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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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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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Heidari M, Shokrani P. Imaging Role in Diagnosis, Prognosis, and Treatment Response Prediction Associated with High-grade Glioma. JOURNAL OF MEDICAL SIGNALS & SENSORS 2024; 14:7. [PMID: 38993200 PMCID: PMC11111132 DOI: 10.4103/jmss.jmss_30_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 07/31/2022] [Accepted: 03/14/2023] [Indexed: 07/13/2024]
Abstract
Background Glioma is one of the most drug and radiation-resistant tumors. Gliomas suffer from inter- and intratumor heterogeneity which makes the outcome of similar treatment protocols vary from patient to patient. This article is aimed to overview the potential imaging markers for individual diagnosis, prognosis, and treatment response prediction in malignant glioma. Furthermore, the correlation between imaging findings and biological and clinical information of glioma patients is reviewed. Materials and Methods The search strategy in this study is to select related studies from scientific websites such as PubMed, Scopus, Google Scholar, and Web of Science published until 2022. It comprised a combination of keywords such as Biomarkers, Diagnosis, Prognosis, Imaging techniques, and malignant glioma, according to Medical Subject Headings. Results Some imaging parameters that are effective in glioma management include: ADC, FA, Ktrans, regional cerebral blood volume (rCBV), cerebral blood flow (CBF), ve, Cho/NAA and lactate/lipid ratios, intratumoral uptake of 18F-FET (for diagnostic application), RD, ADC, ve, vp, Ktrans, CBFT1, rCBV, tumor blood flow, Cho/NAA, lactate/lipid, MI/Cho, uptakes of 18F-FET, 11C-MET, and 18F-FLT (for prognostic and predictive application). Cerebral blood volume and Ktrans are related to molecular markers such as vascular endothelial growth factor (VEGF). Preoperative ADCmin value of GBM tumors is associated with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. 2-hydroxyglutarate metabolite and dynamic 18F-FDOPA positron emission tomography uptake are related to isocitrate dehydrogenase (IDH) mutations. Conclusion Parameters including ADC, RD, FA, rCBV, Ktrans, vp, and uptake of 18F-FET are useful for diagnosis, prognosis, and treatment response prediction in glioma. A significant correlation between molecular markers such as VEGF, MGMT, and IDH mutations with some diffusion and perfusion imaging parameters has been identified.
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Affiliation(s)
- Maryam Heidari
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Parvaneh Shokrani
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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4
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Maziero D, Azzam GA, de La Fuente M, Stoyanova R, Ford JC, Mellon EA. Implementation and evaluation of a dynamic contrast-enhanced MR perfusion protocol for glioblastoma using a 0.35 T MRI-Linac system. Phys Med 2024; 119:103316. [PMID: 38340693 DOI: 10.1016/j.ejmp.2024.103316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 11/29/2023] [Accepted: 02/05/2024] [Indexed: 02/12/2024] Open
Abstract
PURPOSE MRI-linear accelerator (MRI-Linac) systems allow for daily tracking of MRI changes during radiotherapy (RT). Since one common MRI-Linac operates at 0.35 T, there are efforts towards developing protocols at that field strength. In this study we demonstrate the implementation of a post-contrast 3DT1-weighted (3D-T1w) and dynamic contrast-enhancement (DCE) protocol to assess glioblastoma response to RT using a 0.35 T MRI-Linac. METHODS AND MATERIALS The protocol implemented was used to acquire 3D-T1w and DCE data from a flow phantom and two patients with glioblastoma (a responder and a non-responder) who underwent RT on a 0.35 T MRI-Linac. The detection of post-contrast-enhanced volumes was evaluated by comparing the 3DT1w images from the 0.35 T MRI-Linac to images obtained using a 3 T scanner. The DCE data were tested temporally and spatially using data from a flow phantom and patients. Ktrans maps were derived from DCE at three time points (a week before treatment-Pre RT, four weeks through treatment-Mid RT, and three weeks after treatment-Post RT) and were validated with patients' treatment outcomes. RESULTS The 3D-T1w contrast-enhancement volumes were visually and volumetrically similar between 0.35 T MRI-Linac and 3 T. DCE images showed temporal stability, and associated Ktrans maps were consistent with patient response to treatment. On average, Ktrans values showed a 54 % decrease and 8.6 % increase for a responder and non-responder respectively when Pre RT and Mid RT images were compared. CONCLUSION Our findings support the feasibility of obtaining post-contrast 3D-T1w and DCE data from patients with glioblastoma using a 0.35 T MRI-Linac system.
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Affiliation(s)
- Danilo Maziero
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, La Jolla, CA 92093, United States; Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States.
| | - Gregory Albert Azzam
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States
| | - Macarena de La Fuente
- Department of Neurology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States
| | - Radka Stoyanova
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States
| | - John Chetley Ford
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States
| | - Eric Albert Mellon
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States
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Steidl E, Filipski K, Hattingen E, Steinbach JP, Maurer GD. Longitudinal study on MRI and neuropathological findings: Neither DSC-perfusion derived rCBVmax nor vessel densities correlate between newly diagnosed and progressive glioblastoma. PLoS One 2023; 18:e0274400. [PMID: 36724187 PMCID: PMC9891512 DOI: 10.1371/journal.pone.0274400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/26/2022] [Indexed: 02/02/2023] Open
Abstract
INTRODUCTION When evaluating MRIs for glioblastoma progression, previous scans are usually included into the review. Nowadays dynamic susceptibility contrast (DSC)-perfusion is an essential component in MR-diagnostics of gliomas, since the extent of hyperperfusion upon first diagnosis correlates with gene expression and survival. We aimed to investigate if this initial perfusion signature also characterizes the glioblastoma at time of progression. If so, DSC-perfusion data from the initial diagnosis could be of diagnostic benefit in follow-up assessments. METHODS We retrospectively identified 65 patients with isocitrate dehydrogenase wildtype glioblastoma who had received technically identical DSC-perfusion measurements at initial diagnosis and at time of first progression. We determined maximum relative cerebral blood volume values (rCBVmax) by standardized re-evaluation of the data including leakage correction. In addition, the corresponding tissue samples from 24 patients were examined histologically for the maximum vessel density within the tumor. Differences (paired t-test/ Wilcoxon matched pairs test) and correlations (Spearman) between the measurements at both timepoints were calculated. RESULTS The rCBVmax was consistently lower at time of progression compared to rCBVmax at time of first diagnosis (p < .001). There was no correlation between the rCBVmax values at both timepoints (r = .12). These findings were reflected in the histological examination, with a lower vessel density in progressive glioblastoma (p = .01) and no correlation between the two timepoints (r = -.07). CONCLUSION Our results suggest that the extent of hyperperfusion in glioblastoma at first diagnosis is not a sustaining tumor characteristic. Hence, the rCBVmax at initial diagnosis should be disregarded when reviewing MRIs for glioblastoma progression.
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Affiliation(s)
- Eike Steidl
- Institute of Neuroradiology, Goethe University Hospital, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- * E-mail:
| | - Katharina Filipski
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Neurology (Edinger Institute), Goethe University Hospital, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
| | - Elke Hattingen
- Institute of Neuroradiology, Goethe University Hospital, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
| | - Joachim P. Steinbach
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
- Dr. Senckenberg Institute of Neurooncology, Goethe University Hospital, Frankfurt am Main, Germany
| | - Gabriele D. Maurer
- Dr. Senckenberg Institute of Neurooncology, Goethe University Hospital, Frankfurt am Main, Germany
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Fatania K, Frood R, Tyyger M, McDermott G, Fernandez S, Shaw GC, Boissinot M, Salvatore D, Ottobrini L, Teh I, Wright J, Bailey MA, Koch-Paszkowski J, Schneider JE, Buckley DL, Murray L, Scarsbrook A, Short SC, Currie S. Exploratory Analysis of Serial 18F-fluciclovine PET-CT and Multiparametric MRI during Chemoradiation for Glioblastoma. Cancers (Basel) 2022; 14:3485. [PMID: 35884545 PMCID: PMC9315674 DOI: 10.3390/cancers14143485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/11/2022] [Accepted: 07/15/2022] [Indexed: 12/03/2022] Open
Abstract
Anti-1-amino-3-18fluorine-fluorocyclobutane-1-carboxylic acid (18F-fluciclovine) positron emission tomography (PET) shows preferential glioma uptake but there is little data on how uptake correlates with post-contrast T1-weighted (Gd-T1) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) activity during adjuvant treatment. This pilot study aimed to compare 18F-fluciclovine PET, DCE-MRI and Gd-T1 in patients undergoing chemoradiotherapy for glioblastoma (GBM), and in a parallel pre-clinical GBM model, to investigate correlation between 18F-fluciclovine uptake, MRI findings, and tumour biology. 18F-fluciclovine-PET-computed tomography (PET-CT) and MRI including DCE-MRI were acquired before, during and after adjuvant chemoradiotherapy (60 Gy in 30 fractions with temozolomide) in GBM patients. MRI volumes were manually contoured; PET volumes were defined using semi-automatic thresholding. The similarity of the PET and DCE-MRI volumes outside the Gd-T1 volume boundary was measured using the Dice similarity coefficient (DSC). CT-2A tumour-bearing mice underwent MRI and 18F-fluciclovine PET-CT. Post-mortem mice brains underwent immunohistochemistry staining for ASCT2 (amino acid transporter), nestin (stemness) and Ki-67 (proliferation) to assess for biologically active tumour. 6 patients were recruited (GBM 1-6) and grouped according to overall survival (OS)-short survival (GBM-SS, median OS 249 days) and long survival (GBM-LS, median 903 days). For GBM-SS, PET tumour volumes were greater than DCE-MRI, in turn greater than Gd-T1. For GBM-LS, Gd-T1 and DCE-MRI were greater than PET. Tumour-specific 18F-fluciclovine uptake on pre-clinical PET-CT corresponded to immunostaining for Ki-67, nestin and ASCT2. Results suggest volumes of 18F-fluciclovine-PET activity beyond that depicted by DCE-MRI and Gd-T1 are associated with poorer prognosis in patients undergoing chemoradiotherapy for GBM. The pre-clinical model confirmed 18F-fluciclovine uptake reflected biologically active tumour.
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Affiliation(s)
- Kavi Fatania
- Department of Radiology, Leeds Teaching Hospitals Trust, Leeds General Infirmary, Leeds LS1 3EX, UK; (R.F.); (A.S.); (S.C.)
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK; (G.C.S.); (M.B.); (L.M.); (S.C.S.)
| | - Russell Frood
- Department of Radiology, Leeds Teaching Hospitals Trust, Leeds General Infirmary, Leeds LS1 3EX, UK; (R.F.); (A.S.); (S.C.)
| | - Marcus Tyyger
- Department of Medical Physics, Leeds Teaching Hospitals Trust, St James’s University Hospital, Leeds LS9 7TF, UK; (M.T.); (G.M.)
| | - Garry McDermott
- Department of Medical Physics, Leeds Teaching Hospitals Trust, St James’s University Hospital, Leeds LS9 7TF, UK; (M.T.); (G.M.)
| | - Sharon Fernandez
- Department of Clinical Oncology, Leeds Teaching Hospitals Trust, St James’s University Hospital, Leeds LS9 7TF, UK;
| | - Gary C. Shaw
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK; (G.C.S.); (M.B.); (L.M.); (S.C.S.)
| | - Marjorie Boissinot
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK; (G.C.S.); (M.B.); (L.M.); (S.C.S.)
| | - Daniela Salvatore
- Department of Pathophysiology and Transplantation, University of Milan, 20122 Segrate, Italy; (D.S.); (L.O.)
| | - Luisa Ottobrini
- Department of Pathophysiology and Transplantation, University of Milan, 20122 Segrate, Italy; (D.S.); (L.O.)
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 20054 Segrate, Italy
| | - Irvin Teh
- Biomedical Imaging Science Department, and Discovery & Translational Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9TJ, UK; (I.T.); (J.W.); (M.A.B.); (J.K.-P.); (J.E.S.); (D.L.B.)
| | - John Wright
- Biomedical Imaging Science Department, and Discovery & Translational Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9TJ, UK; (I.T.); (J.W.); (M.A.B.); (J.K.-P.); (J.E.S.); (D.L.B.)
| | - Marc A. Bailey
- Biomedical Imaging Science Department, and Discovery & Translational Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9TJ, UK; (I.T.); (J.W.); (M.A.B.); (J.K.-P.); (J.E.S.); (D.L.B.)
- Leeds Vascular Institute, Leeds Teaching Hospitals Trust, Leeds General Infirmary, Leeds LS1 3EX, UK
| | - Joanna Koch-Paszkowski
- Biomedical Imaging Science Department, and Discovery & Translational Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9TJ, UK; (I.T.); (J.W.); (M.A.B.); (J.K.-P.); (J.E.S.); (D.L.B.)
| | - Jurgen E. Schneider
- Biomedical Imaging Science Department, and Discovery & Translational Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9TJ, UK; (I.T.); (J.W.); (M.A.B.); (J.K.-P.); (J.E.S.); (D.L.B.)
| | - David L. Buckley
- Biomedical Imaging Science Department, and Discovery & Translational Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9TJ, UK; (I.T.); (J.W.); (M.A.B.); (J.K.-P.); (J.E.S.); (D.L.B.)
| | - Louise Murray
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK; (G.C.S.); (M.B.); (L.M.); (S.C.S.)
- Department of Clinical Oncology, Leeds Teaching Hospitals Trust, St James’s University Hospital, Leeds LS9 7TF, UK;
| | - Andrew Scarsbrook
- Department of Radiology, Leeds Teaching Hospitals Trust, Leeds General Infirmary, Leeds LS1 3EX, UK; (R.F.); (A.S.); (S.C.)
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK; (G.C.S.); (M.B.); (L.M.); (S.C.S.)
| | - Susan C. Short
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK; (G.C.S.); (M.B.); (L.M.); (S.C.S.)
- Department of Clinical Oncology, Leeds Teaching Hospitals Trust, St James’s University Hospital, Leeds LS9 7TF, UK;
| | - Stuart Currie
- Department of Radiology, Leeds Teaching Hospitals Trust, Leeds General Infirmary, Leeds LS1 3EX, UK; (R.F.); (A.S.); (S.C.)
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK; (G.C.S.); (M.B.); (L.M.); (S.C.S.)
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7
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Goldman J, Hagiwara A, Yao J, Raymond C, Ong C, Bakhti R, Kwon E, Farhat M, Torres C, Erickson LG, Curl BJ, Lee M, Pope WB, Salamon N, Nghiemphu PL, Ji M, Eldred BS, Liau LM, Lai A, Cloughesy TF, Chung C, Ellingson BM. Paradoxical Association Between Relative Cerebral Blood Volume Dynamics Following Chemoradiation and Increased Progression-Free Survival in Newly Diagnosed IDH Wild-Type MGMT Promoter Methylated Glioblastoma With Measurable Disease. Front Oncol 2022; 12:849993. [PMID: 35371980 PMCID: PMC8964348 DOI: 10.3389/fonc.2022.849993] [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: 01/06/2022] [Accepted: 02/07/2022] [Indexed: 11/15/2022] Open
Abstract
Background and Purpose While relative cerebral blood volume (rCBV) may be diagnostic and prognostic for survival in glioblastoma (GBM), changes in rCBV during chemoradiation in the subset of newly diagnosed GBM with subtotal resection and the impact of MGMT promoter methylation status on survival have not been explored. This study aimed to investigate the association between rCBV response, MGMT methylation status, and progression-free (PFS) and overall survival (OS) in newly diagnosed GBM with measurable enhancing lesions. Methods 1,153 newly diagnosed IDH wild-type GBM patients were screened and 53 patients (4.6%) had measurable post-surgical tumor (>1mL). rCBV was measured before and after patients underwent chemoradiation. Patients with a decrease in rCBV >10% were considered rCBV Responders, while patients with an increase or a decrease in rCBV <10% were considered rCBV Non-Responders. The association between change in enhancing tumor volume, change in rCBV, MGMT promotor methylation status, and PFS or OS were explored. Results A decrease in tumor volume following chemoradiation trended towards longer OS (p=0.12; median OS=26.8 vs. 16.3 months). Paradoxically, rCBV Non-Responders had a significantly improved PFS compared to Responders (p=0.047; median PFS=9.6 vs. 7.2 months). MGMT methylated rCBV Non-Responders exhibited a significantly longer PFS compared to MGMT unmethylated rCBV Non-Responders (p<0.001; median PFS=0.5 vs. 7.1 months), and MGMT methylated rCBV Non-Responders trended towards longer PFS compared to methylated rCBV Responders (p=0.089; median PFS=20.5 vs. 13.8 months). Conclusions This preliminary report demonstrates that in newly diagnosed IDH wild-type GBM with measurable enhancing disease after surgery (5% of patients), an enigmatic non-response in rCBV was associated with longer PFS, particularly in MGMT methylated patients.
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Affiliation(s)
- Jodi Goldman
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Christian Ong
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Rojin Bakhti
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Elizabeth Kwon
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Maguy Farhat
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Carlo Torres
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lily G Erickson
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Brandon J Curl
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Maggie Lee
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Matthew Ji
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Blaine S Eldred
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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8
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Fuster-Garcia E, Thokle Hovden I, Fløgstad Svensson S, Larsson C, Vardal J, Bjørnerud A, Emblem KE. Quantification of Tissue Compression Identifies High-Grade Glioma Patients with Reduced Survival. Cancers (Basel) 2022; 14:cancers14071725. [PMID: 35406497 PMCID: PMC8997138 DOI: 10.3390/cancers14071725] [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] [Received: 02/17/2022] [Revised: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 02/01/2023] Open
Abstract
The compression of peritumoral healthy tissue in brain tumor patients is considered a major cause of the life-threatening neurologic symptoms. Although significant deformations caused by the tumor growth can be observed radiologically, the quantification of minor tissue deformations have not been widely investigated. In this study, we propose a method to quantify subtle peritumoral deformations. A total of 127 MRI longitudinal studies from 23 patients with high-grade glioma were included. We estimate longitudinal displacement fields based on a symmetric normalization algorithm and we propose four biomarkers. We assess the interpatient and intrapatient association between proposed biomarkers and the survival based on Cox analyses, and the potential of the biomarkers to stratify patients according to their survival based on Kaplan−Meier analysis. Biomarkers show a significant intrapatient association with survival (p < 0.05); however, only compression biomarkers show the ability to stratify patients between those with higher and lower overall survival (AUC = 0.83, HR = 6.30, p < 0.05 for CompCH). The compression biomarkers present three times higher Hazard Ratios than those representing only displacement. Our study provides a robust and automated method for quantifying and delineating compression in the peritumoral area. Based on the proposed methodology, we found an association between lower compression in the peritumoral area and good prognosis in high-grade glial tumors.
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Affiliation(s)
- Elies Fuster-Garcia
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, 46022 Valencia, Spain
- Correspondence:
| | - Ivar Thokle Hovden
- Department of Diagnostic Physics, Oslo University Hospital, 0372 Oslo, Norway; (I.T.H.); (S.F.S.); (K.E.E.)
- Department of Physics, University of Oslo, 0316 Oslo, Norway;
| | - Siri Fløgstad Svensson
- Department of Diagnostic Physics, Oslo University Hospital, 0372 Oslo, Norway; (I.T.H.); (S.F.S.); (K.E.E.)
- Department of Physics, University of Oslo, 0316 Oslo, Norway;
| | - Christopher Larsson
- Department of Neurosurgery, Oslo University Hospital, 0372 Oslo, Norway;
- Unit for Computational Radiology and Artificial Intelligence, Oslo University Hospital, 0372 Oslo, Norway;
| | - Jonas Vardal
- Unit for Computational Radiology and Artificial Intelligence, Oslo University Hospital, 0372 Oslo, Norway;
- Department of Radiology, Drammen Hospital, Vestre Viken Hospital Trust, 3004 Drammen, Norway
| | - Atle Bjørnerud
- Department of Physics, University of Oslo, 0316 Oslo, Norway;
- Unit for Computational Radiology and Artificial Intelligence, Oslo University Hospital, 0372 Oslo, Norway;
- Department of Psychology, Faculty for Social Sciences, University of Oslo, 0851 Oslo, Norway
| | - Kyrre E. Emblem
- Department of Diagnostic Physics, Oslo University Hospital, 0372 Oslo, Norway; (I.T.H.); (S.F.S.); (K.E.E.)
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9
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Stumpo V, Guida L, Bellomo J, Van Niftrik CHB, Sebök M, Berhouma M, Bink A, Weller M, Kulcsar Z, Regli L, Fierstra J. Hemodynamic Imaging in Cerebral Diffuse Glioma—Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions. Cancers (Basel) 2022; 14:cancers14051342. [PMID: 35267650 PMCID: PMC8909110 DOI: 10.3390/cancers14051342] [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: 02/01/2022] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 02/05/2023] Open
Abstract
Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.
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Affiliation(s)
- Vittorio Stumpo
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
- Correspondence:
| | - Lelio Guida
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Jacopo Bellomo
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Christiaan Hendrik Bas Van Niftrik
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Martina Sebök
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Moncef Berhouma
- Department of Neurosurgical Oncology and Vascular Neurosurgery, Pierre Wertheimer Neurological and Neurosurgical Hospital, Hospices Civils de Lyon, 69500 Lyon, France;
| | - Andrea Bink
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
- Department of Neuroradiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Michael Weller
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
- Department of Neurology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Zsolt Kulcsar
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
- Department of Neuroradiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Luca Regli
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Jorn Fierstra
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
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10
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Parent EE, Johnson DR, Gleason T, Villanueva-Meyer JE. Neuro-Oncology Practice Clinical Debate: FDG PET to differentiate glioblastoma recurrence from treatment-related changes. Neurooncol Pract 2021; 8:518-525. [PMID: 34594566 PMCID: PMC8475205 DOI: 10.1093/nop/npab027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The ability to accurately differentiate treatment-related changes (ie, pseudoprogression and radiation necrosis) from recurrent glioma remains a critical diagnostic problem in neuro-oncology. Because these entities are treated differently and have vastly different outcomes, accurate diagnosis is necessary to provide optimal patient care. In current practice, this diagnostic quandary commonly requires either serial imaging or histopathologic tissue confirmation. In this article, experts in the field debate the utility of 2-deoxy-2[18F]fluoro-d-glucose positron emission tomography (FDG PET) as an imaging tool to distinguish tumor recurrence from treatment-related changes in a patient with glioblastoma and progressive contrast enhancement on magnetic resonance (MR) following chemoradiotherapy.
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Affiliation(s)
- Ephraim E Parent
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
| | - Derek R Johnson
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Tyler Gleason
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Javier E Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
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11
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Zolotovskaia M, Tkachev V, Sorokin M, Garazha A, Kim E, Kantelhardt SR, Bikar SE, Zottel A, Šamec N, Kuzmin D, Sprang B, Moisseev A, Giese A, Efimov V, Jovčevska I, Buzdin A. Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas. Cancers (Basel) 2021; 13:4117. [PMID: 34439271 PMCID: PMC8394245 DOI: 10.3390/cancers13164117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 01/17/2023] Open
Abstract
Gliomas are the most common malignant brain tumors with high mortality rates. Recently we showed that the FREM2 gene has a role in glioblastoma progression. Here we reconstructed the FREM2 molecular pathway using the human interactome model. We assessed the biomarker capacity of FREM2 expression and its pathway as the overall survival (OS) and progression-free survival (PFS) biomarkers. To this end, we used three literature and one experimental RNA sequencing datasets collectively covering 566 glioblastomas (GBM) and 1097 low-grade gliomas (LGG). The activation level of deduced FREM2 pathway showed strong biomarker characteristics and significantly outperformed the FREM2 expression level itself. For all relevant datasets, it could robustly discriminate GBM and LGG (p < 1.63 × 10-13, AUC > 0.74). High FREM2 pathway activation level was associated with poor OS in LGG (p < 0.001), and low PFS in LGG (p < 0.001) and GBM (p < 0.05). FREM2 pathway activation level was poor prognosis biomarker for OS (p < 0.05) and PFS (p < 0.05) in LGG with IDH mutation, for PFS in LGG with wild type IDH (p < 0.001) and mutant IDH with 1p/19q codeletion(p < 0.05), in GBM with unmethylated MGMT (p < 0.05), and in GBM with wild type IDH (p < 0.05). Thus, we conclude that the activation level of the FREM2 pathway is a potent new-generation diagnostic and prognostic biomarker for multiple molecular subtypes of GBM and LGG.
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Affiliation(s)
- Marianna Zolotovskaia
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Department of Oncology, Hematology and Radiotherapy, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Victor Tkachev
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Maxim Sorokin
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Andrew Garazha
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
| | - Ella Kim
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Sven Rainer Kantelhardt
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Sven-Ernö Bikar
- StarSEQ GmbH, Joh.-Joachim-Becher-Weg 30a, 55128 Mainz, Germany;
| | - Alja Zottel
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Neja Šamec
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Denis Kuzmin
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Bettina Sprang
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Alexey Moisseev
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
| | - Alf Giese
- Orthocentrum Hamburg, Hansastrasse 1, 20149 Hamburg, Germany;
| | - Victor Efimov
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Ivana Jovčevska
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
- European Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, 1200 Brussels, Belgium
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12
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Wang C, Padgett KR, Su MY, Mellon EA, Maziero D, Chang Z. Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy. Med Phys 2021; 49:2794-2819. [PMID: 34374098 DOI: 10.1002/mp.15130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 11/11/2022] Open
Abstract
Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.
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Affiliation(s)
- Chunhao Wang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA.,Department of Radiology, University of Miami, Miami, Florida, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California, USA.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Eric A Mellon
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Danilo Maziero
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Zheng Chang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
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13
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Operator dependency of arterial input function in dynamic contrast-enhanced MRI. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 35:105-112. [PMID: 34213687 PMCID: PMC8901481 DOI: 10.1007/s10334-021-00926-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 04/20/2021] [Accepted: 04/23/2021] [Indexed: 11/09/2022]
Abstract
Objective To investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas. Methods The study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in Ktrans was assessed by Fleiss’ kappa (κ). Results There was a moderate to substantial agreement (ICC 0.51–0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77–0.95 for Ktrans, kep and ve and ICC 0.70 for vp. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in Ktrans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement. Discussion AUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.
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14
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Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI. Sci Rep 2021; 11:9974. [PMID: 33976264 PMCID: PMC8113258 DOI: 10.1038/s41598-021-89218-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 04/22/2021] [Indexed: 02/03/2023] Open
Abstract
Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903-1.000) for local recurrence; 0.864 (0.726-0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or "radiomic risk score", increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.
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15
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Maziero D, Straza MW, Ford JC, Bovi JA, Diwanji T, Stoyanova R, Paulson ES, Mellon EA. MR-Guided Radiotherapy for Brain and Spine Tumors. Front Oncol 2021; 11:626100. [PMID: 33763361 PMCID: PMC7982530 DOI: 10.3389/fonc.2021.626100] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 02/12/2021] [Indexed: 12/19/2022] Open
Abstract
MRI is the standard modality to assess anatomy and response to treatment in brain and spine tumors given its superb anatomic soft tissue contrast (e.g., T1 and T2) and numerous additional intrinsic contrast mechanisms that can be used to investigate physiology (e.g., diffusion, perfusion, spectroscopy). As such, hybrid MRI and radiotherapy (RT) devices hold unique promise for Magnetic Resonance guided Radiation Therapy (MRgRT). In the brain, MRgRT provides daily visualizations of evolving tumors that are not seen with cone beam CT guidance and cannot be fully characterized with occasional standalone MRI scans. Significant evolving anatomic changes during radiotherapy can be observed in patients with glioblastoma during the 6-week fractionated MRIgRT course. In this review, a case of rapidly changing symptomatic tumor is demonstrated for possible therapy adaptation. For stereotactic body RT of the spine, MRgRT acquires clear isotropic images of tumor in relation to spinal cord, cerebral spinal fluid, and nearby moving organs at risk such as bowel. This visualization allows for setup reassurance and the possibility of adaptive radiotherapy based on anatomy in difficult cases. A review of the literature for MR relaxometry, diffusion, perfusion, and spectroscopy during RT is also presented. These techniques are known to correlate with physiologic changes in the tumor such as cellularity, necrosis, and metabolism, and serve as early biomarkers of chemotherapy and RT response correlating with patient survival. While physiologic tumor investigations during RT have been limited by the feasibility and cost of obtaining frequent standalone MRIs, MRIgRT systems have enabled daily and widespread physiologic measurements. We demonstrate an example case of a poorly responding tumor on the 0.35 T MRIgRT system with relaxometry and diffusion measured several times per week. Future studies must elucidate which changes in MR-based physiologic metrics and at which timepoints best predict patient outcomes. This will lead to early treatment intensification for tumors identified to have the worst physiologic responses during RT in efforts to improve glioblastoma survival.
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Affiliation(s)
- Danilo Maziero
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Michael W Straza
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - John C Ford
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Joseph A Bovi
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Tejan Diwanji
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Radka Stoyanova
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Eric S Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Eric A Mellon
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
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16
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Sabour S. Prediction of survival and progression in glioblastoma patients using temporal perfusion changes during radiochemotherapy; Methodological issues to avoid misinterpretation. Magn Reson Imaging 2020; 71:25-26. [PMID: 32428548 DOI: 10.1016/j.mri.2020.05.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 05/14/2020] [Indexed: 11/16/2022]
Affiliation(s)
- Siamak Sabour
- Department of Clinical Epidemiology, School of public health, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Safety Promotions and Injury Prevention Research Centre, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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