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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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Ladenhauf VK, Galijasevic M, Kerschbaumer J, Freyschlag CF, Nowosielski M, Birkl-Toeglhofer AM, Haybaeck J, Gizewski ER, Mangesius S, Grams AE. Peritumoral ADC Values Correlate with the MGMT Methylation Status in Patients with Glioblastoma. Cancers (Basel) 2023; 15:cancers15051384. [PMID: 36900177 PMCID: PMC10000073 DOI: 10.3390/cancers15051384] [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: 01/11/2023] [Revised: 02/14/2023] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
Different results have been reported concerning the relationship of the apparent diffusion coefficient (ADC) values and the status of methylation as the promoter gene for the enzyme methylguanine-DNA methyltransferase (MGMT) in patients with glioblastomas (GBs). The aim of this study was to investigate if there were correlations between the ADC values of the enhancing tumor and peritumoral areas of GBs and the MGMT methylation status. In this retrospective study, we included 42 patients with newly diagnosed unilocular GB with one MRI study prior to any treatment and histopathological data. After co-registration of ADC maps with T1-weighted sequences after contrast administration and dynamic susceptibility contrast (DSC) perfusion, we manually selected one region-of-interest (ROI) in the enhancing and perfused tumor and one ROI in the peritumoral white matter. Both ROIs were mirrored in the healthy hemisphere for normalization. In the peritumoral white matter, absolute and normalized ADC values were significantly higher in patients with MGMT-unmethylated tumors, as compared to patients with MGMT-methylated tumors (absolute values p = 0.002, normalized p = 0.0007). There were no significant differences in the enhancing tumor parts. The ADC values in the peritumoral region correlated with MGMT methylation status, confirmed by normalized ADC values. In contrast to other studies, we could not find a correlation between the ADC values or the normalized ADC values and the MGMT methylation status in the enhancing tumor parts.
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Affiliation(s)
- Valentin Karl Ladenhauf
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Malik Galijasevic
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Correspondence: ; Tel.: +43-50-504-83248
| | - Johannes Kerschbaumer
- Department of Neurosurgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | | | - Martha Nowosielski
- Department of Neurology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Anna Maria Birkl-Toeglhofer
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Johannes Haybaeck
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Diagnostic & Research Center for Molecular BioMedicine, Institute of Pathology, Medical University of Graz, 8010 Graz, Austria
| | - Elke Ruth Gizewski
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Stephanie Mangesius
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Astrid Ellen Grams
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
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Gihr G, Horvath-Rizea D, Kohlhof-Meinecke P, Ganslandt O, Henkes H, Härtig W, Donitza A, Skalej M, Schob S. Diffusion Weighted Imaging in Gliomas: A Histogram-Based Approach for Tumor Characterization. Cancers (Basel) 2022; 14:cancers14143393. [PMID: 35884457 PMCID: PMC9321540 DOI: 10.3390/cancers14143393] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/07/2022] [Accepted: 07/09/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Glioma represent approximately one-third of all brain tumors. Although they differ clinically, histologically and genetically, they often are not distinguishable by morphological magnetic resonance imaging (MRI) diagnostics. We therefore investigated in this retrospective study whether diffusion weighted imaging (DWI) using a radiomic approach could provide complementary information with respect to tumor differentiation and cell proliferation, as well as the underlying genetic and epigenetic tumor profile. We identified several histogram features that could facilitate presurgical tumor grading and potentially enable one to draw conclusions about tumor characteristics on a cellular and subcellular scale. Abstract (1) Background: Astrocytic gliomas present overlapping appearances in conventional MRI. Supplementary techniques are necessary to improve preoperative diagnostics. Quantitative DWI via the computation of apparent diffusion coefficient (ADC) histograms has proven valuable for tumor characterization and prognosis in this regard. Thus, this study aimed to investigate (I) the potential of ADC histogram analysis (HA) for distinguishing low-grade gliomas (LGG) and high-grade gliomas (HGG) and (II) whether those parameters are associated with Ki-67 immunolabelling, the isocitrate-dehydrogenase-1 (IDH1) mutation profile and the methylguanine-DNA-methyl-transferase (MGMT) promoter methylation profile; (2) Methods: The ADC-histograms of 82 gliomas were computed. Statistical analysis was performed to elucidate associations between histogram features and WHO grade, Ki-67 immunolabelling, IDH1 and MGMT profile; (3) Results: Minimum, lower percentiles (10th and 25th), median, modus and entropy of the ADC histogram were significantly lower in HGG. Significant differences between IDH1-mutated and IDH1-wildtype gliomas were revealed for maximum, lower percentiles, modus, standard deviation (SD), entropy and skewness. No differences were found concerning the MGMT status. Significant correlations with Ki-67 immunolabelling were demonstrated for minimum, maximum, lower percentiles, median, modus, SD and skewness; (4) Conclusions: ADC HA facilitates non-invasive prediction of the WHO grade, tumor-proliferation rate and clinically significant mutations in case of astrocytic gliomas.
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Affiliation(s)
- Georg Gihr
- Katharinenhospital Stuttgart, Clinic for Neuroradiology, 70174 Stuttgart, Germany; (D.H.-R.); (H.H.)
- Correspondence: (G.G.); (S.S.); Tel.: +49-711-2785-4454 (G.G.); +49-345-557-2342 (S.S.)
| | - Diana Horvath-Rizea
- Katharinenhospital Stuttgart, Clinic for Neuroradiology, 70174 Stuttgart, Germany; (D.H.-R.); (H.H.)
| | | | - Oliver Ganslandt
- Katharinenhospital Stuttgart, Clinic for Neurosurgery, 70174 Stuttgart, Germany;
| | - Hans Henkes
- Katharinenhospital Stuttgart, Clinic for Neuroradiology, 70174 Stuttgart, Germany; (D.H.-R.); (H.H.)
| | - Wolfgang Härtig
- Paul Flechsig Institute for Brain Research, University of Leipzig, 04103 Leipzig, Germany;
| | - Aneta Donitza
- Department for Neuroradiology, Clinic and Policlinic for Radiology, University Hospital Halle (Saale), 06120 Halle (Saale), Germany; (A.D.); (M.S.)
| | - Martin Skalej
- Department for Neuroradiology, Clinic and Policlinic for Radiology, University Hospital Halle (Saale), 06120 Halle (Saale), Germany; (A.D.); (M.S.)
| | - Stefan Schob
- Department for Neuroradiology, Clinic and Policlinic for Radiology, University Hospital Halle (Saale), 06120 Halle (Saale), Germany; (A.D.); (M.S.)
- Correspondence: (G.G.); (S.S.); Tel.: +49-711-2785-4454 (G.G.); +49-345-557-2342 (S.S.)
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Chawla S, Bukhari S, Afridi OM, Wang S, Yadav SK, Akbari H, Verma G, Nath K, Haris M, Bagley S, Davatzikos C, Loevner LA, Mohan S. Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma. NMR IN BIOMEDICINE 2022; 35:e4719. [PMID: 35233862 PMCID: PMC9203929 DOI: 10.1002/nbm.4719] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 02/22/2022] [Accepted: 02/25/2022] [Indexed: 05/15/2023]
Abstract
Pseudoprogression (PsP) refers to treatment-related clinico-radiologic changes mimicking true progression (TP) that occurs in patients with glioblastoma (GBM), predominantly within the first 6 months after the completion of surgery and concurrent chemoradiation therapy (CCRT) with temozolomide. Accurate differentiation of TP from PsP is essential for making informed decisions on appropriate therapeutic intervention as well as for prognostication of these patients. Conventional neuroimaging findings are often equivocal in distinguishing between TP and PsP and present a considerable diagnostic dilemma to oncologists and radiologists. These challenges have emphasized the need for developing alternative imaging techniques that may aid in the accurate diagnosis of TP and PsP. In this review, we encapsulate the current state of knowledge in the clinical applications of commonly used metabolic and physiologic magnetic resonance (MR) imaging techniques such as diffusion and perfusion imaging and proton spectroscopy in distinguishing TP from PsP. We also showcase the potential of promising imaging techniques, such as amide proton transfer and amino acid-based positron emission tomography, in providing useful information about the treatment response. Additionally, we highlight the role of "radiomics", which is an emerging field of radiology that has the potential to change the way in which advanced MR techniques are utilized in assessing treatment response in GBM patients. Finally, we present our institutional experiences and discuss future perspectives on the role of multiparametric MR imaging in identifying PsP in GBM patients treated with "standard-of-care" CCRT as well as novel/targeted therapies.
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Affiliation(s)
- Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sultan Bukhari
- Rowan School of Osteopathic Medicine at Rowan University, Voorhees, New Jersey, USA
| | - Omar M. Afridi
- Rowan School of Osteopathic Medicine at Rowan University, Voorhees, New Jersey, USA
| | - Sumei Wang
- Department of Cardiology, Lenox Hill Hospital, Northwell Health, New York, New York, USA
| | - Santosh K. Yadav
- Laboratory of Functional and Molecular Imaging, Sidra Medicine, Doha, Qatar
| | - Hamed Akbari
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Gaurav Verma
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Kavindra Nath
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mohammad Haris
- Laboratory of Functional and Molecular Imaging, Sidra Medicine, Doha, Qatar
| | - Stephen Bagley
- Department of Hematology-Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laurie A. Loevner
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Huang H, Wang FF, Luo S, Chen G, Tang G. Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis. DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY (ANKARA, TURKEY) 2021; 27:716-724. [PMID: 34792025 DOI: 10.5152/dir.2021.21153] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE We aimed to assess the diagnostic performance of radiomics using machine learning algorithms to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in glioma patients. METHODS A comprehensive literature search of PubMed, EMBASE, and Web of Science until 27 July 2021 was performed to identify eligible studies. Stata SE 15.0 and Meta-Disc 1.4 were used for data analysis. RESULTS A total of fifteen studies with 1663 patients were included: five studies with training and validation cohorts and ten with only training cohorts. The pooled sensitivity and specificity of machine learning for predicting MGMT promoter methylation in gliomas were 85% (95% CI 79%-90%) and 84% (95% CI 78%-88%) in the training cohort (n=15) and 84% (95% CI 70%-92%) and 78% (95% CI 63%-88%) in the validation cohort (n=5). The AUC was 0.91 (95% CI 0.88-0.93) in the training cohort and 0.88 (95% CI 0.85-0.91) in the validation cohort. The meta-regression demonstrated that magnetic resonance imaging sequences were related to heterogeneity. The sensitivity analysis showed that heterogeneity was reduced by excluding one study with the lowest diagnostic performance. CONCLUSION This meta-analysis demonstrated that machine learning is a promising, reliable and repeatable candidate method for predicting MGMT promoter methylation status in glioma and showed a higher performance than non-machine learning methods.
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Affiliation(s)
- Huan Huang
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China
| | - Fei-Fei Wang
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China
| | - Shigang Luo
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China
| | - Guangxiang Chen
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China
| | - Guangcai Tang
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China
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Coolens C, Gwilliam MN, Alcaide-Leon P, de Freitas Faria IM, Ynoe de Moraes F. Transformational Role of Medical Imaging in (Radiation) Oncology. Cancers (Basel) 2021; 13:cancers13112557. [PMID: 34070984 PMCID: PMC8197089 DOI: 10.3390/cancers13112557] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Onboard, imaging techniques have brought about a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables us to better visualize where to deliver lethal doses of radiation and thus allows the shrinking of necessary geometric margins leading to reduced toxicities. Alongside improvements in treatment delivery, advances in medical imaging have also allowed us to better define the volumes we wish to target. The development of imaging techniques that can capture aspects of the tumor’s biology before, during and after therapy is transforming how treatment can be delivered. Technological changes have further made these biological imaging techniques available in real-time providing the opportunity to monitor a patient’s response to treatment closely and often before any volume changes are visible on conventional radiological images. Here we discuss the development of robust quantitative imaging biomarkers and how they can personalize therapy towards meaningful clinical endpoints. Abstract Onboard, real-time, imaging techniques, from the original megavoltage planar imaging devices, to the emerging combined MRI-Linear Accelerators, have brought a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables lethal doses of radiation to be delivered to target volumes with progressively more accuracy and thus allows shrinking of necessary geometric margins, leading to reduced toxicities. Alongside these improvements in treatment delivery, advances in medical imaging, e.g., PET, and MRI, have also allowed target volumes themselves to be better defined. The development of functional and molecular imaging is now driving a conceptually larger step transformation to both better understand the cancer target and disease to be treated, as well as how tumors respond to treatment. A biological description of the tumor microenvironment is now accepted as an essential component of how to personalize and adapt treatment. This applies not only to radiation oncology but extends widely in cancer management from surgical oncology planning and interventional radiology, to evaluation of targeted drug delivery efficacy in medical oncology/immunotherapy. Here, we will discuss the role and requirements of functional and metabolic imaging techniques in the context of brain tumors and metastases to reliably provide multi-parametric imaging biomarkers of the tumor microenvironment.
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Affiliation(s)
- Catherine Coolens
- Department of Medical Physics, Princess Margaret Cancer Centre & University Health Network, Toronto, ON M5G 1Z5, Canada;
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- TECHNA Institute, University Health Network, Toronto, ON M5G 1Z5, Canada
- Correspondence:
| | - Matt N. Gwilliam
- Department of Medical Physics, Princess Margaret Cancer Centre & University Health Network, Toronto, ON M5G 1Z5, Canada;
| | - Paula Alcaide-Leon
- Joint Department of Medical Imaging, University Health Network, Toronto, ON M5G 1Z5, Canada;
| | | | - Fabio Ynoe de Moraes
- Department of Oncology, Division of Radiation Oncology, Queen’s University, Kingston, ON K7L 5P9, Canada;
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7
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Gihr G, Horvath-Rizea D, Hekeler E, Ganslandt O, Henkes H, Hoffmann KT, Scherlach C, Schob S. Diffusion weighted imaging in high-grade gliomas: A histogram-based analysis of apparent diffusion coefficient profile. PLoS One 2021; 16:e0249878. [PMID: 33857203 PMCID: PMC8049265 DOI: 10.1371/journal.pone.0249878] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 03/26/2021] [Indexed: 12/15/2022] Open
Abstract
Purpose Glioblastoma and anaplastic astrocytoma represent the most commonly encountered high-grade-glioma (HGG) in adults. Although both neoplasms are very distinct entities in context of epidemiology, clinical course and prognosis, their appearance in conventional magnetic resonance imaging (MRI) is very similar. In search for additional information aiding the distinction of potentially confusable neoplasms, histogram analysis of apparent diffusion coefficient (ADC) maps recently proved to be auxiliary in a number of entities. Therefore, our present exploratory retrospective study investigated whether ADC histogram profile parameters differ significantly between anaplastic astrocytoma and glioblastoma, reflect the proliferation index Ki-67, or are associated with the prognostic relevant MGMT (methylguanine-DNA methyl-transferase) promotor methylation status. Methods Pre-surgical ADC volumes of 56 HGG patients were analyzed by histogram-profiling. Association between extracted histogram parameters and neuropathology including WHO-grade, Ki-67 expression and MGMT promotor methylation status was investigated due to comparative and correlative statistics. Results Grade IV gliomas were more heterogeneous than grade III tumors. More specifically, ADCmin and the lowest percentile ADCp10 were significantly lower, whereas ADCmax, ADC standard deviation and Skewness were significantly higher in the glioblastoma group. ADCmin, ADCmax, ADC standard deviation, Kurtosis and Entropy of ADC histogram were significantly correlated with Ki-67 expression. No significant difference could be revealed by comparison of ADC histogram parameters between MGMT promotor methylated and unmethylated HGG. Conclusions ADC histogram parameters differ significantly between glioblastoma and anaplastic astrocytoma and show distinct associations with the proliferative activity in both HGG. Our results suggest ADC histogram profiling as promising biomarker for differentiation of both, however, further studies with prospective multicenter design are wanted to confirm and further elaborate this hypothesis.
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Affiliation(s)
- Georg Gihr
- Clinic for Neuroradiology, Katharinenhospital Stuttgart, Stuttgart, Germany
- * E-mail:
| | | | - Elena Hekeler
- Department for Pathology, Katharinenhospital Stuttgart, Stuttgart, Germany
| | - Oliver Ganslandt
- Clinic for Neurosurgery, Katharinenhospital Stuttgart, Stuttgart, Germany
| | - Hans Henkes
- Clinic for Neuroradiology, Katharinenhospital Stuttgart, Stuttgart, Germany
| | - Karl-Titus Hoffmann
- Department for Neuroradiology, University Hospital Leipzig, Leipzig, Germany
| | - Cordula Scherlach
- Department for Neuroradiology, University Hospital Leipzig, Leipzig, Germany
| | - Stefan Schob
- Department for Radiology, University Hospital Halle (Saale), Halle (Saale), Germany
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8
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Schell M, Pflüger I, Brugnara G, Isensee F, Neuberger U, Foltyn M, Kessler T, Sahm F, Wick A, Nowosielski M, Heiland S, Weller M, Platten M, Maier-Hein KH, Von Deimling A, Van Den Bent MJ, Gorlia T, Wick W, Bendszus M, Kickingereder P. Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial. Neuro Oncol 2020; 22:1667-1676. [PMID: 32393964 PMCID: PMC7690360 DOI: 10.1093/neuonc/noaa120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND This study validated a previously described diffusion MRI phenotype as a potential predictive imaging biomarker in patients with recurrent glioblastoma receiving bevacizumab (BEV). METHODS A total of 396/596 patients (66%) from the prospective randomized phase II/III EORTC-26101 trial (with n = 242 in the BEV and n = 154 in the non-BEV arm) met the inclusion criteria with availability of anatomical and diffusion MRI sequences at baseline prior treatment. Apparent diffusion coefficient (ADC) histograms from the contrast-enhancing tumor volume were fitted to a double Gaussian distribution and the mean of the lower curve (ADClow) was used for further analysis. The predictive ability of ADClow was assessed with biomarker threshold models and multivariable Cox regression for overall survival (OS) and progression-free survival (PFS). RESULTS ADClow was associated with PFS (hazard ratio [HR] = 0.625, P = 0.007) and OS (HR = 0.656, P = 0.031). However, no (predictive) interaction between ADClow and the treatment arm was present (P = 0.865 for PFS, P = 0.722 for OS). Independent (prognostic) significance of ADClow was retained after adjusting for epidemiological, clinical, and molecular characteristics (P ≤ 0.02 for OS, P ≤ 0.01 PFS). The biomarker threshold model revealed an optimal ADClow cutoff of 1241*10-6 mm2/s for OS. Thereby, median OS for BEV-patients with ADClow ≥ 1241 was 10.39 months versus 8.09 months for those with ADClow < 1241 (P = 0.004). Similarly, median OS for non-BEV patients with ADClow ≥ 1241 was 9.80 months versus 7.79 months for those with ADClow < 1241 (P = 0.054). CONCLUSIONS ADClow is an independent prognostic parameter for stratifying OS and PFS in patients with recurrent glioblastoma. Consequently, the previously suggested role of ADClow as predictive imaging biomarker could not be confirmed within this phase II/III trial.
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Affiliation(s)
- Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Irada Pflüger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Foltyn
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Kessler
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | - Antje Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Nowosielski
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
- Department of Neurology, Medical University, Innsbruck, Austria
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Michael Platten
- Department of Neurology, Mannheim Medical Center, University of Heidelberg, Mannheim, Germany
| | - Klaus H Maier-Hein
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Department of Neurology, Mannheim Medical Center, University of Heidelberg, Mannheim, Germany
| | - Andreas Von Deimling
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | | | - Thierry Gorlia
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Kickingereder
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
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9
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Radiomics prognostication model in glioblastoma using diffusion- and perfusion-weighted MRI. Sci Rep 2020; 10:4250. [PMID: 32144360 PMCID: PMC7060336 DOI: 10.1038/s41598-020-61178-w] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 02/19/2020] [Indexed: 01/30/2023] Open
Abstract
We aimed to develop and validate a multiparametric MR radiomics model using conventional, diffusion-, and perfusion-weighted MR imaging for better prognostication in patients with newly diagnosed glioblastoma. A total of 216 patients with newly diagnosed glioblastoma were enrolled from two tertiary medical centers and divided into training (n = 158) and external validation sets (n = 58). Radiomic features were extracted from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. After radiomic feature selection using LASSO regression, an individualized radiomic score was calculated. A multiparametric MR prognostic model was built using the radiomic score and clinical predictors. The results showed that the multiparametric MR prognostic model (radiomics score + clinical predictors) exhibited good discrimination (C-index, 0.74) and performed better than a conventional MR radiomics model (C-index, 0.65, P < 0.0001) or clinical predictors (C-index, 0.66; P < 0.0001). The multiparametric MR prognostic model also showed robustness in external validation (C-index, 0.70). Our results indicate that the incorporation of diffusion- and perfusion-weighted MR imaging into an MR radiomics model to improve prognostication in glioblastoma patients improved its performance over that achievable using clinical predictors alone.
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10
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Gihr GA, Horvath-Rizea D, Hekeler E, Ganslandt O, Henkes H, Hoffmann KT, Scherlach C, Schob S. Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: in vivo Characterization of Tumor Architecture and Corresponding Neuropathology. Front Oncol 2020; 10:206. [PMID: 32158691 PMCID: PMC7051987 DOI: 10.3389/fonc.2020.00206] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 02/06/2020] [Indexed: 02/01/2023] Open
Abstract
Background: Low-grade gliomas (LGG) in adults are usually slow growing and frequently asymptomatic brain tumors, originating from glial cells of the central nervous system (CNS). Although regarded formally as “benign” neoplasms, they harbor the potential of malignant transformation associated with high morbidity and mortality. Their complex and unpredictable tumor biology requires a reliable and conclusive presurgical magnetic resonance imaging (MRI). A promising and emerging MRI approach in this context is histogram based apparent diffusion coefficient (ADC) profiling, which recently proofed to be capable of providing prognostic relevant information in different tumor entities. Therefore, our study investigated whether histogram profiling of ADC distinguishes grade I from grade II glioma, reflects the proliferation index Ki-67, as well as the IDH (isocitrate dehydrogenase) mutation and MGMT (methylguanine-DNA methyl-transferase) promotor methylation status. Material and Methods: Pre-treatment ADC volumes of 26 LGG patients were used for histogram-profiling. WHO-grade, Ki-67 expression, IDH mutation, and MGMT promotor methylation status were evaluated. Comparative and correlative statistics investigating the association between histogram-profiling and neuropathology were performed. Results: Almost the entire ADC profile (p25, p75, p90, mean, median) was significantly lower in grade II vs. grade I gliomas. Entropy, as second order histogram parameter of ADC volumes, was significantly higher in grade II gliomas compared with grade I gliomas. Mean, maximum value (ADCmax) and the percentiles p10, p75, and p90 of ADC histogram were significantly correlated with Ki-67 expression. Furthermore, minimum ADC value (ADCmin) was significantly associated with MGMT promotor methylation status as well as ADC entropy with IDH-1 mutation status. Conclusions: ADC histogram-profiling is a valuable radiomic approach, which helps differentiating tumor grade, estimating growth kinetics and probably prognostic relevant genetic as well as epigenetic alterations in LGG.
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Affiliation(s)
| | | | - Elena Hekeler
- Department for Pathology, Katharinenhospital Stuttgart, Stuttgart, Germany
| | - Oliver Ganslandt
- Katharinenhospital Stuttgart, Clinic for Neurosurgery, Stuttgart, Germany
| | - Hans Henkes
- Katharinenhospital Stuttgart, Clinic for Neuroradiology, Stuttgart, Germany
| | - Karl-Titus Hoffmann
- Department for Neuroradiology, University Hospital Leipzig, Leipzig, Germany
| | - Cordula Scherlach
- Department for Neuroradiology, University Hospital Leipzig, Leipzig, Germany
| | - Stefan Schob
- Department for Neuroradiology, University Hospital Leipzig, Leipzig, Germany
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11
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Colip C, Oztek MA, Lo S, Yuh W, Fink J. Updates in the Neuoroimaging and WHO Classification of Primary CNS Gliomas: A Review of Current Terminology, Diagnosis, and Clinical Relevance From a Radiologic Prospective. Top Magn Reson Imaging 2019; 28:73-84. [PMID: 31022050 DOI: 10.1097/rmr.0000000000000195] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
As new advances in the genomics and imaging of CNS tumors continues to evolve, a standardized system for classification is increasingly essential to diagnosis and management. The molecular markers introduced in the 2016 WHO classification of CNS tumors bring both practical and conceptual advances to the characterization of gliomas, strengthening the prognostic and predictive value of terminology while shedding light on the underlying mechanisms that drive biologic behavior. The purpose of this article is to provide a succinct overview of primary intracranial gliomas from a neuroradiologic prospective and according to the 5th edition WHO classification that was revised in 2016. An update of the molecular markers pertinent to defining the major lineages of brain gliomas will be provided, followed by discussion of the terminology, grading and imaging features associated with individual entities. Neuroradiologists should be aware of the key genomic and radiomic features of common brain gliomas, and familiar with an integrated approach to their diagnosis and grading.
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Affiliation(s)
- Charles Colip
- University of Washington Medical Center, Department of Radiology, Seattle, WA
| | - Murat Alp Oztek
- University of Washington Medical Center, Department of Radiology, Seattle, WA
| | - Simon Lo
- University of Washington Medical Center, Department of Radiation Oncology, Seattle, WA
| | - Willam Yuh
- University of Washington Medical Center, Department of Radiology, Seattle, WA
| | - James Fink
- University of Washington Medical Center, Department of Radiology, Seattle, WA
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12
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Predictive markers for MGMT promoter methylation in glioblastomas. Neurosurg Rev 2019; 42:867-876. [DOI: 10.1007/s10143-018-01061-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 10/23/2018] [Accepted: 11/22/2018] [Indexed: 12/24/2022]
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13
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Han Y, Yan LF, Wang XB, Sun YZ, Zhang X, Liu ZC, Nan HY, Hu YC, Yang Y, Zhang J, Yu Y, Sun Q, Tian Q, Hu B, Xiao G, Wang W, Cui GB. Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis. BMC Cancer 2018; 18:215. [PMID: 29467012 PMCID: PMC5822523 DOI: 10.1186/s12885-018-4114-2] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 02/09/2018] [Indexed: 12/28/2022] Open
Abstract
Background The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter has been associated with treatment response in glioblastoma(GBM). Using pre-operative MRI techniques to predict MGMT promoter methylation status remains inconclusive. In this study, we investigated the value of features from structural and advanced imagings in predicting the methylation of MGMT promoter in primary glioblastoma patients. Methods Ninety-two pathologically confirmed primary glioblastoma patients underwent preoperative structural MR imagings and the efficacy of structural image features were qualitatively analyzed using Fisher’s exact test. In addition, 77 of the 92 patients underwent additional advanced MRI scans including diffusion-weighted (DWI) and 3-diminsional pseudo-continuous arterial spin labeling (3D pCASL) imaging. Apparent diffusion coefficient (ADC) and relative cerebral blood flow (rCBF) values within the manually drawn region-of-interest (ROI) were calculated and compared using independent sample t test for their efficacies in predicting MGMT promoter methylation. Receiver operating characteristic curve (ROC) analysis was used to investigate the predicting efficacy with the area under the curve (AUC) and cross validations. Multiple-variable logistic regression model was employed to evaluate the predicting performance of multiple variables. Results MGMT promoter methylation was associated with tumor location and necrosis (P < 0.05). Significantly increased ADC value (P < 0.001) and decreased rCBF (P < 0.001) were associated with MGMT promoter methylation in primary glioblastoma. The ADC achieved the better predicting efficacy than rCBF (ADC: AUC, 0.860; sensitivity, 81.1%; specificity, 82.5%; vs rCBF: AUC, 0.835; sensitivity, 75.0%; specificity, 78.4%; P = 0.032). The combination of tumor location, necrosis, ADC and rCBF resulted in the highest AUC of 0.914. Conclusion ADC and rCBF are promising imaging biomarkers in clinical routine to predict the MGMT promoter methylation in primary glioblastoma patients.
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Affiliation(s)
- Yu Han
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Lin-Feng Yan
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Xi-Bin Wang
- Department of Medical Image Diagnosis, Hanzhong Central Hospital, Hanzhong, Shaanxi, 723000, China
| | - Ying-Zhi Sun
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Xin Zhang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Zhi-Cheng Liu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Hai-Yan Nan
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Yu-Chuan Hu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Yang Yang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Jin Zhang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Ying Yu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Qian Sun
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Qiang Tian
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Bo Hu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Gang Xiao
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China
| | - Wen Wang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China.
| | - Guang-Bin Cui
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University), 569 Xinsi Road, Xi'an, 710038, China.
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14
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Abstract
Radiogenomics is a relatively new and exciting field within radiology that links different imaging features with diverse genomic events. Genomics advances provided by the Cancer Genome Atlas and the Human Genome Project have enabled us to harness and integrate this information with noninvasive imaging phenotypes to create a better 3-dimensional understanding of tumor behavior and biology. Beyond imaging-histopathology, imaging genomic linkages provide an important layer of complexity that can help in evaluating and stratifying patients into clinical trials, monitoring treatment response, and enhancing patient outcomes. This article reviews some of the important radiogenomic literatures in brain tumors.
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15
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Abstract
Primary brain tumors, most commonly gliomas, are histopathologically typed and graded as World Health Organization (WHO) grades I-IV according to increasing degrees of malignancy. These grades provide prognostic information and guidance on treatment such as radiation therapy and chemotherapy after surgery. Despite the confirmed value of the WHO grading system, results of a multitude of studies and prospective interventional trials now indicate that tumors with identical morphologic criteria can have highly different outcomes. Molecular markers can allow subtypes of tumors of the same morphologic type and WHO grade to be distinguished and are, therefore, of great interest in personalization of brain tumor treatment. Recent genomic-wide studies have resulted in a far more comprehensive understanding of the genomic alterations in gliomas and provide suggestions for a new molecularly based classification. Magnetic resonance (MR) imaging phenotypes can serve as noninvasive surrogates for tumor genotypes and can provide important information for diagnosis, prognosis, and, eventually, personalized treatment. The newly emerged field of radiogenomics allows specific MR imaging phenotypes to be linked with gene expression profiles. In this article, the authors review the conventional and advanced imaging features of three tumoral genotypes with prognostic and therapeutic consequences: (a) isocitrate dehydrogenase mutation; (b) the combined loss of the short arm of chromosome 1 and the long arm of chromosome 19, or 1p19q codeletion; and (c) methylguanine methyltransferase promoter methylation. © RSNA, 2017.
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Affiliation(s)
- Marion Smits
- From the Department of Radiology, Erasmus MC University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, the Netherlands (M.S.); and Brain Tumor Center, Erasmus MC Cancer Center, Rotterdam, the Netherlands (M.J.v.d.B.)
| | - Martin J van den Bent
- From the Department of Radiology, Erasmus MC University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, the Netherlands (M.S.); and Brain Tumor Center, Erasmus MC Cancer Center, Rotterdam, the Netherlands (M.J.v.d.B.)
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16
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Cui Y, Ren S, Tha KK, Wu J, Shirato H, Li R. Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. Eur Radiol 2017; 27:3583-3592. [PMID: 28168370 DOI: 10.1007/s00330-017-4751-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 01/10/2017] [Accepted: 01/16/2017] [Indexed: 10/25/2022]
Abstract
OBJECTIVE To develop and validate a volume-based, quantitative imaging marker by integrating multi-parametric MR images for predicting glioblastoma survival, and to investigate its relationship and synergy with molecular characteristics. METHODS We retrospectively analysed 108 patients with primary glioblastoma. The discovery cohort consisted of 62 patients from the cancer genome atlas (TCGA). Another 46 patients comprising 30 from TCGA and 16 internally were used for independent validation. Based on integrated analyses of T1-weighted contrast-enhanced (T1-c) and diffusion-weighted MR images, we identified an intratumoral subregion with both high T1-c and low ADC, and accordingly defined a high-risk volume (HRV). We evaluated its prognostic value and biological significance with genomic data. RESULTS On both discovery and validation cohorts, HRV predicted overall survival (OS) (concordance index: 0.642 and 0.653, P < 0.001 and P = 0.038, respectively). HRV stratified patients within the proneural molecular subtype (log-rank P = 0.040, hazard ratio = 2.787). We observed different OS among patients depending on their MGMT methylation status and HRV (log-rank P = 0.011). Patients with unmethylated MGMT and high HRV had significantly shorter survival (median survival: 9.3 vs. 18.4 months, log-rank P = 0.002). CONCLUSION Volume of the high-risk intratumoral subregion identified on multi-parametric MRI predicts glioblastoma survival, and may provide complementary value to genomic information. KEY POINTS • High-risk volume (HRV) defined on multi-parametric MRI predicted GBM survival. • The proneural molecular subtype tended to harbour smaller HRV than other subtypes. • Patients with unmethylated MGMT and high HRV had significantly shorter survival. • HRV complements genomic information in predicting GBM survival.
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Affiliation(s)
- Yi Cui
- Department of Radiation Oncology, Stanford University, 1070 Arastradero Rd., Palo Alto, CA, 94304, USA. .,Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, Hokkaido University, Hokkaido, Japan.
| | - Shangjie Ren
- School of Electrical Engineering and Automation, Tianjin University, Tianjin Shi, China
| | - Khin Khin Tha
- Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, Hokkaido University, Hokkaido, Japan.,Department of Radiology and Nuclear Medicine, Hokkaido University, Hokkaido, Japan
| | - Jia Wu
- Department of Radiation Oncology, Stanford University, 1070 Arastradero Rd., Palo Alto, CA, 94304, USA
| | - Hiroki Shirato
- Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, Hokkaido University, Hokkaido, Japan.,Department of Radiology and Nuclear Medicine, Hokkaido University, Hokkaido, Japan
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, 1070 Arastradero Rd., Palo Alto, CA, 94304, USA.,Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, Hokkaido University, Hokkaido, Japan
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17
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Choi YS, Ahn SS, Kim DW, Chang JH, Kang SG, Kim EH, Kim SH, Rim TH, Lee SK. Incremental Prognostic Value of ADC Histogram Analysis over MGMT Promoter Methylation Status in Patients with Glioblastoma. Radiology 2016; 281:175-84. [PMID: 27120357 DOI: 10.1148/radiol.2016151913] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To investigate the incremental prognostic value of apparent diffusion coefficient (ADC) histogram analysis over oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in patients with glioblastoma and the correlation between ADC parameters and MGMT status. Materials and Methods This retrospective study was approved by institutional review board, and informed consent was waived. A total of 112 patients with glioblastoma were divided into training (74 patients) and test (38 patients) sets. Overall survival (OS) and progression-free survival (PFS) was analyzed with ADC parameters, MGMT status, and other clinical factors. Multivariate Cox regression models with and without ADC parameters were constructed. Model performance was assessed with c index and receiver operating characteristic curve analyses for 12- and 16-month OS and 12-month PFS in the training set and validated in the test set. ADC parameters were compared according to MGMT status for the entire cohort. Results By using ADC parameters, the c indices and diagnostic accuracies for 12- and 16-month OS and 12-month PFS in the models showed significant improvement, with the exception of c indices in the models for PFS (P < .05 for all) in the training set. In the test set, the diagnostic accuracy was improved by using ADC parameters and was significant, with the 25th and 50th percentiles of ADC for 16-month OS (P = .040 and P = .047) and the 25th percentile of ADC for 12-month PFS (P = .026). No significant correlation was found between ADC parameters and MGMT status. Conclusion ADC histogram analysis had incremental prognostic value over MGMT promoter methylation status in patients with glioblastoma. (©) RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Yoon Seong Choi
- From the Department of Radiology and Research Institute of Radiological Science (Y.S.C., S.S.A., S.-K.L.), Department of Neurosurgery (J.H.C., S.-G.K., E.H.K.), Department of Pathology (S.H.K.), and Department of Ophthalmology (T.H.R.), Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; and Department of Policy Research Affairs, National Health Insurance Service Ilsan Hospital, Goyang, Korea (D.W.K.)
| | - Sung Soo Ahn
- From the Department of Radiology and Research Institute of Radiological Science (Y.S.C., S.S.A., S.-K.L.), Department of Neurosurgery (J.H.C., S.-G.K., E.H.K.), Department of Pathology (S.H.K.), and Department of Ophthalmology (T.H.R.), Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; and Department of Policy Research Affairs, National Health Insurance Service Ilsan Hospital, Goyang, Korea (D.W.K.)
| | - Dong Wook Kim
- From the Department of Radiology and Research Institute of Radiological Science (Y.S.C., S.S.A., S.-K.L.), Department of Neurosurgery (J.H.C., S.-G.K., E.H.K.), Department of Pathology (S.H.K.), and Department of Ophthalmology (T.H.R.), Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; and Department of Policy Research Affairs, National Health Insurance Service Ilsan Hospital, Goyang, Korea (D.W.K.)
| | - Jong Hee Chang
- From the Department of Radiology and Research Institute of Radiological Science (Y.S.C., S.S.A., S.-K.L.), Department of Neurosurgery (J.H.C., S.-G.K., E.H.K.), Department of Pathology (S.H.K.), and Department of Ophthalmology (T.H.R.), Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; and Department of Policy Research Affairs, National Health Insurance Service Ilsan Hospital, Goyang, Korea (D.W.K.)
| | - Seok-Gu Kang
- From the Department of Radiology and Research Institute of Radiological Science (Y.S.C., S.S.A., S.-K.L.), Department of Neurosurgery (J.H.C., S.-G.K., E.H.K.), Department of Pathology (S.H.K.), and Department of Ophthalmology (T.H.R.), Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; and Department of Policy Research Affairs, National Health Insurance Service Ilsan Hospital, Goyang, Korea (D.W.K.)
| | - Eui Hyun Kim
- From the Department of Radiology and Research Institute of Radiological Science (Y.S.C., S.S.A., S.-K.L.), Department of Neurosurgery (J.H.C., S.-G.K., E.H.K.), Department of Pathology (S.H.K.), and Department of Ophthalmology (T.H.R.), Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; and Department of Policy Research Affairs, National Health Insurance Service Ilsan Hospital, Goyang, Korea (D.W.K.)
| | - Se Hoon Kim
- From the Department of Radiology and Research Institute of Radiological Science (Y.S.C., S.S.A., S.-K.L.), Department of Neurosurgery (J.H.C., S.-G.K., E.H.K.), Department of Pathology (S.H.K.), and Department of Ophthalmology (T.H.R.), Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; and Department of Policy Research Affairs, National Health Insurance Service Ilsan Hospital, Goyang, Korea (D.W.K.)
| | - Tyler Hyungtaek Rim
- From the Department of Radiology and Research Institute of Radiological Science (Y.S.C., S.S.A., S.-K.L.), Department of Neurosurgery (J.H.C., S.-G.K., E.H.K.), Department of Pathology (S.H.K.), and Department of Ophthalmology (T.H.R.), Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; and Department of Policy Research Affairs, National Health Insurance Service Ilsan Hospital, Goyang, Korea (D.W.K.)
| | - Seung-Koo Lee
- From the Department of Radiology and Research Institute of Radiological Science (Y.S.C., S.S.A., S.-K.L.), Department of Neurosurgery (J.H.C., S.-G.K., E.H.K.), Department of Pathology (S.H.K.), and Department of Ophthalmology (T.H.R.), Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; and Department of Policy Research Affairs, National Health Insurance Service Ilsan Hospital, Goyang, Korea (D.W.K.)
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18
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Abstract
Imaging is integral to the management of patients with brain tumors. Conventional structural imaging provides exquisite anatomic detail but remains limited in the evaluation of molecular characteristics of intracranial neoplasms. Quantitative and physiologic biomarkers derived from advanced imaging techniques have been increasingly utilized as problem-solving tools to identify glioma grade and assess response to therapy. This chapter provides a comprehensive overview of the imaging strategies used in the clinical assessment of patients with gliomas and describes how novel imaging biomarkers have the potential to improve patient management.
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Affiliation(s)
- Whitney B Pope
- Radiological Sciences, Ronald Reagan Medical Center, Los Angeles, CA, USA.
| | - Ibrahim Djoukhadar
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
| | - Alan Jackson
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
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