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Zhao Y, Pan J, Han B, Hou W, Li B, Wang J, Wang G, He Y, Ma M, Zhou J, Yu C, Sun SK. Ultrahigh-Resolution Visualization of Vascular Heterogeneity in Brain Tumors via Magnetic Nanoparticles-Enhanced Susceptibility-Weighted Imaging. ACS NANO 2024. [PMID: 39094075 DOI: 10.1021/acsnano.4c02611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
The precise assessment of vascular heterogeneity in brain tumors is vital for diagnosing, grading, predicting progression, and guiding treatment decisions. However, currently, there is a significant shortage of high-resolution imaging approaches. Herein, we propose a contrast-enhanced susceptibility-weighted imaging (CE-SWI) utilizing the minimalist dextran-modified Fe3O4 nanoparticles (Dextran@Fe3O4 NPs) for ultrahigh-resolution mapping of vasculature in brain tumors. The Dextran@Fe3O4 NPs are prepared via a facile coprecipitation method under room temperature, and exhibit small hydrodynamic size (28 nm), good solubility, excellent biocompatibility, and high transverse relaxivity (r2*, 159.7 mM-1 s-1) under 9.4 T magnetic field. The Dextran@Fe3O4 NPs-enhanced SWI can increase the contrast-to-noise ratio (CNR) of cerebral vessels to 2.5 times that before injection and achieves ultrahigh-spatial-resolution visualization of microvessels as small as 0.1 mm in diameter. This advanced imaging capability not only allows for the detailed mapping of both enlarged peritumoral drainage vessels and the intratumoral microvessels, but also facilitates the sensitive imaging detection of vascular permeability deterioration in a C6 cells-bearing rat glioblastoma model. Our proposed Dextran@Fe3O4 NPs-enhanced SWI provides a powerful imaging technique with great clinical translation potential for the precise theranostics of brain tumors.
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Affiliation(s)
- Yujie Zhao
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jinbin Pan
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Bing Han
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wenjing Hou
- Department of Diagnostic and Therapeutic Ultrasonography, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Bingjie Li
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jiaojiao Wang
- Department of Radiology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China
| | - Guohe Wang
- School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin 300204, China
| | - Yujing He
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Min Ma
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Junzi Zhou
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Shao-Kai Sun
- School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin 300204, China
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Li Z, Wang D, Ooi MB, Choudhary P, Ragunathan S, Karis JP, Pipe JG, Quarles CC, Stokes AM. A 3D dual-echo spiral sequence for simultaneous dynamic susceptibility contrast and dynamic contrast-enhanced MRI with single bolus injection. Magn Reson Med 2024; 92:631-644. [PMID: 38469930 PMCID: PMC11207201 DOI: 10.1002/mrm.30077] [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: 10/27/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE Perfusion MRI reveals important tumor physiological and pathophysiologic information, making it a critical component in managing brain tumor patients. This study aimed to develop a dual-echo 3D spiral technique with a single-bolus scheme to simultaneously acquire both dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) data and overcome the limitations of current EPI-based techniques. METHODS A 3D spiral-based technique with dual-echo acquisition was implemented and optimized on a 3T MRI scanner with a spiral staircase trajectory and through-plane SENSE acceleration for improved speed and image quality, in-plane variable-density undersampling combined with a sliding-window acquisition and reconstruction approach for increased speed, and an advanced iterative deblurring algorithm. Four volunteers were scanned and compared with the standard of care (SOC) single-echo EPI and a dual-echo EPI technique. Two patients were scanned with the spiral technique during a preload bolus and compared with the SOC single-echo EPI collected during the second bolus injection. RESULTS Volunteer data demonstrated that the spiral technique achieved high image quality, reduced geometric artifacts, and high temporal SNR compared with both single-echo and dual-echo EPI. Patient perfusion data showed that the spiral acquisition achieved accurate DSC quantification comparable to SOC single-echo dual-dose EPI, with the additional DCE information. CONCLUSION A 3D dual-echo spiral technique was developed to simultaneously acquire both DSC and DCE data in a single-bolus injection with reduced contrast use. Preliminary volunteer and patient data demonstrated increased temporal SNR, reduced geometric artifacts, and accurate perfusion quantification, suggesting a competitive alternative to SOC-EPI techniques for brain perfusion MRI.
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Affiliation(s)
- Zhiqiang Li
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
| | - Dinghui Wang
- Department of Radiology, Mayo Clinic, Rochester, MN USA
| | | | - Poonam Choudhary
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
| | | | - John P Karis
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
| | - James G Pipe
- Department of Radiology, Mayo Clinic, Rochester, MN USA
- Department of Radiology, University of Wisconsin, Madison, WI USA
| | - C Chad Quarles
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Ashley M Stokes
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
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Foltyn-Dumitru M, Kessler T, Sahm F, Wick W, Heiland S, Bendszus M, Vollmuth P, Schell M. Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities. Neuro Oncol 2024; 26:1099-1108. [PMID: 38153923 PMCID: PMC11145444 DOI: 10.1093/neuonc/noad259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND While the association between diffusion and perfusion magnetic resonance imaging (MRI) and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival. METHODS A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient normalized relative cerebral blood volume and relative cerebral blood flow were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using partition around medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership. RESULTS Using the training dataset (231/289) we identified 2 stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (P = 0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (P ≤ 0.004 each). CONCLUSIONS Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion, and perfusion MRI in predicting survival rates of glioblastoma patients.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Kessler
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
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Shalom ES, Van Loo S, Khan A, Sourbron SP. Identifiability of spatiotemporal tissue perfusion models. Phys Med Biol 2024; 69:115034. [PMID: 38636525 DOI: 10.1088/1361-6560/ad4087] [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: 12/06/2023] [Accepted: 04/18/2024] [Indexed: 04/20/2024]
Abstract
Objective.Standard models for perfusion quantification in DCE-MRI produce a bias by treating voxels as isolated systems. Spatiotemporal models can remove this bias, but it is unknown whether they are fundamentally identifiable. The aim of this study is to investigate this question in silico using one-dimensional toy systems with a one-compartment blood flow model and a two-compartment perfusion model.Approach.For each of the two models, identifiability is explored theoretically and in-silico for three systems. Concentrations over space and time are simulated by forward propagation. Different levels of noise and temporal undersampling are added to investigate sensitivity to measurement error. Model parameters are fitted using a standard gradient descent algorithm, applied iteratively with a stepwise increasing time window. Model fitting is repeated with different initial values to probe uniqueness of the solution. Reconstruction accuracy is quantified for each parameter by comparison to the ground truth.Main results.Theoretical analysis shows that flows and volume fractions are only identifiable up to a constant, and that this degeneracy can be removed by proper choice of parameters. Simulations show that in all cases, the tissue concentrations can be reconstructed accurately. The one-compartment model shows accurate reconstruction of blood velocities and arterial input functions, independent of the initial values and robust to measurement error. The two-compartmental perfusion model was not fully identifiable, showing good reconstruction of arterial velocities and input functions, but multiple valid solutions for the perfusion parameters and venous velocities, and a strong sensitivity to measurement error in these parameters.Significance.These results support the use of one-compartment spatiotemporal flow models, but two-compartment perfusion models were not sufficiently identifiable. Future studies should investigate whether this degeneracy is resolved in more realistic 2D and 3D systems, by adding physically justified constraints, or by optimizing experimental parameters such as injection duration or temporal resolution.
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Affiliation(s)
- Eve S Shalom
- School of Physics and Astronomy, The University of Leeds, United Kingdom
| | - Sven Van Loo
- Department of Applied Physics, Ghent University, Belgium
| | - Amirul Khan
- School of Civil Engineering, The University of Leeds, United Kingdom
| | - Steven P Sourbron
- Division of Clinical Medicine, University of Sheffield, United Kingdom
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Park CJ, Kim S, Han K, Ahn SS, Kim D, Park YW, Chang JH, Kim SH, Lee SK. Diffusion- and Perfusion-Weighted MRI Radiomics for Survival Prediction in Patients with Lower-Grade Gliomas. Yonsei Med J 2024; 65:283-292. [PMID: 38653567 PMCID: PMC11045349 DOI: 10.3349/ymj.2023.0323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/27/2023] [Accepted: 12/13/2023] [Indexed: 04/25/2024] Open
Abstract
PURPOSE Lower-grade gliomas of histologic grades 2 and 3 follow heterogenous clinical outcomes, which necessitates risk stratification. This study aimed to evaluate whether diffusion-weighted and perfusion-weighted MRI radiomics allow overall survival (OS) prediction in patients with lower-grade gliomas and investigate its prognostic value. MATERIALS AND METHODS In this retrospective study, radiomic features were extracted from apparent diffusion coefficient, relative cerebral blood volume map, and Ktrans map in patients with pathologically confirmed lower-grade gliomas (January 2012-February 2019). The radiomics risk score (RRS) calculated from selected features constituted a radiomics model. Multivariable Cox regression analysis, including clinical features and RRS, was performed. The models' integrated area under the receiver operating characteristic curves (iAUCs) were compared. The radiomics model combined with clinical features was presented as a nomogram. RESULTS The study included 129 patients (median age, 44 years; interquartile range, 37-57 years; 63 female): 90 patients for training set and 39 patients for test set. The RRS was an independent risk factor for OS with a hazard ratio of 6.01. The combined clinical and radiomics model achieved superior performance for OS prediction compared to the clinical model in both training (iAUC, 0.82 vs. 0.72, p=0.002) and test sets (0.88 vs. 0.76, p=0.04). The radiomics nomogram combined with clinical features exhibited good agreement between the actual and predicted OS with C-index of 0.83 and 0.87 in the training and test sets, respectively. CONCLUSION Adding diffusion- and perfusion-weighted MRI radiomics to clinical features improved survival prediction in lower-grade glioma.
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Affiliation(s)
- Chae Jung Park
- Department of Radiology, Research Institute of Radiological Science, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sooyon Kim
- Department of Applied Statistics, Yonsei University, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology, Center for Clinical Imaging Data Science, Research Institute of Radiological Sciences, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology, Center for Clinical Imaging Data Science, Research Institute of Radiological Sciences, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Dain Kim
- Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Korea
| | - Yae Won Park
- Department of Radiology, Center for Clinical Imaging Data Science, Research Institute of Radiological Sciences, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Center for Clinical Imaging Data Science, Research Institute of Radiological Sciences, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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Hoggarth AR, Muthukumar S, Thomas SM, Crowley J, Kiser J, Witcher MR. Clinical Theranostics in Recurrent Gliomas: A Review. Cancers (Basel) 2024; 16:1715. [PMID: 38730666 PMCID: PMC11083317 DOI: 10.3390/cancers16091715] [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: 03/30/2024] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
Gliomas represent the most commonly occurring tumors in the central nervous system and account for approximately 80% of all malignant primary brain tumors. With a high malignancy and recurrence risk, the prognosis of high-grade gliomas is poor, with a mean survival time of 12-18 months. While contrast-enhanced MRI serves as the standard diagnostic imaging modality for gliomas, it faces limitations in the evaluation of recurrent gliomas, failing to distinguish between treatment-related changes and tumor progression, and offers no direct therapeutic options. Recent advances in imaging modalities have attempted to address some of these limitations, including positron emission tomography (PET), which has demonstrated success in delineating tumor margins and guiding the treatment of recurrent gliomas. Additionally, with the advent of theranostics in nuclear medicine, PET tracers, when combined with therapeutic agents, have also evolved beyond a purely diagnostic modality, serving both diagnostic and therapeutic roles. This review will discuss the growing involvement of theranostics in diagnosing and treating recurrent gliomas and address the associated impact on quality of life and functional recovery.
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Affiliation(s)
- Austin R. Hoggarth
- Department of Neurosurgery, Carilion Clinic, 1906 Belleview Avenue, Roanoke, VA 24014, USA;
- Virginia Tech Carilion School of Medicine, 2 Riverside Circle, Roanoke, VA 24016, USA; (S.M.); (S.M.T.)
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Sankar Muthukumar
- Virginia Tech Carilion School of Medicine, 2 Riverside Circle, Roanoke, VA 24016, USA; (S.M.); (S.M.T.)
| | - Steven M. Thomas
- Virginia Tech Carilion School of Medicine, 2 Riverside Circle, Roanoke, VA 24016, USA; (S.M.); (S.M.T.)
| | - James Crowley
- Carilion Clinic Radiology, Roanoke, VA 24016, USA; (J.C.); (J.K.)
| | - Jackson Kiser
- Carilion Clinic Radiology, Roanoke, VA 24016, USA; (J.C.); (J.K.)
| | - Mark R. Witcher
- Department of Neurosurgery, Carilion Clinic, 1906 Belleview Avenue, Roanoke, VA 24014, USA;
- Virginia Tech Carilion School of Medicine, 2 Riverside Circle, Roanoke, VA 24016, USA; (S.M.); (S.M.T.)
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
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Tang L, Wu T, Hu R, Gu Q, Yang X, Mao H. Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI). Quant Imaging Med Surg 2024; 14:2774-2787. [PMID: 38617153 PMCID: PMC11007532 DOI: 10.21037/qims-23-1471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 02/04/2024] [Indexed: 04/16/2024]
Abstract
Background Magnetic resonance imaging (MRI) is a primary non-invasive imaging modality for tumor segmentation, leveraging its exceptional soft tissue contrast and high resolution. Current segmentation methods typically focus on structural MRI, such as T1-weighted post-contrast-enhanced or fluid-attenuated inversion recovery (FLAIR) sequences. However, these methods overlook the blood perfusion and hemodynamic properties of tumors, readily derived from dynamic susceptibility contrast (DSC) enhanced MRI. This study introduces a novel hybrid method combining density-based analysis of hemodynamic properties in time-dependent perfusion imaging with deep learning spatial segmentation techniques to enhance tumor segmentation. Methods First, a U-Net convolutional neural network (CNN) is employed on structural images to delineate a region of interest (ROI). Subsequently, Hierarchical Density-Based Scans (HDBScan) are employed within the ROI to augment segmentation by exploring intratumoral hemodynamic heterogeneity through the investigation of tumor time course profiles unveiled in DSC MRI. Results The approach was tested and evaluated using a cohort of 513 patients from the open-source University of Pennsylvania glioblastoma database (UPENN-GBM) dataset, achieving a 74.83% Intersection over Union (IoU) score when compared to structural-only segmentation. The algorithm also exhibited increased precision and localized predictions of heightened segmentation boundary complexity, resulting in a 146.92% increase in contour complexity (ICC) compared to the reference standard provided by the UPENN-GBM dataset. Importantly, segmenting tumors with the developed new approach uncovered a negative correlation of the tumor volume with the scores in the Karnofsky Performance Scale (KPS) clinically used for assessing the functional status of patients (-0.309), which is not observed with the prevailing segmentation standard. Conclusions This work demonstrated that including hemodynamic properties of tissues from DSC MRI can improve existing structural or morphological feature-based tumor segmentation techniques with additional information on tumor biology and physiology. This approach can also be applied to other clinical indications that use perfusion MRI for diagnosis or treatment monitoring.
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Affiliation(s)
- Leonardo Tang
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Tianhe Wu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Ranliang Hu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Quanquan Gu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA, USA
| | - Hui Mao
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
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Hilario A, Salvador E, Cardenas A, Romero J, Lechuga C, Chen Z, Martinez de Aragon A, Perez-Nuñez A, Hernandez-Lain A, Sepulveda J, Lagares A, Toldos O, Rodriguez-Gonzalez V, Ramos A. Low rCBV values in glioblastoma tumor progression under chemoradiotherapy. Neuroradiology 2024; 66:317-323. [PMID: 38183424 DOI: 10.1007/s00234-023-03279-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 12/26/2023] [Indexed: 01/08/2024]
Abstract
PURPOSE After standard treatment for glioblastoma, perfusion MRI remains challenging for differentiating tumor progression from post-treatment changes. Our objectives were (1) to correlate rCBV values at diagnosis and at first tumor progression and (2) to analyze the relationship of rCBV values at tumor recurrence with enhancing volume, localization of tumor progression, and time elapsed since the end of radiotherapy in tumor recurrence. METHODS Inclusion criteria were (1) age > 18 years, (2) histologically confirmed glioblastoma treated with STUPP regimen, and (3) tumor progression according to RANO criteria > 12 weeks after radiotherapy. Co-registration of segmented enhancing tumor VOIs with dynamic susceptibility contrast perfusion MRI was performed using Olea Sphere software. For tumor recurrence, we correlated rCBV values with enhancing tumor volume, with recurrence localization, and with time elapsed from the end of radiotherapy to progression. Analyses were performed with SPSS software. RESULTS Sixty-four patients with glioblastoma were included in the study. Changes in rCBV values between diagnosis and first tumor progression were significant (p < 0.001), with a mean and median decreases of 32% and 46%, respectively. Mean rCBV values were also different (p < 0.01) when tumors progressed distally (radiation field rCBV values of 1.679 versus 3.409 distally). However, changes and, therefore, low rCBV values after radiotherapy in tumor recurrence were independent of time. CONCLUSION Chemoradiation alters tumor perfusion and rCBV values may be decreased in the setting of tumor progression. Changes in rCBV values with respect to diagnosis, with low rCBV in tumor progression, are independent of time but related to the site of recurrence.
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Affiliation(s)
- A Hilario
- Department of Radiology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain.
| | - E Salvador
- Department of Radiology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - A Cardenas
- Department of Radiology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - J Romero
- Department of Radiology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - C Lechuga
- Department of Radiology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - Z Chen
- Department of Radiology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - A Martinez de Aragon
- Department of Radiology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - A Perez-Nuñez
- Department of Neurosurgery, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - A Hernandez-Lain
- Department of Neuropathology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - J Sepulveda
- Department of Medical Oncology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - A Lagares
- Department of Neurosurgery, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - O Toldos
- Department of Neuropathology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - V Rodriguez-Gonzalez
- Department of Radiation Oncology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
| | - A Ramos
- Department of Radiology, Hospital 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain
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Lee J, Chen MM, Liu HL, Ucisik FE, Wintermark M, Kumar VA. MR Perfusion Imaging for Gliomas. Magn Reson Imaging Clin N Am 2024; 32:73-83. [PMID: 38007284 DOI: 10.1016/j.mric.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
Accurate diagnosis and treatment evaluation of patients with gliomas is imperative to make clinical decisions. Multiparametric MR perfusion imaging reveals physiologic features of gliomas that can help classify them according to their histologic and molecular features as well as distinguish them from other neoplastic and nonneoplastic entities. It is also helpful in distinguishing tumor recurrence or progression from radiation necrosis, pseudoprogression, and pseudoresponse, which is difficult with conventional MR imaging. This review provides an update on MR perfusion imaging for the diagnosis and treatment monitoring of patients with gliomas following standard-of-care chemoradiation therapy and other treatment regimens such as immunotherapy.
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Affiliation(s)
- Jina Lee
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Melissa M Chen
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - F Eymen Ucisik
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Max Wintermark
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Vinodh A Kumar
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA.
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Herr J, Stoyanova R, Mellon EA. Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review. Crit Rev Oncog 2024; 29:33-65. [PMID: 38683153 DOI: 10.1615/critrevoncog.2023050852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.
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Affiliation(s)
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
| | - Eric Albert Mellon
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
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11
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Dobeson CB, Birkbeck M, Bhatnagar P, Hall J, Pearson R, West S, English P, Butteriss D, Perthen J, Lewis J. Perfusion MRI in the evaluation of brain metastases: current practice review and rationale for study of baseline MR perfusion imaging prior to stereotactic radiosurgery (STARBEAM-X). Br J Radiol 2023; 96:20220462. [PMID: 37660364 PMCID: PMC10646666 DOI: 10.1259/bjr.20220462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/04/2023] [Accepted: 08/21/2023] [Indexed: 09/05/2023] Open
Abstract
Stereotactic radiosurgery is an established focal treatment for brain metastases with high local control rates. An important side-effect of stereotactic radiosurgery is the development of radionecrosis. On conventional MR imaging, radionecrosis and tumour progression often have similar appearances, but have contrasting management approaches. Perfusion MR imaging is often used in the post-treatment setting in order to help distinguish between the two, but image interpretation can be fraught with challenges.Perfusion MR plays an established role in the baseline and post-treatment evaluation of primary brain tumours and a number of studies have concentrated on the value of perfusion imaging in brain metastases. Of the parameters generated, relative cerebral blood volume is the most widely used variable in terms of its clinical value in differentiating between radionecrosis and tumour progression. Although it has been suggested that the relative cerebral blood volume tends to be elevated in active metastatic disease following treatment with radiosurgery, but not with treatment-related changes, the literature available on interpretation of the ratios provided in the context of defining tumour progression is not consistent.This article aims to provide an overview of the role perfusion MRI plays in the assessment of brain metastases and introduces the rationale for the STARBEAM-X study (Study of assessment of radionecrosis in brain metastases using MR perfusion extra imaging), which will prospectively evaluate baseline perfusion imaging in brain metastases. We hope this will allow insight into the vascular appearance of metastases from different primary sites, and aid in the interpretation of post-treatment perfusion imaging.
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Affiliation(s)
| | - Matthew Birkbeck
- Northern Medical Physics and Clinical Engineering, Freeman Hospital, Newcastle upon Tyne, UK
| | - Priya Bhatnagar
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Julie Hall
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Rachel Pearson
- Department of Oncology, Northern Centre for Cancer Care, Freeman Hospital, Newcastle upon Tyne, UK
| | - Serena West
- Department of Oncology, Northern Centre for Cancer Care, Freeman Hospital, Newcastle upon Tyne, UK
| | - Philip English
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - David Butteriss
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Joanna Perthen
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Joanne Lewis
- Department of Oncology, Northern Centre for Cancer Care, Freeman Hospital, Newcastle upon Tyne, UK
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12
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Sumiyoshi A, Shibata S, Lazarova D, Zhelev Z, Aoki I, Bakalova R. Tolerable treatment of glioblastoma with redox-cycling 'mitocans': a comparative study in vivo. Redox Rep 2023; 28:2220531. [PMID: 37581329 PMCID: PMC10435007 DOI: 10.1080/13510002.2023.2220531] [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] [Indexed: 08/16/2023] Open
Abstract
Objectives: The present study describes a pharmacological strategy for the treatment of glioblastoma by redoxcycling 'mitocans' such as quinone/ascorbate combination drugs, based on their tumor-selective redox-modulating effects and tolerance to normal cells and tissues.Methods: Experiments were performed on glioblastoma mice (orthotopic model) treated with coenzyme Q0/ascorbate (Q0/A). The drug was injected intracranially in a single dose. The following parameters were analyzed in vivo using MRI orex vivo using conventional assays: tumor growth, survival, cerebral and tumor perfusion, tumor cell density, tissue redox-state, and expression of tumor-associated NADH oxidase (tNOX).Results: Q0/A markedly suppressed tumor growth and significantly increased survival of glioblastoma mice. This was accompanied by increased oxidative stress in the tumor but not in non-cancerous tissues, increased tumor blood flow, and downregulation of tNOX. The redox-modulating and anticancer effects of Q0/A were more pronounced than those of menadione/ascorbate (M/A) obtained in our previous study. No adverse drug-related side-effects were observed in glioblastoma mice treated with Q0/A.Discussion: Q0/A differentiated cancer cells and tissues, particularly glioblastoma, from normal ones by redox targeting, causing a severe oxidative stress in the tumor but not in non-cancerous tissues. Q0/A had a pronounced anticancer activity and could be considered safe for the organism within certain concentration limits. The results suggest that the rate of tumor resorption and metabolism of toxic residues must be controlled and maintained within tolerable limits to achieve longer survival, especially at intracranial drug administration.
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Affiliation(s)
- Akira Sumiyoshi
- Department of Molecular Imaging and Theranostics, National Institutes for Quantum Science and Technology (QST), Chiba, Japan
| | - Sayaka Shibata
- Department of Molecular Imaging and Theranostics, National Institutes for Quantum Science and Technology (QST), Chiba, Japan
| | - Dessislava Lazarova
- Faculty of Medicine, Sofia University, “St. Kliment Ohridski”, Sofia, Bulgaria
| | - Zhivko Zhelev
- Faculty of Medicine, Trakia University, Stara Zagora, Bulgaria
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Ichio Aoki
- Department of Molecular Imaging and Theranostics, National Institutes for Quantum Science and Technology (QST), Chiba, Japan
| | - Rumiana Bakalova
- Department of Molecular Imaging and Theranostics, National Institutes for Quantum Science and Technology (QST), Chiba, Japan
- Faculty of Medicine, Sofia University, “St. Kliment Ohridski”, Sofia, Bulgaria
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13
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Tunlayadechanont P, Panyaping T, Chansakul T, Hirunpat P, Kampaengtip A. Intravoxel incoherent motion for differentiating residual/recurrent tumor from post-treatment change in patients with high-grade glioma. Neuroradiol J 2023; 36:657-664. [PMID: 37105183 PMCID: PMC10649527 DOI: 10.1177/19714009231173108] [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] [Indexed: 04/29/2023] Open
Abstract
PURPOSE To investigate the diagnostic value of f derived from IVIM technique and to correlate it with rCBV derived from DSC for the differentiation of residual/recurrent tumor from post-treatment change in patients with high-grade glioma. MATERIALS AND METHODS Patients who underwent MR imaging with IVIM and DSC studies for evaluation of high-grade glioma after standard treatment were enrolled in this retrospective study. For qualitative analysis, the f and rCBV maps were interpreted as hypoperfused or hyperperfused in each parameter. Quantitative analysis was performed using ROI analysis in f and rCBV parameters. The lesions were divided into residual/recurrent tumor and post-treatment change groups. RESULTS Nineteen patients with high-grade glioma were included. In qualitative analysis, the f-map shows higher sensitivity (100.0%) than rCBV map (92.3%), while the rCBV map shows higher specificity (100.0%) than the f-map (83.3%). In quantitative analysis, the optimal cutoff values of 1.19 for f and 1.06 for rCBV are shown to provide high diagnostic value with high sensitivity (91.7%) for both parameters but slightly higher specificity of rCBV (85.7%) than f (71.4%). The correlation between f and rCBV was good with ICC of 0.810. CONCLUSION The f value measured by IVIM technique, non-contrast perfusion technique, has high diagnostic performance and potential to be an alternative method to CBV measured by DSC for differentiation between residual/recurrent tumor and post-treatment change in patients with high-grade glioma.
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Affiliation(s)
- Padcha Tunlayadechanont
- Division of Neurological Radiology, Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Theeraphol Panyaping
- Division of Neurological Radiology, Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Thanissara Chansakul
- Division of Neurological Radiology, Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Pornrujee Hirunpat
- Division of Neurological Radiology, Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Adun Kampaengtip
- Division of Neurological Radiology, Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
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14
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Khalili N, Shooli H, Hosseini N, Fathi Kazerooni A, Familiar A, Bagheri S, Anderson H, Bagley SJ, Nabavizadeh A. Adding Value to Liquid Biopsy for Brain Tumors: The Role of Imaging. Cancers (Basel) 2023; 15:5198. [PMID: 37958372 PMCID: PMC10650848 DOI: 10.3390/cancers15215198] [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: 09/18/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
Clinical management in neuro-oncology has changed to an integrative approach that incorporates molecular profiles alongside histopathology and imaging findings. While the World Health Organization (WHO) guideline recommends the genotyping of informative alterations as a routine clinical practice for central nervous system (CNS) tumors, the acquisition of tumor tissue in the CNS is invasive and not always possible. Liquid biopsy is a non-invasive approach that provides the opportunity to capture the complex molecular heterogeneity of the whole tumor through the detection of circulating tumor biomarkers in body fluids, such as blood or cerebrospinal fluid (CSF). Despite all of the advantages, the low abundance of tumor-derived biomarkers, particularly in CNS tumors, as well as their short half-life has limited the application of liquid biopsy in clinical practice. Thus, it is crucial to identify the factors associated with the presence of these biomarkers and explore possible strategies that can increase the shedding of these tumoral components into biological fluids. In this review, we first describe the clinical applications of liquid biopsy in CNS tumors, including its roles in the early detection of recurrence and monitoring of treatment response. We then discuss the utilization of imaging in identifying the factors that affect the detection of circulating biomarkers as well as how image-guided interventions such as focused ultrasound can help enhance the presence of tumor biomarkers through blood-brain barrier (BBB) disruption.
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Affiliation(s)
- Nastaran Khalili
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (N.K.); (A.F.K.); (A.F.)
| | - Hossein Shooli
- Department of Radiology, Bushehr University of Medical Sciences, Bushehr 75146-33196, Iran
| | - Nastaran Hosseini
- School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran;
| | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (N.K.); (A.F.K.); (A.F.)
- AI2D Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ariana Familiar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (N.K.); (A.F.K.); (A.F.)
| | - Sina Bagheri
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (H.A.)
| | - Hannah Anderson
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (H.A.)
| | - Stephen J. Bagley
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (N.K.); (A.F.K.); (A.F.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (H.A.)
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15
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van Dijken BRJ, Doff AR, Enting RH, van Laar PJ, Jeltema HR, Dierckx RAJO, van der Hoorn A. Influence of MRI Follow-Up on Treatment Decisions during Standard Concomitant and Adjuvant Chemotherapy in Patients with Glioblastoma: Is Less More? Cancers (Basel) 2023; 15:4973. [PMID: 37894340 PMCID: PMC10605145 DOI: 10.3390/cancers15204973] [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: 09/15/2023] [Revised: 10/03/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
MRI is the gold standard for treatment response assessments for glioblastoma. However, there is no consensus regarding the optimal interval for MRI follow-up during standard treatment. Moreover, a reliable assessment of treatment response is hindered by the occurrence of pseudoprogression. It is unknown if a radiological follow-up strategy at 2-3 month intervals actually benefits patients and how it influences clinical decision making about the continuation or discontinuation of treatment. This study assessed the consequences of scheduled follow-up scans post-chemoradiotherapy (post-CCRT), after three cycles of adjuvant chemotherapy [TMZ3/6], and after the completion of treatment [TMZ6/6]), and of unscheduled scans on treatment decisions during standard concomitant and adjuvant treatment in glioblastoma patients. Additionally, we evaluated how often follow-up scans resulted in diagnostic uncertainty (tumor progression versus pseudoprogression), and whether perfusion MRI improved clinical decision making. Scheduled follow-up scans during standard treatment in glioblastoma patients rarely resulted in an early termination of treatment (2.3% post-CCRT, 3.2% TMZ3/6, and 7.8% TMZ6/6), but introduced diagnostic uncertainty in 27.7% of cases. Unscheduled scans resulted in more major treatment consequences (30%; p < 0.001). Perfusion MRI caused less diagnostic uncertainty (p = 0.021) but did not influence treatment consequences (p = 0.871). This study does not support the current pragmatic follow-up strategy and suggests a more tailored follow-up approach.
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Affiliation(s)
- Bart R. J. van Dijken
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands
| | - Annerieke R. Doff
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands
| | - Roelien H. Enting
- Department of Neurology, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands;
| | - Peter Jan van Laar
- Department of Radiology, Hospital Group Twente, 7600 SZ Almelo, The Netherlands
| | - Hanne-Rinck Jeltema
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands;
| | - Rudi A. J. O. Dierckx
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands
- Department of Nuclear Medicine, Medical Imaging Center, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands
| | - Anouk van der Hoorn
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands
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16
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Huang Q, Wu J, Le N, Shen Y, Guo P, Schreck KC, Kamson D, Blair L, Heo HY, Li X, Li W, Sair HL, Blakeley JO, Laterra J, Holdhoff M, Grossman SA, Mukherjee D, Bettegowda C, van Zijl P, Zhou J, Jiang S. CEST2022: Amide proton transfer-weighted MRI improves the diagnostic performance of multiparametric non-contrast-enhanced MRI techniques in patients with post-treatment high-grade gliomas. Magn Reson Imaging 2023; 102:222-228. [PMID: 37321378 PMCID: PMC10528251 DOI: 10.1016/j.mri.2023.06.003] [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: 03/02/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023]
Abstract
New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional-and even some advanced MRI techniques-are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting. In this study, we evaluated and compared the diagnostic performances of APTw MRI with several non-contrast-enhanced MRI sequences, such as diffusion-weighted imaging, susceptibility-weighted imaging, and pseudo-continuous arterial spin labeling. Thirty-nine scans from 28 glioma patients were obtained on a 3 T MRI scanner. A histogram analysis approach was employed to extract parameters from each tumor area. Statistically significant parameters (P < 0.05) were selected to train multivariate logistic regression models to evaluate the performance of MRI sequences. Multiple histogram parameters, particularly from APTw and pseudo-continuous arterial spin labeling images, demonstrated significant differences between treatment effect and recurrent tumor. The regression model trained on the combination of all significant histogram parameters achieved the best result (area under the curve = 0.89). We found that APTw images added value to other advanced MR images for the differentiation of treatment effect and tumor recurrence.
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Affiliation(s)
- Qianqi Huang
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jingpu Wu
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nhat Le
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yiqing Shen
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Pengfei Guo
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Karisa C Schreck
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David Kamson
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Lindsay Blair
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Hye-Young Heo
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Xu Li
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Wenbo Li
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Haris L Sair
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jaishri O Blakeley
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - John Laterra
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Matthias Holdhoff
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Stuart A Grossman
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Debraj Mukherjee
- Department of Neurosurgery, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Chetan Bettegowda
- Department of Neurosurgery, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Peter van Zijl
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jinyuan Zhou
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Shanshan Jiang
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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17
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Li KL, Lewis D, Zhu X, Coope DJ, Djoukhadar I, King AT, Cootes T, Jackson A. A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis. Pharmaceuticals (Basel) 2023; 16:1282. [PMID: 37765090 PMCID: PMC10534691 DOI: 10.3390/ph16091282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/01/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
This study aimed to develop and evaluate a new DCE-MRI processing technique that combines LEGATOS, a dual-temporal resolution DCE-MRI technique, with multi-kinetic models. This technique enables high spatial resolution interrogation of flow and permeability effects, which is currently challenging to achieve. Twelve patients with neurofibromatosis type II-related vestibular schwannoma (20 tumours) undergoing bevacizumab therapy were imaged at 1.5 T both before and at 90 days following treatment. Using the new technique, whole-brain, high spatial resolution images of the contrast transfer coefficient (Ktrans), vascular fraction (vp), extravascular extracellular fraction (ve), capillary plasma flow (Fp), and the capillary permeability-surface area product (PS) could be obtained, and their predictive value was examined. Of the five microvascular parameters derived using the new method, baseline PS exhibited the strongest correlation with the baseline tumour volume (p = 0.03). Baseline ve showed the strongest correlation with the change in tumour volume, particularly the percentage tumour volume change at 90 days after treatment (p < 0.001), and PS demonstrated a larger reduction at 90 days after treatment (p = 0.0001) when compared to Ktrans or Fp alone. Both the capillary permeability-surface area product (PS) and the extravascular extracellular fraction (ve) significantly differentiated the 'responder' and 'non-responder' tumour groups at 90 days (p < 0.05 and p < 0.001, respectively). These results highlight that this novel DCE-MRI analysis approach can be used to evaluate tumour microvascular changes during treatment and the need for future larger clinical studies investigating its role in predicting antiangiogenic therapy response.
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Affiliation(s)
- Ka-Loh Li
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (K.-L.L.); (T.C.); (A.J.)
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester M13 9PL, UK; (D.L.); (D.J.C.); (A.T.K.)
| | - Daniel Lewis
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester M13 9PL, UK; (D.L.); (D.J.C.); (A.T.K.)
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9NT, UK
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
| | - Xiaoping Zhu
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (K.-L.L.); (T.C.); (A.J.)
- Wolfson Molecular Imaging Centre, University of Manchester, 27 Palatine Road, Manchester M20 3LJ, UK
| | - David J. Coope
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester M13 9PL, UK; (D.L.); (D.J.C.); (A.T.K.)
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9NT, UK
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
| | - Ibrahim Djoukhadar
- Department of Neuroradiology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9NT, UK;
| | - Andrew T. King
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester M13 9PL, UK; (D.L.); (D.J.C.); (A.T.K.)
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9NT, UK
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester M13 9PL, UK
| | - Timothy Cootes
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (K.-L.L.); (T.C.); (A.J.)
| | - Alan Jackson
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (K.-L.L.); (T.C.); (A.J.)
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18
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Hazari PP, Yadav SK, Kumar PK, Dhingra V, Rani N, Kumar R, Singh B, Mishra AK. Preclinical and Clinical Use of Indigenously Developed 99mTc-Diethylenetriaminepentaacetic Acid-Bis-Methionine: l-Type Amino Acid Transporter 1-Targeted Single Photon Emission Computed Tomography Radiotracer for Glioma Management. ACS Pharmacol Transl Sci 2023; 6:1233-1247. [PMID: 37705592 PMCID: PMC10496141 DOI: 10.1021/acsptsci.3c00091] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Indexed: 09/15/2023]
Abstract
A new era in tumor classification, diagnosis, and prognostic evaluation has begun as a consequence of recent developments in the molecular and genetic characterization of central nervous system tumors. In this newly emerging era, molecular imaging modalities are essential for preoperative diagnosis, surgical planning, targeted treatment, and post-therapy evaluation of gliomas. The radiotracers are able to identify brain tumors, distinguish between low- and high-grade lesions, confirm a patient's eligibility for theranostics, and assess post-radiation alterations. We previously synthesized and reported the novel l-type amino acid transporter 1 (LAT-1)-targeted amino acid derivative in light of the use of amino acid derivatives in imaging technologies. Further, we have developed a single vial ready to label Tc-lyophilized kit preparations of diethylenetriaminepentaacetic acid-bis-methionine [DTPA-bis(Met)], also referred to as methionine-diethylenetriaminepentaacetic acid-methionine (MDM) and evaluated its imaging potential in numerous clinical studies. This review summarizes our previous publications on 99mTc-DTPA-bis(Met) in different clinical studies such as detection of breast cancer, as a prognostic marker, in detection of recurrent/residual gliomas, for differentiation of recurrent/residual gliomas from radiation necrosis, and for comparison of 99mTc-DTPA-bis(Met) with 11C-L-methionine (11C-MET), with relevant literature on imaging modalities in glioma management.
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Affiliation(s)
- Puja Panwar Hazari
- Division of Cyclotron and Radiopharmaceutical Sciences, Institute of Nuclear Medicine and Allied Sciences, DRDO, Delhi- 110054, India
| | - Shiv Kumar Yadav
- Division of Cyclotron and Radiopharmaceutical Sciences, Institute of Nuclear Medicine and Allied Sciences, DRDO, Delhi- 110054, India
| | - Pardeep Kumar Kumar
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health & Neurosciences, Bangalore-560029, India
| | - Vandana Dhingra
- All India Institute of Medical Sciences, Rishikesh-249203, India
| | - Nisha Rani
- Division of Psychiatric Neuroimaging, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine 600 N. Wolfe Street, Phipps 300, Baltimore, Maryland 21287, United States
| | - Rakesh Kumar
- All India Institute of Medical Sciences, Delhi-110029, India
| | - Baljinder Singh
- Department of Nuclear Medicine, Postgraduate Institute of Medical Education and Research, Chandigarh-160012, India
| | - Anil K Mishra
- Division of Cyclotron and Radiopharmaceutical Sciences, Institute of Nuclear Medicine and Allied Sciences, DRDO, Delhi- 110054, India
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Alizadeh M, Broomand Lomer N, Azami M, Khalafi M, Shobeiri P, Arab Bafrani M, Sotoudeh H. Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme. Cancers (Basel) 2023; 15:4429. [PMID: 37760399 PMCID: PMC10526457 DOI: 10.3390/cancers15184429] [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: 07/11/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios. Preliminary published studies are promising about the role of radiomics in post-treatment glioma/GBM. However, this field faces significant challenges, including a lack of evidence-based solid data, scattering publication, heterogeneity of studies, and small sample sizes. The present review explores radiomics's capabilities in following patients with glioma/GBM status post-treatment and to differentiate tumor progression, recurrence, pseudoprogression, and radionecrosis.
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Affiliation(s)
- Mohammadreza Alizadeh
- Physiology Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
| | - Nima Broomand Lomer
- Faculty of Medicine, Guilan University of Medical Sciences, Rasht 41937-13111, Iran;
| | - Mobin Azami
- Student Research Committee, Kurdistan University of Medical Sciences, Sanandaj 66186-34683, Iran;
| | - Mohammad Khalafi
- Radiology Department, Tabriz University of Medical Sciences, Tabriz 51656-65931, Iran;
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Sciences, Tehran 14167-53955, Iran; (P.S.); (M.A.B.)
| | - Melika Arab Bafrani
- School of Medicine, Tehran University of Medical Sciences, Tehran 14167-53955, Iran; (P.S.); (M.A.B.)
| | - Houman Sotoudeh
- Department of Radiology and Neurology, Heersink School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL 35294, USA
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20
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Nafe R, Porto L, Samp PF, You SJ, Hattingen E. Adult-type and Pediatric-type Diffuse Gliomas : What the Neuroradiologist Should Know. Clin Neuroradiol 2023; 33:611-624. [PMID: 36941392 PMCID: PMC10449995 DOI: 10.1007/s00062-023-01277-z] [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: 11/25/2022] [Accepted: 02/03/2023] [Indexed: 03/22/2023]
Abstract
The classification of diffuse gliomas into the adult type and the pediatric type is the new basis for the diagnosis and clinical evaluation. The knowledge for the neuroradiologist should not remain limited to radiological aspects but should be based additionally on the current edition of the World Health Organization (WHO) classification of tumors of the central nervous system (CNS). This classification defines the 11 entities of diffuse gliomas, which are included in the 3 large groups of adult-type diffuse gliomas, pediatric-type diffuse low-grade gliomas, and pediatric-type diffuse high-grade gliomas. This article provides a detailed overview of important molecular, morphological, and clinical aspects for all 11 entities, such as typical genetic alterations, age distribution, variability of the tumor localization, variability of histopathological and radiological findings within each entity, as well as currently available statistical information on prognosis and outcome. Important differential diagnoses are also discussed.
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Affiliation(s)
- Reinhold Nafe
- Dept. Neuroradiology, Clinics of Johann Wolfgang-Goethe University, Schleusenweg 2-16, 60528, Frankfurt am Main, Germany.
| | - Luciana Porto
- Dept. Neuroradiology, Clinics of Johann Wolfgang-Goethe University, Schleusenweg 2-16, 60528, Frankfurt am Main, Germany
| | - Patrick-Felix Samp
- Dept. Neuroradiology, Clinics of Johann Wolfgang-Goethe University, Schleusenweg 2-16, 60528, Frankfurt am Main, Germany
| | - Se-Jong You
- Dept. Neuroradiology, Clinics of Johann Wolfgang-Goethe University, Schleusenweg 2-16, 60528, Frankfurt am Main, Germany
| | - Elke Hattingen
- Dept. Neuroradiology, Clinics of Johann Wolfgang-Goethe University, Schleusenweg 2-16, 60528, Frankfurt am Main, Germany
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21
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Sohn B, Park K, Ahn SS, Park YW, Choi SH, Kang SG, Kim SH, Chang JH, Lee SK. Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype. J Neurooncol 2023; 164:341-351. [PMID: 37689596 DOI: 10.1007/s11060-023-04435-y] [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: 07/24/2023] [Accepted: 08/23/2023] [Indexed: 09/11/2023]
Abstract
PURPOSE To develop and validate a dynamic contrast-enhanced (DCE) MRI-based radiomics model to predict epidermal growth factor receptor (EGFR) amplification in patients with glioblastoma, isocitrate dehydrogenase (IDH) wildtype. METHODS Patients with pathologically confirmed glioblastoma, IDH wildtype, from January 2015 to December 2020, with an EGFR amplification status, were included. Patients who did not undergo DCE or conventional brain MRI were excluded. Patients were categorized into training and test sets by a ratio of 7:3. DCE MRI data were used to generate volume transfer constant (Ktrans) and extracellular volume fraction (Ve) maps. Ktrans, Ve, and conventional MRI were then used to extract the radiomics features, from which the prediction models for EGFR amplification status were developed and validated. RESULTS A total of 190 patients (mean age, 59.9; male, 55.3%), divided into training (n = 133) and test (n = 57) sets, were enrolled. In the test set, the radiomics model using the Ktrans map exhibited the highest area under the receiver operating characteristic curve (AUROC), 0.80 (95% confidence interval [CI], 0.65-0.95). The AUROC for the Ve map-based and conventional MRI-based models were 0.74 (95% CI, 0.58-0.90) and 0.76 (95% CI, 0.61-0.91). CONCLUSION The DCE MRI-based radiomics model that predicts EGFR amplification in glioblastoma, IDH wildtype, was developed and validated. The MRI-based radiomics model using the Ktrans map has higher AUROC than conventional MRI.
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Affiliation(s)
- Beomseok Sohn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Kisung Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, South Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung Hong Choi
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
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22
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Veikutis V, Brazdziunas M, Keleras E, Basevicius A, Grib A, Skaudickas D, Lukosevicius S. Diagnostic Approaches to Adult-Type Diffuse Glial Tumors: Comparative Literature and Clinical Practice Study. Curr Oncol 2023; 30:7818-7835. [PMID: 37754483 PMCID: PMC10528153 DOI: 10.3390/curroncol30090568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/27/2023] [Accepted: 08/08/2023] [Indexed: 09/28/2023] Open
Abstract
Gliomas are the most frequent intrinsic central nervous system tumors. The new 2021 WHO Classification of Central Nervous System Tumors brought significant changes into the classification of gliomas, that underline the role of molecular diagnostics, with the adult-type diffuse glial tumors now identified primarily by their biomarkers rather than histology. The status of the isocitrate dehydrogenase (IDH) 1 or 2 describes tumors at their molecular level and together with the presence or absence of 1p/19q codeletion are the most important biomarkers used for the classification of adult-type diffuse glial tumors. In recent years terminology has also changed. IDH-mutant, as previously known, is diagnostically used as astrocytoma and IDH-wildtype is used as glioblastoma. A comprehensive understanding of these tumors not only gives patients a more proper treatment and better prognosis but also highlights new difficulties. MR imaging is of the utmost importance for diagnosing and supervising the response to treatment. By monitoring the tumor on followup exams better results can be achieved. Correlations are seen between tumor diagnostic and clinical manifestation and surgical administration, followup care, oncologic treatment, and outcomes. Minimal resection site use of functional imaging (fMRI) and diffusion tensor imaging (DTI) have become indispensable tools in invasive treatment. Perfusion imaging provides insightful information about the vascularity of the tumor, spectroscopy shows metabolic activity, and nuclear medicine imaging displays tumor metabolism. To accommodate better treatment the differentiation of pseudoprogression, pseudoresponse, or radiation necrosis is needed. In this report, we present a literature review of diagnostics of gliomas, the differences in their imaging features, and our radiology's departments accumulated experience concerning gliomas.
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Affiliation(s)
- Vincentas Veikutis
- Medical Academy, Lithuanian University of Health Sciences, LT50161 Kaunas, Lithuania; (M.B.); (E.K.); (A.B.); (D.S.); (S.L.)
| | - Mindaugas Brazdziunas
- Medical Academy, Lithuanian University of Health Sciences, LT50161 Kaunas, Lithuania; (M.B.); (E.K.); (A.B.); (D.S.); (S.L.)
- Faculty of Medicine, Kaunas University of Applied Sciences, LT44162 Kaunas, Lithuania
| | - Evaldas Keleras
- Medical Academy, Lithuanian University of Health Sciences, LT50161 Kaunas, Lithuania; (M.B.); (E.K.); (A.B.); (D.S.); (S.L.)
| | - Algidas Basevicius
- Medical Academy, Lithuanian University of Health Sciences, LT50161 Kaunas, Lithuania; (M.B.); (E.K.); (A.B.); (D.S.); (S.L.)
| | - Andrei Grib
- Department of Internal Medicine, Nicolae Testemitanu State University of Medicine and Pharmacy, MD2004 Chisinau, Moldova;
| | - Darijus Skaudickas
- Medical Academy, Lithuanian University of Health Sciences, LT50161 Kaunas, Lithuania; (M.B.); (E.K.); (A.B.); (D.S.); (S.L.)
| | - Saulius Lukosevicius
- Medical Academy, Lithuanian University of Health Sciences, LT50161 Kaunas, Lithuania; (M.B.); (E.K.); (A.B.); (D.S.); (S.L.)
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23
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Autry AW, Vaziri S, LaFontaine M, Gordon JW, Chen HY, Kim Y, Villanueva-Meyer JE, Molinaro A, Clarke JL, Oberheim Bush NA, Xu D, Lupo JM, Larson PEZ, Vigneron DB, Chang SM, Li Y. Multi-parametric hyperpolarized 13C/ 1H imaging reveals Warburg-related metabolic dysfunction and associated regional heterogeneity in high-grade human gliomas. Neuroimage Clin 2023; 39:103501. [PMID: 37611371 PMCID: PMC10470324 DOI: 10.1016/j.nicl.2023.103501] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/29/2023] [Accepted: 08/16/2023] [Indexed: 08/25/2023]
Abstract
BACKGROUND Dynamic hyperpolarized (HP)-13C MRI has enabled real-time, non-invasive assessment of Warburg-related metabolic dysregulation in glioma using a [1-13C]pyruvate tracer that undergoes conversion to [1-13C]lactate and [13C]bicarbonate. Using a multi-parametric 1H/HP-13C imaging approach, we investigated dynamic and steady-state metabolism, together with physiological parameters, in high-grade gliomas to characterize active tumor. METHODS Multi-parametric 1H/HP-13C MRI data were acquired from fifteen patients with progressive/treatment-naïve glioblastoma [prog/TN GBM, IDH-wildtype (n = 11)], progressive astrocytoma, IDH-mutant, grade 4 (G4AIDH+, n = 2) and GBM manifesting treatment effects (n = 2). Voxel-wise regional analysis of the cohort with prog/TN GBM assessed imaging heterogeneity across contrast-enhancing/non-enhancing lesions (CEL/NEL) and normal-appearing white matter (NAWM) using a mixed effects model. To enable cross-nucleus parameter association, normalized perfusion, diffusion, and dynamic/steady-state (HP-13C/spectroscopic) metabolic data were collectively examined at the 13C resolution. Prog/TN GBM were similarly compared against progressive G4AIDH+ and treatment effects. RESULTS Regional analysis of Prog/TN GBM metabolism revealed statistically significant heterogeneity in 1H choline-to-N-acetylaspartate index (CNI)max, [1-13C]lactate, modified [1-13C]lactate-to-[1-13C]pyruvate ratio (CELval > NELval > NAWMval); [1-13C]lactate-to-[13C]bicarbonate ratio (CELval > NELval/NAWMval); and 1H-lactate (CELval/NELval > NAWMundetected). Significant associations were found between normalized perfusion (cerebral blood volume, nCBV; peak height, nPH) and levels of [1-13C]pyruvate and [1-13C]lactate, as well as between CNImax and levels of [1-13C]pyruvate, [1-13C]lactate and modified ratio. GBM, by comparison to G4AIDH+, displayed lower perfusion %-recovery and modeled rate constants for [1-13C]pyruvate-to-[1-13C]lactate conversion (kPL), and higher 1H-lactate and [1-13C]pyruvate levels, while having higher nCBV, %-recovery, kPL, [1-13C]pyruvate-to-[1-13C]lactate and modified ratios relative to treatment effects. CONCLUSIONS GBM consistently displayed aberrant, Warburg-related metabolism and regional heterogeneity detectable by novel HP-13C/1H imaging techniques.
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Affiliation(s)
- Adam W Autry
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Sana Vaziri
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Marisa LaFontaine
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Jeremy W Gordon
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Hsin-Yu Chen
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Yaewon Kim
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Javier E Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA; Department of Neurological Surgery, University of California, San Francisco, USA
| | - Annette Molinaro
- Department of Neurological Surgery, University of California, San Francisco, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
| | - Jennifer L Clarke
- Department of Neurological Surgery, University of California, San Francisco, USA; Department of Neurology, University of California, San Francisco, USA
| | - Nancy Ann Oberheim Bush
- Department of Neurological Surgery, University of California, San Francisco, USA; Department of Neurology, University of California, San Francisco, USA
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Janine M Lupo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Daniel B Vigneron
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA; Department of Bioengineering and Therapeutic Science, University of California, San Francisco, USA
| | - Susan M Chang
- Department of Neurological Surgery, University of California, San Francisco, USA
| | - Yan Li
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
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24
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Mannam SS, Nwagwu CD, Sumner C, Weinberg BD, Hoang KB. Perfusion-Weighted Imaging: The Use of a Novel Perfusion Scoring Criteria to Improve the Assessment of Brain Tumor Recurrence versus Treatment Effects. Tomography 2023; 9:1062-1070. [PMID: 37368539 DOI: 10.3390/tomography9030087] [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: 04/20/2023] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 06/29/2023] Open
Abstract
INTRODUCTION Imaging surveillance of contrast-enhancing lesions after the treatment of malignant brain tumors with radiation is plagued by an inability to reliably distinguish between tumor recurrence and treatment effects. Magnetic resonance perfusion-weighted imaging (PWI)-among other advanced brain tumor imaging modalities-is a useful adjunctive tool for distinguishing between these two entities but can be clinically unreliable, leading to the need for tissue sampling to confirm diagnosis. This may be partially because clinical PWI interpretation is non-standardized and no grading criteria are used for assessment, leading to interpretation discrepancies. This variance in the interpretation of PWI and its subsequent effect on the predictive value has not been studied. Our objective is to propose structured perfusion scoring criteria and determine their effect on the clinical value of PWI. METHODS Patients treated at a single institution between 2012 and 2022 who had prior irradiated malignant brain tumors and subsequent progression of contrast-enhancing lesions determined by PWI were retrospectively studied from CTORE (CNS Tumor Outcomes Registry at Emory). PWI was given two separate qualitative scores (high, intermediate, or low perfusion). The first (control) was assigned by a neuroradiologist in the radiology report in the course of interpretation with no additional instruction. The second (experimental) was assigned by a neuroradiologist with additional experience in brain tumor interpretation using a novel perfusion scoring rubric. The perfusion assessments were divided into three categories, each directly corresponding to the pathology-reported classification of residual tumor content. The interpretation accuracy in predicting the true tumor percentage, our primary outcome, was assessed through Chi-squared analysis, and inter-rater reliability was assessed using Cohen's Kappa. RESULTS Our 55-patient cohort had a mean age of 53.5 ± 12.2 years. The percentage agreement between the two scores was 57.4% (κ: 0.271). Upon conducting the Chi-squared analysis, we found an association with the experimental group reads (p-value: 0.014) but no association with the control group reads (p-value: 0.734) in predicting tumor recurrence versus treatment effects. CONCLUSIONS With our study, we showed that having an objective perfusion scoring rubric aids in improved PWI interpretation. Although PWI is a powerful tool for CNS lesion diagnosis, methodological radiology evaluation greatly improves the accurate assessment and characterization of tumor recurrence versus treatment effects by all neuroradiologists. Further work should focus on standardizing and validating scoring rubrics for PWI evaluation in tumor patients to improve diagnostic accuracy.
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Affiliation(s)
- Sneha Sai Mannam
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chibueze D Nwagwu
- Department of Neurosurgery, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Christina Sumner
- Department of Radiology and Imaging Sciences, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Brent D Weinberg
- Department of Radiology and Imaging Sciences, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Kimberly B Hoang
- Department of Neurosurgery, School of Medicine, Emory University, Atlanta, GA 30322, USA
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25
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Lehmann PM, Seidemo A, Andersen M, Xu X, Li X, Yadav NN, Wirestam R, Liebig P, Testud F, Sundgren P, van Zijl PCM, Knutsson L. A numerical human brain phantom for dynamic glucose-enhanced (DGE) MRI: On the influence of head motion at 3T. Magn Reson Med 2023; 89:1871-1887. [PMID: 36579955 PMCID: PMC9992166 DOI: 10.1002/mrm.29563] [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: 02/23/2022] [Revised: 11/09/2022] [Accepted: 12/07/2022] [Indexed: 12/30/2022]
Abstract
PURPOSE Dynamic glucose-enhanced (DGE) MRI relates to a group of exchange-based MRI techniques where the uptake of glucose analogues is studied dynamically. However, motion artifacts can be mistaken for true DGE effects, while motion correction may alter true signal effects. The aim was to design a numerical human brain phantom to simulate a realistic DGE MRI protocol at 3T that can be used to assess the influence of head movement on the signal before and after retrospective motion correction. METHODS MPRAGE data from a tumor patient were used to simulate dynamic Z-spectra under the influence of motion. The DGE responses for different tissue types were simulated, creating a ground truth. Rigid head movement patterns were applied as well as physiological dilatation and pulsation of the lateral ventricles and head-motion-induced B0 -changes in presence of first-order shimming. The effect of retrospective motion correction was evaluated. RESULTS Motion artifacts similar to those previously reported for in vivo DGE data could be reproduced. Head movement of 1 mm translation and 1.5 degrees rotation led to a pseudo-DGE effect on the order of 1% signal change. B0 effects due to head motion altered DGE changes due to a shift in the water saturation spectrum. Pseudo DGE effects were partly reduced or enhanced by rigid motion correction depending on tissue location. CONCLUSION DGE MRI studies can be corrupted by motion artifacts. Designing post-processing methods using retrospective motion correction including B0 correction will be crucial for clinical implementation. The proposed phantom should be useful for evaluation and optimization of such techniques.
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Affiliation(s)
- Patrick M Lehmann
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Anina Seidemo
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Mads Andersen
- Philips Healthcare, Copenhagen, Denmark
- Lund University Bioimaging Centre, Lund University, Lund, Sweden
| | - Xiang Xu
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins, University School of Medicine, Baltimore, Maryland, USA
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins, University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Nirbhay N Yadav
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins, University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Ronnie Wirestam
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | | | | | - Pia Sundgren
- Lund University Bioimaging Centre, Lund University, Lund, Sweden
- Department of Radiology, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Peter C M van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins, University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins, University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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26
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Alsulami TA, Hyare H, Thomas DL, Golay X. The value of arterial spin labelling (ASL) perfusion MRI in the assessment of post-treatment progression in adult glioma: A systematic review and meta-analysis. Neurooncol Adv 2023; 5:vdad122. [PMID: 37841694 PMCID: PMC10576519 DOI: 10.1093/noajnl/vdad122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023] Open
Abstract
Background The distinction between viable tumor and therapy-induced changes is crucial for the clinical management of patients with gliomas. This study aims to quantitatively assess the efficacy of arterial spin labeling (ASL) biomarkers, including relative cerebral blood flow (rCBF) and absolute cerebral blood flow (CBF), for the discrimination of progressive disease (PD) and treatment-related effects. Methods Eight articles were included in the synthesis after searching the literature systematically. Data have been extracted and a meta-analysis using the random-effect model was subsequently carried out. Diagnostic accuracy assessment was also performed. Results This study revealed that there is a significant difference in perfusion measurements between groups with PD and therapy-induced changes. The rCBF yielded a standardized mean difference (SMD) of 1.25 [95% CI 0.75, 1.75] (p < .00001). The maximum perfusion indices (rCBFmax and CBFmax) both showed equivalent discriminatory ability, with SMD of 1.35 [95% CI 0.78, 1.91] (p < .00001) and 1.56 [95% CI 0.79, 2.33] (p < .0001), respectively. Similarly, accuracy estimates were comparable among ASL-derived metrices. Pooled sensitivities [95% CI] were 0.85 [0.67, 0.94], 0.88 [0.71, 0.96], and 0.93 [0.73, 0.98], and pooled specificities [95% CI] were 0.83 [0.71, 0.91], 0.83 [0.67, 0.92], 0.84 [0.67, 0.93], for rCBF, rCBFmax and CBFmax, respectively. Corresponding HSROC area under curve (AUC) [95% CI] were 0.90 [0.87, 0.92], 0.92 [0.89, 0.94], and 0.93 [0.90, 0.95]. Conclusion These results suggest that ASL quantitative biomarkers, particularly rCBFmax and CBFmax, have the potential to discriminate between glioma progression and therapy-induced changes.
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Affiliation(s)
- Tamadur A Alsulami
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, King Abdulaziz University (KAU), Jeddah, Saudi Arabia
| | - Harpreet Hyare
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- University College London Hospitals NHS Foundation Trust, London, UK
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Xavier Golay
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Trust, London, UK
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27
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Schmitz-Abecassis B, Dirven L, Jiang J, Keller JA, Croese RJI, van Dorth D, Ghaznawi R, Kant IMJ, Taphoorn MJB, van Osch MJP, Koekkoek JAF, de Bresser J. MRI phenotypes of glioblastomas early after treatment are suggestive of overall patient survival. Neurooncol Adv 2023; 5:vdad133. [PMID: 37908765 PMCID: PMC10613962 DOI: 10.1093/noajnl/vdad133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023] Open
Abstract
Background Distinguishing true tumor progression (TP) from treatment-induced abnormalities (eg, pseudo-progression (PP) after radiotherapy) on conventional MRI scans remains challenging in patients with a glioblastoma. We aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall survival time. Methods Structural and perfusion MR images of 67 patients at 3 months post-radiotherapy were visually scored by a neuroradiologist. In total 23 parameters were predefined and used for hierarchical clustering analysis. Progression status was assessed based on the clinical course of each patient 9 months after radiotherapy (or latest available). Multivariable Cox regression models were used to determine the association between the phenotypes, recurrence rate, and overall survival. Results We established 4 subgroups with significantly different tumor MRI characteristics, representing distinct MRI phenotypes of glioblastomas: TP and PP rates did not differ significantly between subgroups. Regression analysis showed that patients in subgroup 1 (characterized by having mostly small and ellipsoid nodular enhancing lesions with some hyper-perfusion) had a significant association with increased mortality at 9 months (HR: 2.6 (CI: 1.1-6.3); P = .03) with a median survival time of 13 months (compared to 22 months of subgroup 2). Conclusions Our study suggests that distinct MRI phenotypes of glioblastomas at 3 months post-radiotherapy can be indicative of overall survival, but does not aid in differentiating TP from PP. The early prognostic information our method provides might in the future be informative for prognostication of glioblastoma patients.
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Affiliation(s)
- Bárbara Schmitz-Abecassis
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Medical Delta, South-Holland, The Netherlands
| | - Linda Dirven
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Neurology, Haaglanden Medical Center, The Hague, The Netherlands
| | - Janey Jiang
- Department of Radiology, HagaZiekenhuis, The Hague, The Netherlands
| | - Jasmin A Keller
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Robert J I Croese
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Neurology, Haaglanden Medical Center, The Hague, The Netherlands
| | - Daniëlle van Dorth
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rashid Ghaznawi
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ilse M J Kant
- Clinical Artificial Intelligence Implementation and Research Lab (CAIRELab) and Department of Information Technology & Digital Innovation, Leiden University Medical Center, Leiden, The Netherlands
- Department of Digital Health, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martin J B Taphoorn
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Neurology, Haaglanden Medical Center, The Hague, The Netherlands
| | | | - Johan A F Koekkoek
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Neurology, Haaglanden Medical Center, The Hague, The Netherlands
| | - Jeroen de Bresser
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
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Sahli H, Ben Slama A, Zeraii A, Labidi S, Sayadi M. ResNet-SVM: Fusion based glioblastoma tumor segmentation and classification. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:27-48. [PMID: 36278391 DOI: 10.3233/xst-221240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Computerized segmentation of brain tumor based on magnetic resonance imaging (MRI) data presents an important challenging act in computer vision. In image segmentation, numerous studies have explored the feasibility and advantages of employing deep neural network methods to automatically detect and segment brain tumors depicting on MRI. For training the deeper neural network, the procedure usually requires extensive computational power and it is also very time-consuming due to the complexity and the gradient diffusion difficulty. In order to address and help solve this challenge, we in this study present an automatic approach for Glioblastoma brain tumor segmentation based on deep Residual Learning Network (ResNet) to get over the gradient problem of deep Convolutional Neural Networks (CNNs). Using the extra layers added to a deep neural network, ResNet algorithm can effectively improve the accuracy and the performance, which is useful in solving complex problems with a much rapid training process. An additional method is then proposed to fully automatically classify different brain tumor categories (necrosis, edema, and enhancing regions). Results confirm that the proposed fusion method (ResNet-SVM) has an increased classification results of accuracy (AC = 89.36%), specificity (SP = 92.52%) and precision (PR = 90.12%) using 260 MRI data for the training and 112 data used for testing and validation of Glioblastoma tumor cases. Compared to the state-of-the art methods, the proposed scheme provides a higher performance by identifying Glioblastoma tumor type.
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Affiliation(s)
- Hanene Sahli
- Laboratory of Signal Image and Energy Mastery, National Higher Engineering School of Tunis, University of Tunis, Tunis, Tunisia
| | - Amine Ben Slama
- Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Abderrazek Zeraii
- Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Salam Labidi
- Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Mounir Sayadi
- Laboratory of Signal Image and Energy Mastery, National Higher Engineering School of Tunis, University of Tunis, Tunis, Tunisia
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Farhadi F, Rajagopal JR, Veziroglu EM, Abdollahi H, Shiri I, Nikpanah M, Morris MA, Zaidi H, Rahmim A, Saboury B. Multi-Scale Temporal Imaging: From Micro- and Meso- to Macro-scale-time Nuclear Medicine. PET Clin 2023; 18:135-148. [DOI: 10.1016/j.cpet.2022.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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30
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Teunissen WHT, Govaerts CW, Kramer MCA, Labrecque JA, Smits M, Dirven L, van der Hoorn A. Diagnostic accuracy of MRI techniques for treatment response evaluation in patients with brain metastasis: A systematic review and meta-analysis. Radiother Oncol 2022; 177:121-133. [PMID: 36377093 DOI: 10.1016/j.radonc.2022.10.026] [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: 05/11/2022] [Revised: 10/11/2022] [Accepted: 10/21/2022] [Indexed: 11/12/2022]
Abstract
BACKGROUND Treatment response assessment in patients with brain metastasis uses contrast enhanced T1-weighted MRI. Advanced MRI techniques have been studied, but the diagnostic accuracy is not well known. Therefore, we performed a metaanalysis to assess the diagnostic accuracy of the currently available MRI techniques for treatment response. METHODS A systematic literature search was done. Study selection and data extraction were done by two authors independently. Meta-analysis was performed using a bivariate random effects model. An independent cohort was used for DSC perfusion external validation of diagnostic accuracy. RESULTS Anatomical MRI (16 studies, 726 lesions) showed a pooled sensitivity of 79% and a specificity of 76%. DCE perfusion (4 studies, 114 lesions) showed a pooled sensitivity of 74% and a specificity of 92%. DSC perfusion (12 studies, 418 lesions) showed a pooled sensitivity was 83% with a specificity of 78%. Diffusion weighted imaging (7 studies, 288 lesions) showed a pooled sensitivity of 67% and a specificity of 79%. MRS (4 studies, 54 lesions) showed a pooled sensitivity of 80% and a specificity of 78%. Combined techniques (6 studies, 375 lesions) showed a pooled sensitivity of 84% and a specificity of 88%. External validation of DSC showed a lower sensitivity and a higher specificity for the reported cut-off values included in this metaanalysis. CONCLUSION A combination of techniques shows the highest diagnostic accuracy differentiating tumor progression from treatment induced abnormalities. External validation of imaging results is important to better define the reliability of imaging results with the different techniques.
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Affiliation(s)
- Wouter H T Teunissen
- Erasmus MC, department of Radiology and Nuclear Medicine, Rotterdam, the Netherlands; Brain Tumor Centre, Erasmus MC Cancer Institute, Rotterdam, the Netherlands; Medical Delta, Delft, The Netherlands
| | - Chris W Govaerts
- University Medical Center Groningen, Medical imaging center, department of Radiology, Groningen, the Netherlands
| | - Miranda C A Kramer
- University Medical Center Groningen, department of Radiotherapy, Groningen, the Netherlands
| | - Jeremy A Labrecque
- Erasmus MC, Netherlands Institute for Health Science (NIHES), Rotterdam, the Netherlands
| | - Marion Smits
- Erasmus MC, department of Radiology and Nuclear Medicine, Rotterdam, the Netherlands; Brain Tumor Centre, Erasmus MC Cancer Institute, Rotterdam, the Netherlands; Medical Delta, Delft, The Netherlands
| | - Linda Dirven
- Leiden University Medical Center, department of Neurology, Leiden, the Netherlands; Haaglanden Medical Center, department of Neurology, The Hague, the Netherlands
| | - Anouk van der Hoorn
- University Medical Center Groningen, Medical imaging center, department of Radiology, Groningen, the Netherlands.
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Abstract
Glioblastoma is the most aggressive primary brain tumor with a poor prognosis. The 2021 WHO CNS5 classification has further stressed the importance of molecular signatures in diagnosis although therapeutic breakthroughs are still lacking. In this review article, updates on the current and novel therapies in IDH-wildtype GBM will be discussed.
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Affiliation(s)
- Jawad M Melhem
- Division of Neurology, Department of Medicine, Faculty of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Mary Jane Lim-Fat
- Division of Neurology, Department of Medicine, Faculty of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - James R Perry
- Division of Neurology, Department of Medicine, Faculty of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.
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Qin D, Yang G, Jing H, Tan Y, Zhao B, Zhang H. Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma. Cancers (Basel) 2022; 14:cancers14153771. [PMID: 35954435 PMCID: PMC9367286 DOI: 10.3390/cancers14153771] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Glioma is the most common primary malignant tumor of the adult central nervous system. Despite aggressive multimodal treatment, its prognosis remains poor. During follow-up, it remains challenging to distinguish treatment-related changes from tumor progression in treated patients with gliomas due to both share clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions). The early effective identification of tumor progression and treatment-related changes is of great significance for the prognosis and treatment of gliomas. We believe that advanced neuroimaging techniques can provide additional information for distinguishing both at an early stage. In this article, we focus on the research of magnetic resonance imaging technology and artificial intelligence in tumor progression and treatment-related changes. Finally, it provides new ideas and insights for clinical diagnosis. Abstract As the most common neuro-epithelial tumors of the central nervous system in adults, gliomas are highly malignant and easy to recurrence, with a dismal prognosis. Imaging studies are indispensable for tracking tumor progression (TP) or treatment-related changes (TRCs). During follow-up, distinguishing TRCs from TP in treated patients with gliomas remains challenging as both share similar clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions) and fulfill criteria for progression. Thus, the early identification of TP and TRCs is of great significance for determining the prognosis and treatment. Histopathological biopsy is currently the gold standard for TP and TRC diagnosis. However, the invasive nature of this technique limits its clinical application. Advanced imaging methods (e.g., diffusion magnetic resonance imaging (MRI), perfusion MRI, magnetic resonance spectroscopy (MRS), positron emission tomography (PET), amide proton transfer (APT) and artificial intelligence (AI)) provide a non-invasive and feasible technical means for identifying of TP and TRCs at an early stage, which have recently become research hotspots. This paper reviews the current research on using the abovementioned advanced imaging methods to identify TP and TRCs of gliomas. First, the review focuses on the pathological changes of the two entities to establish a theoretical basis for imaging identification. Then, it elaborates on the application of different imaging techniques and AI in identifying the two entities. Finally, the current challenges and future prospects of these techniques and methods are discussed.
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Affiliation(s)
- Danlei Qin
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School, Hospital of Stomatology, Taiyuan 030001, China
| | - Guoqiang Yang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
| | - Hui Jing
- Department of MRI, The Six Hospital, Shanxi Medical University, Taiyuan 030008, China;
| | - Yan Tan
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
| | - Bin Zhao
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School, Hospital of Stomatology, Taiyuan 030001, China
- Correspondence: (B.Z.); (H.Z.)
| | - Hui Zhang
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
- Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province, Taiyuan 030001, China
- Correspondence: (B.Z.); (H.Z.)
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Raswoli M, Tsang DS, Zadeh G, Gao AF, Shultz DB. Malignant Mimics of Trigeminal Schwannoma. Adv Radiat Oncol 2022; 8:101056. [PMID: 36176356 PMCID: PMC9513213 DOI: 10.1016/j.adro.2022.101056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 08/11/2022] [Indexed: 11/25/2022] Open
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Hormuth DA, Farhat M, Christenson C, Curl B, Chad Quarles C, Chung C, Yankeelov TE. Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 2022; 187:114367. [PMID: 35654212 PMCID: PMC11165420 DOI: 10.1016/j.addr.2022.114367] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/25/2022] [Accepted: 05/25/2022] [Indexed: 11/01/2022]
Abstract
Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.
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Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Maguy Farhat
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Chase Christenson
- Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Brandon Curl
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - C Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Caroline Chung
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77230, USA
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Liquid Biopsy in Glioblastoma. Cancers (Basel) 2022; 14:cancers14143394. [PMID: 35884454 PMCID: PMC9323318 DOI: 10.3390/cancers14143394] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/10/2022] [Accepted: 07/11/2022] [Indexed: 12/27/2022] Open
Abstract
Simple Summary Glioblastoma is the most common and malignant primary brain tumor. Despite intensive research for new treatments, the survival of patients has not significantly improved in recent decades. Currently, glioblastoma is mainly diagnosed by neuroimaging techniques followed by histopathological and molecular analysis of the resected or biopsied tissue. Both imaging and tissue-based methods have, despite their advantages, some important limitations highlighting the necessity for alternative techniques such as liquid biopsy. It appears as an attractive and non-invasive alternative to support the diagnosis and the follow-up of patients with glioblastoma and to identify early recurrence. Liquid biopsy, primarily through blood tests, involves the detection and quantification of tumoral content released by tumors into the biofluids. The aim of the present review is to discuss the biological bases, the advantages, and the disadvantages of the most important circulating biomarkers so far proposed for glioblastoma. Abstract Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Despite recent advances in therapy modalities, the overall survival of GBM patients remains poor. GBM diagnosis relies on neuroimaging techniques. However, confirmation via histopathological and molecular analysis is necessary. Given the intrinsic limitations of such techniques, liquid biopsy (mainly via blood samples) emerged as a non-invasive and easy-to-implement alternative that could aid in both the diagnosis and the follow-up of GBM patients. Cancer cells release tumoral content into the bloodstream, such as circulating tumor DNA, circulating microRNAs, circulating tumor cells, extracellular vesicles, or circulating nucleosomes: all these could serve as a marker of GBM. In this narrative review, we discuss the current knowledge, the advantages, and the disadvantages of each circulating biomarker so far proposed.
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36
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Withofs N, Kumar R, Alavi A, Hustinx R. Facts and Fictions About [ 18F]FDG versus Other Tracers in Managing Patients with Brain Tumors: It Is Time to Rectify the Ongoing Misconceptions. PET Clin 2022; 17:327-342. [PMID: 35717096 DOI: 10.1016/j.cpet.2022.03.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
MRI is the first-choice imaging technique for brain tumors. Positron emission tomography can be combined together with multiparametric MRI to increase diagnostic confidence. Radiolabeled amino acids have gained wide clinical acceptance. The reported pooled specificity of [18F]FDG positron emission tomography is high and [18F]FDG might still be the first-choice positron emission tomography tracer in cases of World Health Organization grade 3 to 4 gliomas or [18F]FDG-avid tumors, avoiding the use of more expensive and less available radiolabeled amino acids. The present review discusses the additional value of positron emission tomography with a focus on [18F]FDG and radiolabeled amino acids.
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Affiliation(s)
- Nadia Withofs
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liege, Quartier Hopital, Avenue de l'hopital, 1, Liege 1 4000, Belgium; GIGA-CRC in vivo imaging, University of Liege, GIGA CHU - B34 Quartier Hôpital Avenue de l'Hôpital,11, 4000 Liège, Belgium.
| | - Rakesh Kumar
- Diagnostic Nuclear Medicine Division, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Abass Alavi
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liege, Quartier Hopital, Avenue de l'hopital, 1, Liege 1 4000, Belgium; GIGA-CRC in vivo imaging, University of Liege, GIGA CHU - B34 Quartier Hôpital Avenue de l'Hôpital,11, 4000 Liège, Belgium
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Maiter A, Butteriss D, English P, Lewis J, Hassani A, Bhatnagar P. Assessing the diagnostic accuracy and interobserver agreement of MRI perfusion in differentiating disease progression and pseudoprogression following treatment for glioblastoma in a tertiary UK centre. Clin Radiol 2022; 77:e568-e575. [DOI: 10.1016/j.crad.2022.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/12/2022] [Indexed: 11/03/2022]
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Pierce C, Ginat DT. Postsurgical and Postradiation Findings in the Head and Neck Imaging. Neuroimaging Clin N Am 2022; 32:299-313. [DOI: 10.1016/j.nic.2022.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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39
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Flies CM, van Leuken KH, Voorde MT, Verhoeff JJC, De Vos FYF, Seute T, Robe PA, Witkamp TD, Hendrikse J, Dankbaar JW, Snijders TJ. Conventional MRI Criteria to Differentiate Progressive Disease from Treatment-Induced Effects in High-Grade (WHO Grade 3-4) Gliomas. Neurology 2022; 99:e77-e88. [PMID: 35437259 PMCID: PMC9259090 DOI: 10.1212/wnl.0000000000200359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 02/22/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Post-treatment radiological deterioration of patients with an irradiated high-grade (WHO grade 3-4) glioma (HGG) may be the result of true progressive disease (PD) or treatment-induced effects (TIE). Differentiation between these entities is of great importance, but remains a diagnostic challenge. This study assesses the diagnostic value of conventional MRI characteristics to differentiate PD from TIE in HGGs. MATERIAL AND METHODS In this single-centre, retrospective, consecutive cohort study, we included adults with a HGG, who were treated with (chemo-)radiotherapy and subsequently developed a new or increasing contrast-enhancing lesion on conventional follow-up MRI. TIE and PD were defined radiologically as stable/decreased for ≥6 weeks or RANO-progression, and histologically as TIE without viable tumour or PD. Two neuroradiologists assessed twenty-one preselected MRI characteristics of the progressive lesions. The statistical analysis included logistic regression to develop a) a full multivariable model b) a diagnostic model with model reduction, and a Cohen's Kappa interrater reliability (IRR) coefficient. RESULTS 210 patients (median age=61, IQR=54-68, 189 males) with 284 lesions were included, of which 141 (50%) had PD. Median time to PD was 2 (0.7-6.1) and to TIE 0.9 (0.7-3.5) months after radiotherapy. After multivariable modelling and model reduction, the following determinants prevailed: Radiation dose (Odds ratio (OR)=0.68, 95%-CI=0.49-0.93), longer time to progression (TTP, OR=3.56, 95%-CI=1.84-6.88), marginal enhancement (OR=2.04, 95%-CI=1.09-3.83), soap bubble enhancement (OR=2.63, 95%-CI=1.39-4.98) and isointense apparent diffusion coefficient (ADC)-signal (OR=2.11, 95%-CI=1.05-4.24). ORs>1 indicate higher odds of PD. The Hosmer&Lemeshow test showed good calibration (p=0.947) and the area under the ROC-curve was 0.722 (95%-CI=0.66-0.78). In the glioblastoma subgroup, TTP, marginal enhancement and ADC-signal were significant. IRR analysis between neuroradiologists revealed moderate to near-perfect agreement for the predictive items, but poor agreement for others. DISCUSSION Several characteristics from conventional MRI are significant predictors for the discrimination between PD and TIE. However, IRR was variable. Conventional MRI characteristics from this study should be incorporated into a multimodal diagnostic model with advanced imaging techniques. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that in patients with irradiated HGGs, radiation dose, longer time to progression, marginal enhancement, soap bubble enhancement and isointense apparent ADC-signal distinguish PD from TIE.
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Affiliation(s)
- Christina M Flies
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Karlijn H van Leuken
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands.,Stichting Beroepsopleiding Huisarts, the Netherlands
| | - Marlies Ten Voorde
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands.,Mission of the Netherlands Reformed Congregations, in Guinea (Conakry)
| | - Joost J C Verhoeff
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Filip Y F De Vos
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tatjana Seute
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Pierre A Robe
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Theodoor D Witkamp
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jeroen Hendrikse
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jan Willem Dankbaar
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Tom J Snijders
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
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Chougule T, Gupta RK, Saini J, Agrawal S, Gupta M, Vakharia N, Singh A, Patir R, Vaishya S, Ingalhalikar M. Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4647. [PMID: 34766380 DOI: 10.1002/nbm.4647] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 10/11/2021] [Accepted: 10/12/2021] [Indexed: 06/13/2023]
Abstract
Glioblastoma is a highly infiltrative neoplasm with a high propensity of recurrence. The location of recurrence usually cannot be anticipated and depends on various factors, including the surgical resection margins. Currently, radiation planning utilizes the hyperintense signal from T2-FLAIR MRI and is delivered to a limited area defined by standardized guidelines. To this end, noninvasive early prediction and delineation of recurrence can aid in tailored targeted therapy, which may potentially delay the relapse, consequently improving overall survival. In this work, we hypothesize that radiomics-based phenotypic quantifiers may support the detection of recurrence before it is visualized on multimodal MRI. We employ retrospective longitudinal data from 29 subjects with a varying number of time points (three to 13) that includes glioblastoma recurrence. Voxelwise textural and intensity features are computed from multimodal MRI (T1-contrast enhanced [T1CE], FLAIR, and apparent diffusion coefficient), primarily to gain insights into longitudinal radiomic changes from preoperative MRI to recurrence and subsequently to predict the region of relapse from 143 ± 42 days before recurrence using machine learning. T1CE MRI first-order and gray-level co-occurrence matrix features are crucial in detecting local recurrence, while multimodal gray-level difference matrix and first-order features are highly predictive of the distant relapse, with a voxelwise test accuracy of 80.1% for distant recurrence and 71.4% for local recurrence. In summary, our work exemplifies a step forward in predicting glioblastoma recurrence using radiomics-based phenotypic changes that may potentially serve as MR-based biomarkers for customized therapeutic intervention.
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Affiliation(s)
- Tanay Chougule
- Symbiosis Centre for Medical Image Analysis, Symbiosis International University, Pune, India
| | - Rakesh K Gupta
- Radiology, Fortis Hospital to Fortis Memorial Research Institute, Gurgaon, India
| | - Jitender Saini
- Department of Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Shaleen Agrawal
- Radiation Oncology, Fortis Hospital to Fortis Memorial Research Institute, Gurgaon, India
| | - Mamta Gupta
- Radiology, Fortis Hospital to Fortis Memorial Research Institute, Gurgaon, India
| | - Nirvi Vakharia
- Symbiosis Centre for Medical Image Analysis, Symbiosis International University, Pune, India
| | - Anup Singh
- Department of Biomedical Engineering, Indian Institute of Technology, Delhi, India
| | - Rana Patir
- Radiation Oncology, Fortis Hospital to Fortis Memorial Research Institute, Gurgaon, India
| | - Sandeep Vaishya
- Radiation Oncology, Fortis Hospital to Fortis Memorial Research Institute, Gurgaon, India
| | - Madhura Ingalhalikar
- Symbiosis Centre for Medical Image Analysis, Symbiosis International University, Pune, India
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Gaudino S, Marziali G, Giordano C, Gigli R, Varcasia G, Magnani F, Chiesa S, Balducci M, Costantini AM, Della Pepa GM, Olivi A, Russo R, Colosimo C. Regorafenib in Glioblastoma Recurrence: How to Deal With MR Imaging Treatments Changes. FRONTIERS IN RADIOLOGY 2022; 1:790456. [PMID: 37492166 PMCID: PMC10365006 DOI: 10.3389/fradi.2021.790456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 12/29/2021] [Indexed: 07/27/2023]
Abstract
The treatment of recurrent high-grade gliomas remains a major challenge of daily neuro-oncology practice, and imaging findings of new therapies may be challenging. Regorafenib is a multi-kinase inhibitor that has recently been introduced into clinical practice to treat recurrent glioblastoma, bringing with it a novel panel of MRI imaging findings. On the basis of the few data in the literature and on our personal experience, we have identified the main MRI changes during regorafenib therapy, and then, we defined two different patterns, trying to create a simple summary line of the main changes of pathological tissue during therapy. We named these patterns, respectively, pattern A (less frequent, similar to classical progression disease) and pattern B (more frequent, with decreased diffusivity and decrease contrast-enhancement). We have also reported MR changes concerning signal intensity on T1-weighted and T2-weighted images, SWI, and perfusion imaging, derived from the literature (small series or case reports) and from our clinical experience. The clinical implication of these imaging modifications remains to be defined, taking into account that we are still at the dawn in the evaluation of such imaging modifications.
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Affiliation(s)
- Simona Gaudino
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica Sacro Cuore of Rome, Rome, Italy
| | - Giammaria Marziali
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Carolina Giordano
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Riccardo Gigli
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Giuseppe Varcasia
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Francesca Magnani
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Silvia Chiesa
- Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology, UOC di Radioterapia Oncologica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Mario Balducci
- Università Cattolica Sacro Cuore of Rome, Rome, Italy
- Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology, UOC di Radioterapia Oncologica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Alessandro Maria Costantini
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Giuseppe Maria Della Pepa
- Institute of Neurosurgery, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University, Rome, Italy
| | - Alessandro Olivi
- Università Cattolica Sacro Cuore of Rome, Rome, Italy
- Institute of Neurosurgery, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University, Rome, Italy
| | - Rosellina Russo
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Cesare Colosimo
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica Sacro Cuore of Rome, Rome, Italy
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42
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Targeting Glioblastoma via Selective Alteration of Mitochondrial Redox State. Cancers (Basel) 2022; 14:cancers14030485. [PMID: 35158753 PMCID: PMC8833725 DOI: 10.3390/cancers14030485] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/06/2022] [Accepted: 01/11/2022] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Glioblastoma is characterized by a pronounced redox imbalance due to elevated glycolytic and mitochondrial oxidative metabolism. New therapeutic strategies have been developed to modulate glioblastoma redox signaling to effectively suppress growth and prolong survival. However, drug selectivity and therapeutic relapse prove to be the major challenges. We describe a pharmacological strategy for the selective targeting and treatment of glioblastoma using the redox active combination drug menadione/ascorbate, which is characterized by tolerance to normal cells and tissues. Menadione/ascorbate treatment of glioblastoma mice suppressed tumor growth and significantly increased survival without adverse side effects. This is accompanied by increased oxidative stress, decreased reducing capacity and decreased cellular density in the tumor alone, as well as increased brain perfusion and decreased regulation of several oncoproteins and oncometabolites, which implies modulation of the immune response and reduced drug resistance. We believe that this therapeutic strategy is feasible and promising and deserves the attention of clinicians. Abstract Glioblastoma is one of the most aggressive brain tumors, characterized by a pronounced redox imbalance, expressed in a high oxidative capacity of cancer cells due to their elevated glycolytic and mitochondrial oxidative metabolism. The assessment and modulation of the redox state of glioblastoma are crucial factors that can provide highly specific targeting and treatment. Our study describes a pharmacological strategy for targeting glioblastoma using a redox-active combination drug. The experiments were conducted in vivo on glioblastoma mice (intracranial model) and in vitro on cell lines (cancer and normal) treated with the redox cycling pair menadione/ascorbate (M/A). The following parameters were analyzed in vivo using MRI or ex vivo on tissue and blood specimens: tumor growth, survival, cerebral perfusion, cellular density, tissue redox state, expression of tumor-associated NADH oxidase (tNOX) and transforming growth factor-beta 1 (TGF-β1). Dose-dependent effects of M/A on cell viability, mitochondrial functionality, and redox homeostasis were evaluated in vitro. M/A treatment suppressed tumor growth and significantly increased survival without adverse side effects. This was accompanied by increased oxidative stress, decreased reducing capacity, and decreased cellular density in the tumor only, as well as increased cerebral perfusion and down-regulation of tNOX and TGF-β1. M/A induced selective cytotoxicity and overproduction of mitochondrial superoxide in isolated glioblastoma cells, but not in normal microglial cells. This was accompanied by a significant decrease in the over-reduced state of cancer cells and impairment of their “pro-oncogenic” functionality, assessed by dose-dependent decreases in: NADH, NAD+, succinate, glutathione, cellular reducing capacity, mitochondrial potential, steady-state ATP, and tNOX expression. The safety of M/A on normal cells was compromised by treatment with cerivastatin, a non-specific prenyltransferase inhibitor. In conclusion, M/A differentiates glioblastoma cells and tissues from normal cells and tissues by redox targeting, causing severe oxidative stress only in the tumor. The mechanism is complex and most likely involves prenylation of menadione in normal cells, but not in cancer cells, modulation of the immune response, a decrease in drug resistance, and a potential role in sensitizing glioblastoma to conventional chemotherapy.
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43
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Zalles M, Smith N, Saunders D, Lerner M, Fung KM, Battiste J, Towner RA. A tale of two multi-focal therapies for glioblastoma: An antibody targeting ELTD1 and nitrone-based OKN-007. J Cell Mol Med 2021; 26:570-582. [PMID: 34910361 PMCID: PMC8743651 DOI: 10.1111/jcmm.17133] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/11/2021] [Accepted: 12/01/2021] [Indexed: 12/23/2022] Open
Abstract
Glioblastoma (GBM) is the most common primary malignant brain tumour in adults. Despite a multimodal treatment response, survival for GBM patients remains between 12 and 15 months. Anti‐ELTD1 antibody therapy is effective in decreasing tumour volumes and increasing animal survival in an orthotopic GBM xenograft. OKN‐007 is a promising chemotherapeutic agent that is effective in various GBM animal models and is currently in two clinical trials. In this study, we sought to compare anti‐ELTD1 and OKN‐007 therapies, as single agents and combined, against bevacizumab, a commonly used therapeutic agent against GBM, in a human G55 xenograft mouse model. MRI was used to monitor tumour growth, and immunohistochemistry (IHC) was used to assess tumour markers for angiogenesis, cell migration and proliferation in the various treatment groups. OKN and anti‐ELTD1 treatments significantly increased animal survival, reduced tumour volumes and normalized the vasculature. Additionally, anti‐ELTD1 was also shown to significantly affect other pro‐angiogenic factors such as Notch1 and VEGFR2. Unlike bevacizumab, anti‐ELTD1 and OKN treatments did not induce a pro‐migratory phenotype within the tumours. Anti‐ELTD1 treatment was shown to be as effective as OKN therapy. Both OKN and anti‐ELTD1 therapies show promise as potential single‐agent multi‐focal therapies for GBM patients.
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Affiliation(s)
- Michelle Zalles
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA.,Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Nataliya Smith
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
| | - Debra Saunders
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
| | - Megan Lerner
- SurgeryResearch Laboratory, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Kar-Ming Fung
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.,Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - James Battiste
- Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.,Department of Neurology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Rheal A Towner
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA.,Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
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44
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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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45
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Cook TA, Jayamanne DT, Wheeler HR, Wong MHF, Parkinson JF, Cook RJ, Kastelan MA, Cove NJ, Brown C, Back MF. Redo craniotomy or bevacizumab for symptomatic steroid-refractory true or pseudoprogression following IMRT for glioblastoma. Neurooncol Pract 2021; 8:601-608. [PMID: 34594572 DOI: 10.1093/nop/npab034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background There is minimal evidence to support decision making for symptomatic steroid-refractory pseudoprogression or true progression occurring after intensity-modulated radiation therapy (IMRT) for glioblastoma (GBM). This study audited the survival outcome of patients managed with redo craniotomy (RedoSx) or bevacizumab (BEV) for steroid-refractory mass effect after IMRT for GBM. Methods Patients with GBM managed between 2008 and 2019 with the EORTC-NCIC Protocol were entered into a prospective database. Patients with symptomatic steroid-refractory mass effect within 6 months of IMRT managed with either RedoSx or BEV were identified for analysis. For the primary endpoint of median overall survival (OS) postintervention, outcome was analyzed in regards to potential prognostic factors, and differences between groups were assessed by log-rank analyses. Results Of the 399 patients managed with the EORTC-NCIC Protocol, 78 required an intervention within 6 months of IMRT completion for either true or pseudoprogression (49 with RedoSx and 29 with BEV). Subsequently, 20 of the 43 patients managed with RedoSx when BEV was clinically available, required salvage with BEV within 6 months after RedoSx. Median OS postintervention was 8.7 months (95% CI: 7.84-11.61) for the total group; and 8.7 months (95% CI: 6.8-13.1) for RedoSx and 9.4 months (95% CI: 7.8-13.6) for BEV (P = .38). Subsequent use of BEV in RedoSx patients was not associated with improved survival compared with RedoSx alone (P = .10). Age, time from IMRT, and ECOG performance status were not associated with OS. In the RedoSx patients, immunohistochemical features such as Ki-67% reduction correlated with survival. The presence of pure necrosis and residual tumor cells only had improved survival compared with the presence of gross tumor (P < .001). Conclusions At time of symptomatic steroid-refractory true or pseudoprogression following IMRT for GBM, BEV was equivalent to RedoSx in terms of OS. Pseudoprogression with residual cells at RedoSx was not associated with worse outcome compared to pure necrosis.
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Affiliation(s)
- Theresa A Cook
- Central Coast Cancer Centre, Gosford Hospital, Gosford, New South Wales, Australia
| | - Dasantha T Jayamanne
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Helen R Wheeler
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia.,Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Matthew H F Wong
- Central Coast Cancer Centre, Gosford Hospital, Gosford, New South Wales, Australia
| | - Jonathon F Parkinson
- Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia.,The Brain Cancer Group, Sydney, New South Wales, Australia.,Department of Neurosurgery, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Raymond J Cook
- Department of Neurosurgery, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Marina A Kastelan
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia.,The Brain Cancer Group, Sydney, New South Wales, Australia
| | - Nicola J Cove
- Central Coast Cancer Centre, Gosford Hospital, Gosford, New South Wales, Australia
| | - Christopher Brown
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Michael F Back
- Central Coast Cancer Centre, Gosford Hospital, Gosford, New South Wales, Australia.,Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia.,Genesis Cancer Care, Sydney, New South Wales, Australia.,Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia.,The Brain Cancer Group, Sydney, New South Wales, Australia
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46
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Abstract
This article reviews recent advances in the use of standard and advanced imaging techniques for diagnosis and treatment of central nervous system (CNS) tumors, including glioma and brain metastasis. Following the recent transition from a histology-based approach in classifying CNS tumors to one that integrates histology with the molecular information of tumor, the approaches for imaging CNS tumors have also been adapted to this new framework. Some challenges related to the diagnosis and treatment of CNS tumors, such as differentiating tumor from treatment-related imaging changes, require further progress to implement advanced imaging for clinical use.
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Affiliation(s)
- Raymond Y Huang
- Department of Neuroradiology, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Whitney B Pope
- Radiology, Section of Neuroradiology, Brain Tumor Imaging, UCLA Medical Center, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA; Department of Neurology, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA
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47
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Friismose AI, Markovic L, Nguyen N, Gerke O, Schulz MK, Mussmann BR. Amide proton transfer-weighted MRI in the clinical setting - correlation with dynamic susceptibility contrast perfusion in the post-treatment imaging of adult glioma patients at 3T. Radiography (Lond) 2021; 28:95-101. [PMID: 34509365 DOI: 10.1016/j.radi.2021.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/13/2021] [Accepted: 08/19/2021] [Indexed: 10/20/2022]
Abstract
INTRODUCTION We investigated the correlation between amide proton transfer-weighted magnetic resonance imaging (APTw MRI) and dynamic susceptibility contrast (DSC) perfusion in order to assess the potential of APTw MRI as an alternative to DSC in adult brain tumor (glioma) imaging. METHODS After Ethical Committee approval, forty adult patients, treated for histopathologically confirmed glioma (World Health Organization (WHO) grade II-IV), were prospectively imaged at 3 Tesla (3 T) with DSC perfusion and a commercially available three-dimensional (3D) APTw sequence. Two consultant neuroradiologists independently performed region of interest (ROI) measurements on relative cerebral blood volume (rCBV) and APTw maps, co-registered with anatomical images. The correlation APTw MRI-DSC perfusion was assessed using Spearman's rank-order test. Inter-observer agreement was evaluated by the intraclass correlation coefficient (ICC) and Bland-Altman (BA) plots. RESULTS A statistically significant moderately strong positive correlation was observed between maximum rCBV (rCBVmax) and maximum APTw (APTwmax) values (observer 1: r = 0.73; p < 0.01; observer 2: r = 0.62; p < 0.01). We found good inter-observer agreement for APTwmax (ICC = 0.82; 95% confidence interval (CI) 0.66-0.90), with somewhat broad outer 95% CI for the BA Limits of Agreement (LoA) (-1.6 to 1.9). ICC for APTwmax was higher than ICC for rCBVmax (ICC = 0.74; 95%; CI 0.50-0.86), but the difference was not statistically significant. CONCLUSION APTwmax values correlate positively with rCBVmax in patients treated for brain glioma. APTw imaging is a reproducible technique, with some observer dependence. Results need to be confirmed by a larger population analysis. IMPLICATIONS FOR PRACTICE APTw MRI can be a useful addition to glioma follow-up imaging and a potential alternative to DSC perfusion, especially in patients where contrast agent is contraindicated.
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Affiliation(s)
- A I Friismose
- Radiology Department, Odense University Hospital, Odense, Denmark.
| | - L Markovic
- Radiology Department, Odense University Hospital, Odense, Denmark
| | - N Nguyen
- Radiology Department, Odense University Hospital, Odense, Denmark
| | - O Gerke
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - M K Schulz
- Department of Neurosurgery, Odense University Hospital, Odense, Denmark
| | - B R Mussmann
- Radiology Department, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark; OPEN, Odense Patient Data Exploratory Network, Odense University Hospital, Odense, Denmark; Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
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48
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Menna G, Olivi A, Della Pepa GM. Letter to the Editor. A new approach for local tumor control. J Neurosurg 2021; 135:984-985. [PMID: 33962380 DOI: 10.3171/2021.2.jns21409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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49
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Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer 2021; 125:641-657. [PMID: 33958734 PMCID: PMC8405677 DOI: 10.1038/s41416-021-01387-w] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/10/2021] [Accepted: 03/31/2021] [Indexed: 02/03/2023] Open
Abstract
The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.
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50
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Jabeen S, Arbind A, Kumar D, Singh PK, Saini J, Sadashiva N, Krishna U, Arimappamagan A, Santosh V, Nagaraj C. Combined amino acid PET-MRI for identifying recurrence in post-treatment gliomas: together we grow. Eur J Hybrid Imaging 2021; 5:15. [PMID: 34405282 PMCID: PMC8371055 DOI: 10.1186/s41824-021-00109-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 07/21/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE The aim of this study is to compare the diagnostic accuracy of amino acid PET, MR perfusion and diffusion as stand-alone modalities and in combination in identifying recurrence in post-treatment gliomas and to qualitatively assess spatial concordance between the three modalities using simultaneous PET-MR acquisition. METHODS A retrospective review of 48 cases of post-treatment gliomas who underwent simultaneous PET-MRI using C11 methionine as radiotracer was performed. MR perfusion and diffusion sequences were acquired during the PET study. The following parameters were obtained: TBRmax, TBRmean, SUVmax, and SUVmean from the PET images; rCBV from perfusion; and ADCmean and ADCratio from the diffusion images. The final diagnosis was based on clinical/imaging follow-up and histopathology when available. ROC curve analysis in combination with logistic regression analysis was used to compare the diagnostic performance. Spatial concordance between modalities was graded as 0, 1, and 2 representing discordance, < 50% and > 50% concordance respectively. RESULTS There were 35 cases of recurrence and 13 cases of post-treatment changes without recurrence. The highest area under curve (AUC) was obtained for TBRmax followed by rCBV and ADCratio. The AUC increased significantly with a combination of rCBV and TBRmax. Amino acid PET showed the highest diagnostic accuracy and maximum agreement with the final diagnosis. There was discordance between ADC and PET in 22.9%, between rCBV and PET in 16.7% and between PET and contrast enhancement in 14.6% cases. CONCLUSION Amino acid PET had the highest diagnostic accuracy in identifying recurrence in post-treatment gliomas. Combination of PET with MRI further increased the AUC thus improving the diagnostic performance.
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Affiliation(s)
- Shumyla Jabeen
- Sher-i-Kashmir Institute of Medical Sciences, Srinagar, Kashmir, 190001, India
| | - Arpana Arbind
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, 560029, India
| | - Dinesh Kumar
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, 560029, India
| | - Pardeep Kumar Singh
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, 560029, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, 560029, India
| | - Nishanth Sadashiva
- Department of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, 560029, India
| | - Uday Krishna
- Department of Radiation Oncology, Kidwai Memorial Institute of Oncology, Bengaluru, Karnataka, 560029, India
| | - Arivazhagan Arimappamagan
- Department of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, 560029, India
| | - Vani Santosh
- Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, 560029, India
| | - Chandana Nagaraj
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, 560029, India.
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