate tumor-specific model parameters. The most parsimonious and well calibrated of the 10 models, selected using the Akaike information criterion, was then utilized to predict future growth and response at the final two imaging time points. Model predictions were compared at the global level (percent error in tumor volume, and Dice coefficient) as well as at the local or voxel level (concordance correlation coefficient). RESULT The selected model resulted in < 12% error in tumor volume predictions, strong spatial agreement between predicted and observed tumor volumes (Dice coefficient > 0.74), and high level of agreement at the voxel level between the predicted and observed tumor volume fraction and blood volume fraction (concordance correlation coefficient > 0.77 and > 0.65, respectively). CONCLUSIONS This study demonstrates that serial quantitative MRI data collected before and following radiation therapy can be used to accurately predict tumor and vasculature response with a biologically-based mathematical model that is calibrated on an individual basis. To the best of our knowledge, this is the first effort to characterize the tumor and vasculature response to radiation therapy temporally and spatially using imaging-driven mathematical models.
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Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th Street, POB 4.102, 1 University Station (C0200), Austin, TX, USA.
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA.
| | - Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th Street, POB 4.102, 1 University Station (C0200), Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th Street, POB 4.102, 1 University Station (C0200), Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
- Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
- Departments of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
- Departments of Oncology, The University of Texas at Austin, Austin, TX, USA
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Lu L, Chen Y, Shen C, Lian J, Das S, Marks L, Lin W, Zhu T. Initial assessment of 3D magnetic resonance fingerprinting (MRF) towards quantitative brain imaging for radiation therapy. Med Phys 2019; 47:1199-1214. [PMID: 31834641 DOI: 10.1002/mp.13967] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 12/02/2019] [Accepted: 12/06/2019] [Indexed: 12/17/2022] Open
Abstract
PURPOSE Magnetic resonance fingerprinting (MRF) provides quantitative T1/T2 maps, enabling applications in clinical radiotherapy such as large-scale, multi-center clinical trials for longitudinal assessment of therapy response. We evaluated the feasibility of a quantitative three-dimensional-MRF (3D-MRF) towards its radiotherapy applications of primary brain tumors. METHODS A fast whole-brain 3D-MRF sequence initially developed for diagnostic radiology was optimized using flexible body coils, which is the typical MR imaging setup for radiotherapy treatment planning and for MR imaging (MRI)-guided treatment delivery. Optimization criteria included the accuracy and the precision of T1/T2 quantifications of polyvinylpyrrolidone (PVP) solutions, compared to those from the 3D-MRF using a 32-channel head coil. The accuracy of T1/T2 quantifications from the optimized MRF was first examined in healthy volunteers with two different coil setups. The intra- and inter-scanner variations of image intensity from the optimized sequence were quantified by longitudinal scans of the PVP solutions on two 3T scanners. Using a 3D-printed MRI geometry phantom, susceptibility-induced distortion with the optimized 3D-MRF was quantified as the Dice coefficient of phantom contours, compared to those from CT images. By introducing intentional head motion during 10% of the scan, the robustness of the optimized 3D-MRF towards motion was evaluated through visual inspection of motion artifacts and through quantitative analysis of image sharpness in brain MRF maps. RESULTS The optimized sequence acquired whole-brain T1, T2 and proton density maps and with a resolution of 1.2 × 1.2 × 3 mm3 in 10 min, similar to the total acquisition time of 3D T1- and T2-weighted images of the same resolution. In vivo T1 and T2 values of the white and gray matter were consistent with literature. The intra- and inter-scanner variability of the intensity-normalized MRF T1 was 1.0% ± 0.7% and 2.3% ± 1.0% respectively, in contrast to 5.3% ± 3.8% and 3.2% ± 1.6% from the normalized T1-weighted MRI. Repeatability and reproducibility of MRF T1 were independent of intensity normalization. Both phantom and human data demonstrated that the optimized 3D-MRF is more robust to subject motion and artifacts from subject-specific susceptibility difference. Compared to CT contours, the Dice coefficient of phantom contours from 3D-MRF was 0.93, improved from 0.87 from the T1-weighted MRI. CONCLUSION Compared to conventional MRI, the optimized 3D-MRF demonstrated improved repeatability across time points and reproducibility across scanners for better tissue quantification, as well as improved robustness to subject-specific susceptibility and motion artifacts under a typical MR imaging setup for radiotherapy. More importantly, quantitative MRF T1/T2 measurements lead to promising potentials towards longitudinal quantitative assessment of treatment response for better adaptive therapy and for large-scale, multi-center clinical trials.
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Affiliation(s)
- Lan Lu
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yong Chen
- Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Colette Shen
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jun Lian
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Shiva Das
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lawrence Marks
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tong Zhu
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Bliesener Y, Lingala SG, Haldar JP, Nayak KS. Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects. Magn Reson Med 2019; 83:1625-1639. [PMID: 31605556 PMCID: PMC6982604 DOI: 10.1002/mrm.28024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 09/06/2019] [Accepted: 09/09/2019] [Indexed: 12/12/2022]
Abstract
Purpose To evaluate the impact of (k,t) data sampling on the variance of tracer‐kinetic parameter (TK) estimation in high‐resolution whole‐brain dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI) using digital reference objects. We study this in the context of TK model constraints, and in the absence of other constraints. Methods Three anatomically and physiologically realistic brain‐tumor digital reference objects were generated. Data sampling strategies included uniform and variable density; zone‐based, lattice, pseudo‐random, and pseudo‐radial; with 50‐time frames and 4‐fold to 25‐fold undersampling. In all cases, we assume a fully sampled first time frame, and prior knowledge of the arterial input function. TK parameters were estimated by indirect estimation (i.e., image‐time‐series reconstruction followed by model fitting), and direct estimation from the under‐sampled data. We evaluated methods based on the Cramér‐Rao bound and Monte‐Carlo simulations, over the range of signal‐to‐noise ratio (SNR) seen in clinical brain DCE‐MRI. Results Lattice‐based sampling provided the lowest SDs, followed by pseudo‐random, pseudo‐radial, and zone‐based. This ranking was consistent for the Patlak and extended Tofts model. Pseudo‐random sampling resulted in 19% higher averaged SD compared to lattice‐based sampling. Zone‐based sampling resulted in substantially higher SD at undersampling factors above 10. CRB analysis showed only a small difference between uniform and variable density for both lattice‐based and pseudo‐random sampling up to undersampling factors of 25. Conclusion Lattice sampling provided the lowest SDs, although the differences between sampling schemes were not substantial at low undersampling factors. The differences between lattice‐based and pseudo‐random sampling strategies with both uniform and variable density were within the range of error induced by other sources, at up to 25‐fold undersampling.
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Affiliation(s)
- Yannick Bliesener
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Sajan G Lingala
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Justin P Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
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25
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Ahmed Z, Levesque IR. Pharmacokinetic modeling of dynamic contrast-enhanced MRI using a reference region and input function tail. Magn Reson Med 2019; 83:286-298. [PMID: 31393033 DOI: 10.1002/mrm.27913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 06/18/2019] [Accepted: 06/18/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE Quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) requires an arterial input function (AIF) which is difficult to measure. We propose the reference region and input function tail (RRIFT) approach which uses a reference tissue and the washout portion of the AIF. METHODS RRIFT was evaluated in simulations with 100 parameter combinations at various temporal resolutions (5-30 s) and noise levels (σ = 0.01-0.05 mM). RRIFT was compared against the extended Tofts model (ETM) in 8 studies from patients with glioblastoma multiforme. Two versions of RRIFT were evaluated: one using measured patient-specific AIF tails, and another assuming a literature-based AIF tail. RESULTS RRIFT estimated the transfer constant K trans and interstitial volume v e with median errors within 20% across all simulations. RRIFT was more accurate and precise than the ETM at temporal resolutions slower than 10 s. The percentage error of K trans had a median and interquartile range of -9 ± 45% with the ETM and -2 ± 17% with RRIFT at a temporal resolution of 30 s under noiseless conditions. RRIFT was in excellent agreement with the ETM in vivo, with concordance correlation coefficients (CCC) of 0.95 for K trans , 0.96 for v e , and 0.73 for the plasma volume v p using a measured AIF tail. With the literature-based AIF tail, the CCC was 0.89 for K trans , 0.93 for v e and 0.78 for v p . CONCLUSIONS Quantitative DCE-MRI analysis using the input function tail and a reference tissue yields absolute kinetic parameters with the RRIFT method. This approach was viable in simulation and in vivo for temporal resolutions as low as 30 s.
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Affiliation(s)
- Zaki Ahmed
- Medical Physics Unit, McGill University, Montreal, Canada.,Department of Physics, McGill University, Montreal, Canada
| | - Ives R Levesque
- Medical Physics Unit, McGill University, Montreal, Canada.,Department of Physics, McGill University, Montreal, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada.,Cancer Research Program, Research Institute of the McGill University Health Centre, Montreal, Canada
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Neuroimaging and Stereotactic Body Radiation Therapy (SBRT) for Spine Metastasis. Top Magn Reson Imaging 2019; 28:85-96. [PMID: 31022051 DOI: 10.1097/rmr.0000000000000199] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Historically, management options for spinal metastases include surgery for stabilization and decompression and/or external beam radiation therapy (EBRT). EBRT is palliative in nature, as it lacks accurate targeting such that the prescribed radiation doses must be limited in order to maintain safety. Modern advancement in imaging and radiotherapy technology have facilitated the development of stereotactic body radiation therapy (SBRT), which provides increased targeted precision for radiation delivery to tumors resulting in lower overall toxicity, particularly to regional structures such as the spinal cord and esophagus, while delivering higher, more effective, and radically ablative radiation doses.Over the past decade, SBRT has been increasingly utilized as a method of treating spinal metastases either as the primary modality or following surgical intervention in both de novo and reirradiation setting. Numerous studies suggest that SBRT is associated with an 80% to 90% rate of 1-year local control across clinical scenarios. For example, studies of SBRT as the primary treatment modality suggest long-term local control rate of 80% to 95% for spinal metastases. Similarly, SBRT in the adjuvant setting following surgery is associated with local control rates ranging from 70% to 100%. Furthermore, because SBRT allows for lower dose to the spinal cord, it has also been used in patients who have had prior radiation therapy, with studies showing 66% to 93% local control in this scenario.
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27
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DCE and DSC perfusion MRI diagnostic accuracy in the follow-up of primary and metastatic intra-axial brain tumors treated by radiosurgery with cyberknife. Radiat Oncol 2019; 14:65. [PMID: 30992043 PMCID: PMC6466652 DOI: 10.1186/s13014-019-1271-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Accepted: 04/05/2019] [Indexed: 12/14/2022] Open
Abstract
Background The differential diagnosis between radiation necrosis, tumor recurrence and tumor progression is crucial for the evaluation of treatment response and treatment planning. The appearance of treatment-induced tissue necrosis on conventional Magnetic Resonance Imaging (MRI) is similar to brain tumor recurrence and it could be difficult to differentiate the two entities on follow-up MRI examinations. Dynamic Susceptibility Contrast-enhanced (DSC) and Dynamic Contrast-Enhanced (DCE) are MRI perfusion techniques that use an exogenous, intravascular, non-diffusible gadolinium-based contrast agent. The aim of this study was to compare the diagnostic accuracy of DSC and DCE perfusion MRI in the differential diagnosis between radiation necrosis and tumor recurrence, in the follow-up of primary and metastatic intra-axial brain tumors after Stereotactic RadioSurgery (SRS) performed with CyberKnife. Methods A total of 72 enhancing lesions (57 brain metastases and 15 primary brain tumors) were analyzed with DCE and DSC, by means of MRI acquisition performed by 1,5 Tesla MR scanner. The statistical relationship between the diagnosis of tumor recurrence or radiation necrosis, decided according to clinicoradiologically criteria, rCBV and Ktrans was evaluated by the point-biserial correlation coefficient respectively. Results The statistical analysis showed a correlation between the diagnosis of radiation necrosis or recurrent tumor with Ktrans (rpb = 0.54, p < 0.001) and with rCBV (rpb = 0.37, p = 0.002). The ROC analysis of rCBV values demonstrated a good classification ability in differentiating radiation necrosis from tumour recurrence as well as the Ktrans. The optimal cut-off value for rCBV was k = 1.23 with 0.88 of sensitivity and 0.75 of specificity while for Ktrans was k = 28.76 with 0.89 of sensitivity and 0.97 of specificity. Conclusions MRI perfusion techniques, particularly DCE, help in the differential diagnosis by tumor recurrence and radiation necrosis during the follow-up after radiosurgery.
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Benhaddou R, Pensky M, Rajapakshage R. Anisotropic functional Laplace deconvolution. J Stat Plan Inference 2019. [DOI: 10.1016/j.jspi.2018.07.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Olsson LE, Johansson M, Zackrisson B, Blomqvist LK. Basic concepts and applications of functional magnetic resonance imaging for radiotherapy of prostate cancer. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2019; 9:50-57. [PMID: 33458425 PMCID: PMC7807726 DOI: 10.1016/j.phro.2019.02.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 12/27/2018] [Accepted: 02/08/2019] [Indexed: 12/30/2022]
Abstract
Recently, the interest to integrate magnetic resonance imaging (MRI) in radiotherapy for prostate cancer has increased considerably. MRI can contribute in all steps of the radiotherapy workflow from diagnosis, staging, and target definition to treatment follow-up. Of particular interest is the ability of MRI to provide a wide range of functional measures. The complexity of MRI as an imaging modality combined with the growing interest of the application to prostate cancer radiotherapy, emphasize the need for dedicated education within the radiation oncology community. In this context, an overview of the most common as well as a few upcoming functional MR imaging techniques is presented: the basic methodology and measurement is described, the link between the functional measures and the underlying biology is established, and finally relevant applications of functional MRI useful for prostate cancer radiotherapy are given.
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Affiliation(s)
- Lars E Olsson
- Department of Medical Radiation Physics, Translational Medicine, Lund University, Sweden.,Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Sweden
| | | | | | - Lennart K Blomqvist
- Department of Radiology, Molecular Medicine and Surgery, Karolinska University, Sweden
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Das IJ, McGee KP, Tyagi N, Wang H. Role and future of MRI in radiation oncology. Br J Radiol 2018; 92:20180505. [PMID: 30383454 DOI: 10.1259/bjr.20180505] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Technical innovations and developments in areas such as disease localization, dose calculation algorithms, motion management and dose delivery technologies have revolutionized radiation therapy resulting in improved patient care with superior outcomes. A consequence of the ability to design and accurately deliver complex radiation fields is the need for improved target visualization through imaging. While CT imaging has been the standard of care for more than three decades, the superior soft tissue contrast afforded by MR has resulted in the adoption of this technology in radiation therapy. With the development of real time MR imaging techniques, the problem of real time motion management is enticing. Currently, the integration of an MR imaging and megavoltage radiation therapy treatment delivery system (MR-linac or MRL) is a reality that has the potential to provide improved target localization and real time motion management during treatment. Higher magnetic field strengths provide improved image quality potentially providing the backbone for future work related to image texture analysis-a field known as Radiomics-thereby providing meaningful information on the selection of future patients for radiation dose escalation, motion-managed treatment techniques and ultimately better patient care. On-going advances in MRL technologies promise improved real time soft tissue visualization, treatment margin reductions, beam optimization, inhomogeneity corrected dose calculation, fast multileaf collimators and volumetric arc radiation therapy. This review article provides rationale, advantages and disadvantages as well as ideas for future research in MRI related to radiation therapy mainly in adoption of MRL.
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Affiliation(s)
- Indra J Das
- 1 Department of Radiation Oncology, NYU Langone Medical Center , New York, NY , USA
| | - Kiaran P McGee
- 2 Department of Radiology, Mayo Clinic , Rochester, MN , USA
| | - Neelam Tyagi
- 3 Department of Medical Physics, Memorial Sloan-Kettering Cancer Center , New York, NY , USA
| | - Hesheng Wang
- 1 Department of Radiation Oncology, NYU Langone Medical Center , New York, NY , USA
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Fahlström M, Fransson S, Blomquist E, Nyholm T, Larsson EM. Dynamic contrast-enhanced magnetic resonance imaging may act as a biomarker for vascular damage in normal appearing brain tissue after radiotherapy in patients with glioblastoma. Acta Radiol Open 2018; 7:2058460118808811. [PMID: 30542625 PMCID: PMC6236579 DOI: 10.1177/2058460118808811] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2018] [Accepted: 09/25/2018] [Indexed: 11/16/2022] Open
Abstract
Background Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising perfusion method and may be useful in evaluating radiation-induced changes in normal-appearing brain tissue. Purpose To assess whether radiotherapy induces changes in vascular permeability (Ktrans) and the fractional volume of the extravascular extracellular space (Ve) derived from DCE-MRI in normal-appearing brain tissue and possible relationships to radiation dose given. Material and Methods Seventeen patients with glioblastoma treated with radiotherapy and chemotherapy were included; five were excluded because of inconsistencies in the radiotherapy protocol or early drop-out. DCE-MRI, contrast-enhanced three-dimensional (3D) T1-weighted (T1W) images and T2-weighted fluid attenuated inversion recovery (T2-FLAIR) images were acquired before and on average 3.3, 30.6, 101.6, and 185.7 days after radiotherapy. Pre-radiotherapy CE T1W and T2-FLAIR images were segmented into white and gray matter, excluding all non-healthy tissue. Ktrans and Ve were calculated using the extended Kety model with the Parker population-based arterial input function. Six radiation dose regions were created for each tissue type, based on each patient’s computed tomography-based dose plan. Mean Ktrans and Ve were calculated over each dose region and tissue type. Results Global Ktrans and Ve demonstrated mostly non-significant changes with mean values higher for post-radiotherapy examinations in both gray and white matter compared to pre-radiotherapy. No relationship to radiation dose was found. Conclusion Additional studies are needed to validate if Ktrans and Ve derived from DCE-MRI may act as potential biomarkers for acute and early-delayed radiation-induced vascular damages. No dose-response relationship was found.
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Affiliation(s)
- Markus Fahlström
- Department of Radiology, Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Samuel Fransson
- Department of Radiology, Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Erik Blomquist
- Department of Experimental and Clinical Oncology, Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Tufve Nyholm
- Department of Radiation Physics, Radiation Sciences, Umeå University, Umeå, Sweden
| | - Elna-Marie Larsson
- Department of Radiology, Surgical Sciences, Uppsala University, Uppsala, Sweden
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Brighi C, Puttick S, Rose S, Whittaker AK. The potential for remodelling the tumour vasculature in glioblastoma. Adv Drug Deliv Rev 2018; 136-137:49-61. [PMID: 30308226 DOI: 10.1016/j.addr.2018.10.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 10/04/2018] [Accepted: 10/07/2018] [Indexed: 12/19/2022]
Abstract
Despite significant improvements in the clinical management of glioblastoma, poor delivery of systemic therapies to the entire population of tumour cells remains one of the biggest challenges in the achievement of more effective treatments. On the one hand, the abnormal and dysfunctional tumour vascular network largely limits blood perfusion, resulting in an inhomogeneous delivery of drugs to the tumour. On the other hand, the presence of an intact blood-brain barrier (BBB) in certain regions of the tumour prevents chemotherapeutic drugs from permeating through the tumour vessels and reaching the diseased cells. In this review we analyse in detail the implications of the presence of a dysfunctional vascular network and the impenetrable BBB on drug transport. We discuss advantages and limitations of the currently available strategies for remodelling the tumour vasculature aiming to ameliorate the above mentioned limitations. Finally we review research methods for visualising vascular dysfunction and highlight the power of DCE- and DSC-MRI imaging to assess changes in blood perfusion and BBB permeability.
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Marzi S, Farneti A, Vidiri A, Di Giuliano F, Marucci L, Spasiano F, Terrenato I, Sanguineti G. Radiation-induced parotid changes in oropharyngeal cancer patients: the role of early functional imaging and patient-/treatment-related factors. Radiat Oncol 2018; 13:189. [PMID: 30285893 PMCID: PMC6167883 DOI: 10.1186/s13014-018-1137-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 09/24/2018] [Indexed: 02/08/2023] Open
Abstract
Background Functional magnetic resonance imaging may provide several quantitative indices strictly related to distinctive tissue signatures with radiobiological relevance, such as tissue cellular density and vascular perfusion. The role of Intravoxel Incoherent Motion Diffusion Weighted Imaging (IVIM-DWI) and Dynamic Contrast-Enhanced (DCE) MRI in detecting/predicting radiation-induced volumetric changes of parotids both during and shortly after (chemo)radiotherapy of oropharyngeal squamous cell carcinoma (SCC) was explored. Methods Patients with locally advanced oropharyngeal SCC were accrued within a prospective study offering both IVIM-DWI and DCE-MRI at baseline; IVIM-DWI was repeated at the 10th fraction of treatment. Apparent diffusion coefficient (ADC), tissue diffusion coefficient Dt, perfusion fraction f and perfusion-related diffusion coefficient D* were estimated both at baseline and during RT. Semi-quantitative and quantitative parameters, including the transfer constant Ktrans, were calculated from DCE-MRI. Parotids were contoured on T2-weighted images at baseline, 10th fraction and 8th weeks after treatment end and the percent change of parotid volume between baseline/10th fr (∆Vol10fr) and baseline/8th wk. (∆Volpost) computed. Correlations among volumetric changes and patient-, treatment- and imaging-related features were investigated at univariate analysis (Spearman’s Rho). Results Eighty parotids (40 patients) were analyzed. Percent changes were 18.2 ± 10.7% and 31.3 ± 15.8% for ∆Vol10fr and ∆Volpost, respectively. Among baseline characteristics, ∆Vol10fr was correlated to body mass index, patient weight as well as the initial parotid volume. A weak correlation was present between parotid shrinkage after the first 2 weeks of treatment and dosimetric variables, while no association was found after radiotherapy. Percent changes of both ADC and Dt at the 10th fraction were also correlated to ∆Vol10fr. Significant relationships were found between ∆Volpost and baseline DCE-MRI parameters. Conclusions Both IVIM-DWI and DCE-MRI can help to detect/predict early (during treatment) and shortly after treatment completion the parotid shrinkage. They may contribute to clarify the correlations between volumetric changes of parotid glands and patient−/treatment-related variables by assessing individual microcapillary perfusion and tissue diffusivity. Electronic supplementary material The online version of this article (10.1186/s13014-018-1137-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Simona Marzi
- Medical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144, Rome, Italy.
| | - Alessia Farneti
- Department of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144, Rome, Italy
| | - Francesca Di Giuliano
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144, Rome, Italy.,Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Viale Oxford 81, 00133, Rome, Italy
| | - Laura Marucci
- Department of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144, Rome, Italy
| | - Filomena Spasiano
- Department of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144, Rome, Italy
| | - Irene Terrenato
- Biostatistics-Scientific Direction, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144, Rome, Italy
| | - Giuseppe Sanguineti
- Department of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144, Rome, Italy
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Johansson A, Balter JM, Cao Y. Abdominal DCE-MRI reconstruction with deformable motion correction for liver perfusion quantification. Med Phys 2018; 45:4529-4540. [PMID: 30098044 DOI: 10.1002/mp.13118] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Revised: 07/29/2018] [Accepted: 07/29/2018] [Indexed: 01/01/2023] Open
Abstract
PURPOSE Abdominal dynamic contrast-enhanced (DCE) MRI suffers from motion-induced artifacts that can blur images and distort contrast-agent uptake curves. For liver perfusion analysis, image reconstruction with rigid-body motion correction (RMC) can restore distorted portal-venous input functions (PVIF) to higher peak amplitudes. However, RMC cannot correct for liver deformation during breathing. We present a reconstruction algorithm with deformable motion correction (DMC) that enables correction of breathing-induced deformation in the whole abdomen. METHODS Raw data from a golden-angle stack-of-stars gradient-echo sequence were collected for 54 DCE-MRI examinations of 31 patients. For each examination, a respiratory motion signal was extracted from the data and used to reconstruct 21 breathing states from inhale to exhale. The states were aligned with deformable image registration to the end-exhale state. Resulting deformation fields were used to correct back-projection images before reconstruction with view sharing. Images with DMC were compared to uncorrected images and images with RMC. RESULTS DMC significantly increased the PVIF peak amplitude compared to uncorrected images (P << 0.01, mean increase: 8%) but not compared to RMC. The increased PVIF peak amplitude significantly decreased estimated portal-venous perfusion in the liver (P << 0.01, mean decrease: 8 ml/(100 ml·min)). DMC also removed artifacts in perfusion maps at the liver edge and reduced blurring of liver tumors for some patients. CONCLUSIONS DCE-MRI reconstruction with DMC can restore motion-distorted uptake curves in the abdomen and remove motion artifacts from reconstructed images and parameter maps but does not significantly improve perfusion quantification in the liver compared to RMC.
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Affiliation(s)
- Adam Johansson
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - James M Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
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Reynolds HM, Parameswaran BK, Finnegan ME, Roettger D, Lau E, Kron T, Shaw M, Chander S, Siva S. Diffusion weighted and dynamic contrast enhanced MRI as an imaging biomarker for stereotactic ablative body radiotherapy (SABR) of primary renal cell carcinoma. PLoS One 2018; 13:e0202387. [PMID: 30114235 PMCID: PMC6095575 DOI: 10.1371/journal.pone.0202387] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 08/01/2018] [Indexed: 11/19/2022] Open
Abstract
Purpose To explore the utility of diffusion and perfusion changes in primary renal cell carcinoma (RCC) after stereotactic ablative body radiotherapy (SABR) as an early biomarker of treatment response, using diffusion weighted (DWI) and dynamic contrast enhanced (DCE) MRI. Methods Patients enrolled in a prospective pilot clinical trial received SABR for primary RCC, and had DWI and DCE MRI scheduled at baseline, 14 days and 70 days after SABR. Tumours <5cm diameter received a single fraction of 26 Gy and larger tumours received three fractions of 14 Gy. Apparent diffusion coefficient (ADC) maps were computed from DWI data and parametric and pharmacokinetic maps were fitted to the DCE data. Tumour volumes were contoured and statistics extracted. Spearman’s rank correlation coefficients were computed between MRI parameter changes versus the percentage tumour volume change from CT at 6, 12 and 24 months and the last follow-up relative to baseline CT. Results Twelve patients were eligible for DWI analysis, and a subset of ten patients for DCE MRI analysis. DCE MRI from the second follow-up MRI scan showed correlations between the change in percentage voxels with washout contrast enhancement behaviour and the change in tumour volume (ρ = 0.84, p = 0.004 at 12 month CT, ρ = 0.81, p = 0.02 at 24 month CT, and ρ = 0.89, p = 0.001 at last follow-up CT). The change in mean initial rate of enhancement and mean Ktrans at the second follow-up MRI scan were positively correlated with percent tumour volume change at the 12 month CT onwards (ρ = 0.65, p = 0.05 and ρ = 0.66, p = 0.04 at 12 month CT respectively). Changes in ADC kurtosis from histogram analysis at the first follow-up MRI scan also showed positive correlations with the percentage tumour volume change (ρ = 0.66, p = 0.02 at 12 month CT, ρ = 0.69, p = 0.02 at last follow-up CT), but these results are possibly confounded by inflammation. Conclusion DWI and DCE MRI parameters show potential as early response biomarkers after SABR for primary RCC. Further prospective validation using larger patient cohorts is warranted.
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Affiliation(s)
- Hayley M. Reynolds
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- * E-mail:
| | | | - Mary E. Finnegan
- Department of Imaging, Imperial College Healthcare NHS Trust, London, United Kingdom
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | | | - Eddie Lau
- Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Tomas Kron
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mark Shaw
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Sarat Chander
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Shankar Siva
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
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Shi L, He Y, Yuan Z, Benedict S, Valicenti R, Qiu J, Rong Y. Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer. Technol Cancer Res Treat 2018; 17:1533033818782788. [PMID: 29940810 PMCID: PMC6048673 DOI: 10.1177/1533033818782788] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 03/09/2018] [Accepted: 05/16/2018] [Indexed: 12/24/2022] Open
Abstract
Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature.
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Affiliation(s)
- Liting Shi
- Department of Radiology, Taishan Medical University, Tai’an, China
| | - Yaoyao He
- Department of Radiology, Taishan Medical University, Tai’an, China
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Wuhan, China
| | - Stanley Benedict
- Department of Radiation Oncology, University of California Davis
Comprehensive Cancer Center, Sacramento, CA, USA
| | - Richard Valicenti
- Department of Radiation Oncology, University of California Davis
Comprehensive Cancer Center, Sacramento, CA, USA
| | - Jianfeng Qiu
- Department of Radiology, Taishan Medical University, Tai’an, China
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis
Comprehensive Cancer Center, Sacramento, CA, USA
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Santos P, Peck KK, Arevalo-Perez J, Karimi S, Lis E, Yamada Y, Holodny AI, Lyo J. T1-Weighted Dynamic Contrast-Enhanced MR Perfusion Imaging Characterizes Tumor Response to Radiation Therapy in Chordoma. AJNR Am J Neuroradiol 2017; 38:2210-2216. [PMID: 28912284 DOI: 10.3174/ajnr.a5383] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 06/15/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Chordomas notoriously demonstrate a paucity of changes following radiation therapy on conventional MR imaging. We hypothesized that dynamic contrast-enhanced MR perfusion imaging parameters of chordomas would change significantly following radiation therapy. MATERIALS AND METHODS Eleven patients with pathology-proved chordoma who completed dynamic contrast-enhanced MR perfusion imaging pre- and postradiation therapy were enrolled. Quantitative tumor measurements were obtained by 2 attending neuroradiologists. ROIs were used to calculate vascular permeability and plasma volume and generate dynamic contrast-enhancement curves. Quantitative analysis was performed to determine mean and maximum plasma volume and vascular permeability values, while semiquantitative analysis on averaged concentration curves was used to determine the area under the curve. A Mann-Whitney U test at a significance level of P < .05 was used to assess differences of the above parameters between pre- and postradiation therapy. RESULTS Plasma volume mean (pretreatment mean = 0.82; posttreatment mean = 0.42), plasma volume maximum (pretreatment mean = 3.56; posttreatment mean = 2.27), and vascular permeability mean (pretreatment mean = 0.046; posttreatment mean = 0.028) in the ROIs significantly decreased after radiation therapy (P < .05); this change thereby demonstrated the potential for assessing tumor response. Area under the curve values also demonstrated significant differences (P < .05). CONCLUSIONS Plasma volume and vascular permeability decreased after radiation therapy, suggesting that these dynamic contrast-enhanced MR perfusion parameters may be useful for monitoring chordoma growth and response to radiation therapy. Additionally, the characteristic dynamic MR signal intensity-time curve of chordoma may provide a radiographic means of distinguishing chordoma from other spinal lesions.
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Affiliation(s)
- P Santos
- From the Departments of Radiology (P.S., K.K.P., J.A.-P., S.K., E.L., A.I.H., J.L.)
| | - K K Peck
- From the Departments of Radiology (P.S., K.K.P., J.A.-P., S.K., E.L., A.I.H., J.L.) .,Medical Physics (K.K.P.)
| | - J Arevalo-Perez
- From the Departments of Radiology (P.S., K.K.P., J.A.-P., S.K., E.L., A.I.H., J.L.)
| | - S Karimi
- From the Departments of Radiology (P.S., K.K.P., J.A.-P., S.K., E.L., A.I.H., J.L.)
| | - E Lis
- From the Departments of Radiology (P.S., K.K.P., J.A.-P., S.K., E.L., A.I.H., J.L.)
| | - Y Yamada
- Radiation Oncology (Y.Y.), Memorial Sloan Kettering Cancer Center, New York, New York
| | - A I Holodny
- From the Departments of Radiology (P.S., K.K.P., J.A.-P., S.K., E.L., A.I.H., J.L.)
| | - J Lyo
- From the Departments of Radiology (P.S., K.K.P., J.A.-P., S.K., E.L., A.I.H., J.L.)
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Johansson A, Balter J, Cao Y. Rigid-body motion correction of the liver in image reconstruction for golden-angle stack-of-stars DCE MRI. Magn Reson Med 2017; 79:1345-1353. [PMID: 28617993 DOI: 10.1002/mrm.26782] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 05/16/2017] [Accepted: 05/17/2017] [Indexed: 12/25/2022]
Abstract
PURPOSE Respiratory motion can affect pharmacokinetic perfusion parameters quantified from liver dynamic contrast-enhanced MRI. Image registration can be used to align dynamic images after reconstruction. However, intra-image motion blur remains after alignment and can alter the shape of contrast-agent uptake curves. We introduce a method to correct for inter- and intra-image motion during image reconstruction. METHODS Sixteen liver dynamic contrast-enhanced MRI examinations of nine subjects were performed using a golden-angle stack-of-stars sequence. For each examination, an image time series with high temporal resolution but severe streak artifacts was reconstructed. Images were aligned using region-limited rigid image registration within a region of interest covering the liver. The transformations resulting from alignment were used to correct raw data for motion by modulating and rotating acquired lines in k-space. The corrected data were then reconstructed using view sharing. RESULTS Portal-venous input functions extracted from motion-corrected images had significantly greater peak signal enhancements (mean increase: 16%, t-test, P < 0.001) than those from images aligned using image registration after reconstruction. In addition, portal-venous perfusion maps estimated from motion-corrected images showed fewer artifacts close to the edge of the liver. CONCLUSIONS Motion-corrected image reconstruction restores uptake curves distorted by motion. Motion correction also reduces motion artifacts in estimated perfusion parameter maps. Magn Reson Med 79:1345-1353, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Adam Johansson
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - James Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.,Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
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40
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Fite BZ, Kheirolomoom A, Foiret JL, Seo JW, Mahakian LM, Ingham ES, Tam SM, Borowsky AD, Curry FRE, Ferrara KW. Dynamic contrast enhanced MRI detects changes in vascular transport rate constants following treatment with thermally-sensitive liposomal doxorubicin. J Control Release 2017; 256:203-213. [PMID: 28395970 DOI: 10.1016/j.jconrel.2017.04.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2016] [Revised: 03/16/2017] [Accepted: 04/05/2017] [Indexed: 01/03/2023]
Abstract
Temperature-sensitive liposomal formulations of chemotherapeutics, such as doxorubicin, can achieve locally high drug concentrations within a tumor and tumor vasculature while maintaining low systemic toxicity. Further, doxorubicin delivery by temperature-sensitive liposomes can reliably cure local cancer in mouse models. Histological sections of treated tumors have detected red blood cell extravasation within tumors treated with temperature-sensitive doxorubicin and ultrasound hyperthermia. We hypothesize that the local release of drug into the tumor vasculature and resulting high drug concentration can alter vascular transport rate constants along with having direct tumoricidal effects. Dynamic contrast enhanced MRI (DCE-MRI) coupled with a pharmacokinetic model can detect and quantify changes in such vascular transport rate constants. Here, we set out to determine whether changes in rate constants resulting from intravascular drug release were detectable by MRI. We found that the accumulation of gadoteridol was enhanced in tumors treated with temperature-sensitive liposomal doxorubicin and ultrasound hyperthermia. While the initial uptake rate of the small molecule tracer was slower (k1=0.0478±0.011s-1 versus 0.116±0.047s-1) in treated compared to untreated tumors, the tracer was retained after treatment due to a larger reduction in the rate of clearance (k2=0.291±0.030s-1 versus 0.747±0.24s-1). While DCE-MRI assesses a combination of blood flow and permeability, ultrasound imaging of microvascular flow rate is sensitive only to changes in vascular flow rate; based on this technique, blood flow was not significantly altered 30min after treatment. In summary, DCE-MRI provides a means to detect changes that are associated with treatment by thermally-activated particles and such changes can be exploited to enhance local delivery.
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Affiliation(s)
- Brett Z Fite
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.
| | - Azadeh Kheirolomoom
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.
| | - Josquin L Foiret
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.
| | - Jai W Seo
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.
| | - Lisa M Mahakian
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.
| | - Elizabeth S Ingham
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.
| | - Sarah M Tam
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.
| | - Alexander D Borowsky
- Department of Pathology and Laboratory Medicine, University of California, Davis, CA 95616, USA.
| | - Fitz-Roy E Curry
- Department of Physiology and Membrane Biology, University of California, Davis, CA 95616, USA.
| | - Katherine W Ferrara
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.
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41
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Soliman M, Taunk NK, Simons RE, Osborne JR, Kim MM, Szerlip NJ, Spratt DE. Anatomic and functional imaging in the diagnosis of spine metastases and response assessment after spine radiosurgery. Neurosurg Focus 2017; 42:E5. [PMID: 28041315 DOI: 10.3171/2016.9.focus16350] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Spine stereotactic radiosurgery (SSRS) has recently emerged as an increasingly effective treatment for spinal metastases. Studies performed over the past decade have examined the role of imaging in the diagnosis of metastases, as well as treatment response following SSRS. In this paper, the authors describe and review the utility of several imaging modalities in the diagnosis of spinal metastases and monitoring of their response to SSRS. Specifically, we review the role of CT, MRI, and positron emission tomography (PET) in their ability to differentiate between osteoblastic and osteolytic lesions, delineation of initial bony pathology, detection of treatment-related changes in bone density and vertebral compression fracture after SSRS, and tumor response to therapy. Validated consensus guidelines defining the imaging approach to SSRS are needed to standardize the diagnosis and treatment response assessment after SSRS. Future directions of spinal imaging, including advances in targeted tumor-specific molecular imaging markers demonstrate early promise for advancing the role of imaging in SSRS.
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Affiliation(s)
| | | | | | - Joseph R Osborne
- 3Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Nicholas J Szerlip
- 4Neurosurgery, University of Michigan Cancer Center, Ann Arbor, Michigan; and
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42
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Lis E, Saha A, Peck KK, Zatcky J, Zelefsky MJ, Yamada Y, Holodny AI, Bilsky MH, Karimi S. Dynamic contrast-enhanced magnetic resonance imaging of osseous spine metastasis before and 1 hour after high-dose image-guided radiation therapy. Neurosurg Focus 2017; 42:E9. [PMID: 28041318 DOI: 10.3171/2016.9.focus16378] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE High-dose image-guided radiation therapy (HD IGRT) has been instrumental in mitigating some limitations of conventional RT. The recent emergence of dynamic contrast-enhanced (DCE) MRI to investigate tumor physiology can be used to verify the response of human tumors to HD IGRT. The purpose of this study was to evaluate the near-immediate effects of HD IGRT on spine metastases through the use of DCE MRI perfusion studies. METHODS Six patients with spine metastases from prostate, thyroid, and renal cell carcinoma who underwent HD IGRT were studied using DCE MRI prior to and 1 hour after HD IGRT. The DCE perfusion parameters plasma volume (Vp) and vascular permeability (Ktrans) were measured to assess the near-immediate and long-term tumor response. A Mann-Whitney U-test was performed to compare significant changes (at p ≤ 0.05) in perfusion parameters before and after RT. RESULTS The authors observed a precipitous drop in Vp within 1 hour of HD IGRT, with a mean decrease of 65.2%. A significant difference was found between Vp values for before and 1 hour after RT (p ≤ 0.05). No significant change was seen in Vp (p = 0.31) and Ktrans (p = 0.1) from 1 hour after RT to the first follow-up. CONCLUSIONS The data suggest that there is an immediate effect of HD IGRT on the vascularity of spine metastases, as demonstrated by a precipitous decrease in Vp. The DCE MRI studies can detect such changes within 1 hour after RT, and findings are concordant with existing animal models.
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Affiliation(s)
| | | | | | | | | | | | | | - Mark H Bilsky
- 4Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, New York
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43
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Tsegmed U, Kimura T, Nakashima T, Nakamura Y, Higaki T, Imano N, Doi Y, Kenjo M, Ozawa S, Murakami Y, Awai K, Nagata Y. Functional image-guided stereotactic body radiation therapy planning for patients with hepatocellular carcinoma. Med Dosim 2017; 42:97-103. [DOI: 10.1016/j.meddos.2017.01.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 01/12/2017] [Accepted: 01/28/2017] [Indexed: 12/22/2022]
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44
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The Application of Iodine Quantitative Information Obtained by Dual-Source Dual-Energy Computed Tomography on Chemoradiotherapy Effect Monitoring for Cervical Cancer. J Comput Assist Tomogr 2017; 41:737-745. [DOI: 10.1097/rct.0000000000000603] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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45
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You D, Aryal M, Samuels SE, Eisbruch A, Cao Y. Temporal Feature Extraction from DCE-MRI to Identify Poorly Perfused Subvolumes of Tumors Related to Outcomes of Radiation Therapy in Head and Neck Cancer. ACTA ACUST UNITED AC 2016; 2:341-352. [PMID: 28111634 PMCID: PMC5243121 DOI: 10.18383/j.tom.2016.00199] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
This study aimed to develop an automated model to extract temporal features from DCE-MRI in head-and-neck (HN) cancers to localize significant tumor subvolumes having low blood volume (LBV) for predicting local and regional failure after chemoradiation therapy. Temporal features were extracted from time-intensity curves to build classification model for differentiating voxels with LBV from those with high BV. Support vector machine (SVM) classification was trained on the extracted features for voxel classification. Subvolumes with LBV were then assembled from the classified voxels with LBV. The model was trained and validated on independent datasets created from 456 873 DCE curves. The resultant subvolumes were compared to ones derived by a 2-step method via pharmacokinetic modeling of blood volume, and evaluated for classification accuracy and volumetric similarity by DSC. The proposed model achieved an average voxel-level classification accuracy and DSC of 82% and 0.72, respectively. Also, the model showed tolerance on different acquisition parameters of DCE-MRI. The model could be directly used for outcome prediction and therapy assessment in radiation therapy of HN cancers, or even supporting boost target definition in adaptive clinical trials with further validation. The model is fully automatable, extendable, and scalable to extract temporal features of DCE-MRI in other tumors.
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Affiliation(s)
- Daekeun You
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Madhava Aryal
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Stuart E Samuels
- Department of Radiation Oncology, University of Miami, Miami, Florida
| | - Avraham Eisbruch
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yue Cao
- Department of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
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46
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Coolens C, Driscoll B, Foltz W, Pellow C, Menard C, Chung C. Comparison of Voxel-Wise Tumor Perfusion Changes Measured With Dynamic Contrast-Enhanced (DCE) MRI and Volumetric DCE CT in Patients With Metastatic Brain Cancer Treated with Radiosurgery. ACTA ACUST UNITED AC 2016; 2:325-333. [PMID: 30042966 PMCID: PMC6037934 DOI: 10.18383/j.tom.2016.00178] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Dynamic contrast-enhanced (DCE)-MRI metrics are evaluated against volumetric DCE-CT quantitative parameters as a standard for tracer-kinetic validation using a common 4-dimensional temporal dynamic analysis platform in tumor perfusion measurements following stereotactic radiosurgery (SRS) for brain metastases. Patients treated with SRS as part of Research Ethics Board-approved clinical trials underwent volumetric DCE-CT and DCE-MRI at baseline, then at 7 and 21 days after SRS. Temporal dynamic analysis was used to create 3-dimensional pharmacokinetic parameter maps for both modalities. Individual vascular input functions were selected for DCE-CT and a population function was used for DCE-MRI. Semiquantitative and pharmacokinetic DCE parameters were assessed using a modified Tofts model within each tumor at every time point for both modalities for characterization of perfusion and capillary permeability, as well as their dependency on precontrast relaxation times (TRs), T10, and input function. Direct voxel-to-voxel Pearson analysis showed statistically significant correlations between CT and magnetic resonance which peaked at day 7 for Ktrans (R = 0.74, P ≤ .0001). The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxel-wise T10 maps (R = 0.575, P < .001) instead of assigning a fixed T10 value. Comparison of histogram features showed statistically significant correlations between modalities over all tumors for median Ktrans (R = 0.42, P = .01), median area under the enhancement curve (iAUC90) (R = 0.55, P < .01), and median iAUC90 skewness (R = 0.34, P = .03). Statistically significant, strong correlations were found for voxel-wise Ktrans, iAUC90, and ve values between DCE-CT and DCE-MRI. For DCE-MRI, the implementation of voxel-wise T10 maps plays a key role in ensuring the accuracy of heterogeneous pharmacokinetic maps.
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Affiliation(s)
- Catherine Coolens
- Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Ontario, Canada.,TECHNA Institute, University Health Network, Toronto, Ontario, Canada; and
| | - Brandon Driscoll
- Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada
| | - Warren Foltz
- Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Carly Pellow
- Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada
| | - Cynthia Menard
- Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.,Department of Radiation Oncology, University of Montreal Hospital, Montreal, QC, Canada
| | - Caroline Chung
- Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.,TECHNA Institute, University Health Network, Toronto, Ontario, Canada; and
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47
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Shim CS, Lee TY, Cheon YK. Clinical role of contrast-enhanced harmonic endoscopic ultrasound in differentiating pancreatic solid lesions. INTERNATIONAL JOURNAL OF GASTROINTESTINAL INTERVENTION 2016. [DOI: 10.18528/gii150016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- Chan Sup Shim
- Digestive Disease Center, Konkuk University School of Medicine, Seoul, Korea
| | - Tae Yoon Lee
- Digestive Disease Center, Konkuk University School of Medicine, Seoul, Korea
| | - Young Koog Cheon
- Digestive Disease Center, Konkuk University School of Medicine, Seoul, Korea
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48
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Abstract
Cancer therapy is mainly based on different combinations of surgery, radiotherapy, and chemotherapy. Additionally, targeted therapies (designed to disrupt specific tumor hallmarks, such as angiogenesis, metabolism, proliferation, invasiveness, and immune evasion), hormonotherapy, immunotherapy, and interventional techniques have emerged as alternative oncologic treatments. Conventional imaging techniques and current response criteria do not always provide the necessary information regarding therapy success particularly to targeted therapies. In this setting, MR imaging offers an attractive combination of anatomic, physiologic, and molecular information, which may surpass these limitations, and is being increasingly used for therapy response assessment.
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49
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Johansson A, Balter J, Feng M, Cao Y. An Overdetermined System of Transform Equations in Support of Robust DCE-MRI Registration With Outlier Rejection. ACTA ACUST UNITED AC 2016; 2:188-196. [PMID: 28367502 PMCID: PMC5373730 DOI: 10.18383/j.tom.2016.00145] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Quantitative hepatic perfusion parameters derived by fitting dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of liver to a pharmacokinetic model are prone to errors if the dynamic images are not corrected for respiratory motion by image registration. The contrast-induced intensity variations in pre- and postcontrast phases pose challenges for the accuracy of image registration. We propose an overdetermined system of transformation equations between the image volumes in the DCE-MRI series to achieve robust alignment. In this method, we register each volume to every other volume. From the transforms produced by all pairwise registrations, we constructed an overdetermined system of transform equations that was solved robustly by minimizing the L1/2-norm of the residuals. This method was evaluated on a set of 100 liver DCE-MRI examinations from 35 patients by examining the area under spikes appearing in the voxel time–intensity curves. The robust alignment procedure significantly reduced the area under intensity spikes compared with unregistered volumes (P < .001) and volumes registered to a single reference phase (P < .001). Our registration procedure provides a larger number of reliable time–intensity curve samples. The additional reliable samples in the precontrast baseline are important for calculating the postcontrast signal enhancement and thereby for converting intensity to contrast concentration. On the intensity ramp, retained samples help to better describe the uptake dynamics, providing a better foundation for parameter estimation. The presented method also simplifies the analysis of data sets with many patients by eliminating the need for manual intervention during registration.
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Affiliation(s)
- Adam Johansson
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - James Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
| | - Mary Feng
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Radiology, University of Michigan, Ann Arbor, Michigan; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
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50
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Lai YL, Wu CY, Chao KSC. Biological imaging in clinical oncology: radiation therapy based on functional imaging. Int J Clin Oncol 2016; 21:626-632. [PMID: 27384183 DOI: 10.1007/s10147-016-1000-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 05/29/2016] [Indexed: 12/25/2022]
Abstract
Radiation therapy is one of the most effective tools for cancer treatment. In recent years, intensity-modulated radiation therapy has become increasingly popular in that target dose-escalation can be done while sparing adjacent normal tissues. For this reason, the development of measures to pave the way for accurate target delineation is of great interest. With the integration of functional information obtained by biological imaging with radiotherapy, strategies using advanced biological imaging to visualize metabolic pathways and to improve therapeutic index and predict treatment response are discussed in this article.
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Affiliation(s)
- Yo-Liang Lai
- Department of Radiation Oncology, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Chun-Yi Wu
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - K S Clifford Chao
- China Medical University, 91 Hsueh-Shih Road, Taichung, 40402, Taiwan.
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