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Lee MD, Patel SH, Mohan S, Akbari H, Bakas S, Nasrallah MP, Calabrese E, Rudie J, Villanueva-Meyer J, LaMontagne P, Marcus DS, Colen RR, Balana C, Choi YS, Badve C, Barnholtz-Sloan JS, Sloan AE, Booth TC, Palmer JD, Dicker AP, Flanders AE, Shi W, Griffith B, Poisson LM, Chakravarti A, Mahajan A, Chang S, Orringer D, Davatzikos C, Jain R. Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium. Neuroradiology 2023; 65:1343-1352. [PMID: 37468750 PMCID: PMC11058040 DOI: 10.1007/s00234-023-03196-9] [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: 04/14/2023] [Accepted: 07/07/2023] [Indexed: 07/21/2023]
Abstract
PURPOSE While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas. METHODS Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS). RESULTS One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS. CONCLUSION Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.
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Affiliation(s)
- Matthew D Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA.
| | - Sohil H Patel
- Department of Radiology, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Suyash Mohan
- Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Glioblastoma Multiforme Translational Center of Excellence, Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Evan Calabrese
- Department of Radiology, Division of Neuroradiology, Duke University, Durham, NC, USA
| | - Jeffrey Rudie
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Rivka R Colen
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Carmen Balana
- Medical Oncology Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Yoon Seong Choi
- Department of Radiology, Section of Neuroradiology, Yonsei University Health System, Seoul, South Korea
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, OH, USA
| | - Jill S Barnholtz-Sloan
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
- Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Andrew E Sloan
- Department of Neurosurgery, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, OH, USA
- Seidman Cancer Center and Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Thomas C Booth
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, King's College Hospital NHS Foundation Trust, Ruskin WingLondon, UK
| | - Joshua D Palmer
- Department of Radiation Oncology and Neurosurgery, The James Cancer Hospital at the Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Wenyin Shi
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Brent Griffith
- Department of Radiology, Henry Ford Health, Detroit, MI, USA
| | - Laila M Poisson
- Department of Public Health Sciences, Center for Bioinformatics, Henry Ford Health, Detroit, MI, USA
| | - Arnab Chakravarti
- Department of Radiation Oncology and Neurosurgery, The James Cancer Hospital at the Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK
| | - Susan Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Orringer
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
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Cao L, Zhang M, Zhang Y, Ji B, Wang X, Wang X. Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review). Oncol Rep 2022; 49:20. [PMID: 36484403 PMCID: PMC9773014 DOI: 10.3892/or.2022.8457] [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: 06/24/2022] [Accepted: 11/03/2022] [Indexed: 12/13/2022] Open
Abstract
Primary central nervous system lymphoma (PCNSL) and high‑grade glioma (HGG) are distinct entities of the CNS with completely distinct treatments. The treatment of PCNSL is chemotherapy‑based, while surgery is the first choice for HGG. However, the clinical features of the two entities often overlap, and a clear pathological diagnosis is important for subsequent management, especially for the management of PCNSL. Stereotactic biopsy is recognized as one of the minimally invasive alternatives for evaluating the involvement of the CNS. However, in the case of limited tissue materials, the differential diagnosis between the two entities is still difficult. In addition, some patients are too ill to tolerate a needle biopsy. Therefore, combining imaging, histopathology and laboratory examinations is essential in order to make a clear diagnosis as soon as possible. The present study reviews the progress of comparative research on both imaging and laboratory tests based on the pathophysiological changes of the two entities, and proposes an integrative and optimized diagnostic process, with the purpose of building a better understanding for neurologists, hematologists, radiologists and pathologists.
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Affiliation(s)
- Luming Cao
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Mengchao Zhang
- Department of Radiology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Ying Zhang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Bin Ji
- Department of Nuclear Medicine, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Xuemei Wang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Xueju Wang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China,Correspondence to: Dr Xueju Wang, Department of Pathology, China-Japan Union Hospital, Jilin University, 126 Xiantai Street, Changchun, Jilin 130033, P.R. China, E-mail:
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Feraco P, Franciosi R, Picori L, Scalorbi F, Gagliardo C. Conventional MRI-Derived Biomarkers of Adult-Type Diffuse Glioma Molecular Subtypes: A Comprehensive Review. Biomedicines 2022; 10:biomedicines10102490. [PMID: 36289752 PMCID: PMC9598857 DOI: 10.3390/biomedicines10102490] [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: 08/19/2022] [Revised: 09/28/2022] [Accepted: 10/02/2022] [Indexed: 11/25/2022] Open
Abstract
The introduction of molecular criteria into the classification of diffuse gliomas has added interesting practical implications to glioma management. This has created a new clinical need for correlating imaging characteristics with glioma genotypes, also known as radiogenomics or imaging genomics. Although many studies have primarily focused on the use of advanced magnetic resonance imaging (MRI) techniques for radiogenomics purposes, conventional MRI sequences remain the reference point in the study and characterization of brain tumors. A summary of the conventional imaging features of glioma molecular subtypes should be useful as a tool for daily diagnostic brain tumor management. Hence, this article aims to summarize the conventional MRI features of glioma molecular subtypes in light of the recent literature.
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Affiliation(s)
- Paola Feraco
- Neuroradiology Unit, Ospedale S. Chiara, Azienda Provinciale per i Servizi Sanitari, Largo Medaglie d’oro 9, 38122 Trento, Italy
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Via S. Giacomo 14, 40138 Bologna, Italy
- Correspondence:
| | - Rossana Franciosi
- Radiology Unit, Santa Maria del Carmine Hospital, 38068 Rovereto, Italy
| | - Lorena Picori
- Nuclear Medicine Unit, Ospedale S. Chiara, Azienda Provinciale per i Servizi Sanitari, Largo Medaglie d’oro 9, 38122 Trento, Italy
| | - Federica Scalorbi
- Nuclear Medicine Unit, Foundation IRCSS, Istituto Nazionale dei Tumori, 20121 Milan, Italy
| | - Cesare Gagliardo
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University of Palermo, Via del Vespro 129, 90127 Palermo, Italy
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Validation of MRI-Based Models to Predict MGMT Promoter Methylation in Gliomas: BraTS 2021 Radiogenomics Challenge. Cancers (Basel) 2022; 14:cancers14194827. [PMID: 36230750 PMCID: PMC9562637 DOI: 10.3390/cancers14194827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/19/2022] [Accepted: 09/30/2022] [Indexed: 12/05/2022] Open
Abstract
Simple Summary O6-methylguanine-DNA methyl transferase (MGMT) methylation in glioblastoma is an important prognostic and predictive factor that requires an invasive surgical procedure for identification. In several recent studies, MGMT methylation prediction models were developed using MR images, and good diagnostic performance was achieved, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MRI. With unexpected negative results, approximately 80% of the developed models showed no significant difference with the chance level of 50% in terms of external validation accuracy. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MRI, even using deep learning. Abstract O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MR images. Specifically, prediction models were developed and validated with different training datasets: (1) the merged (SNUH + BraTS) (n = 985); (2) SNUH (n = 400); and (3) BraTS datasets (n = 585). A total of 420 training and validation experiments were performed on combinations of datasets, convolutional neural network (CNN) architectures, MRI sequences, and random seed numbers. The first-place solution of the RSNA-MICCAI radiogenomic challenge was also validated using the external test set (SNUH). For model evaluation, the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall were obtained. With unexpected negative results, 80.2% (337/420) and 60.0% (252/420) of the 420 developed models showed no significant difference with a chance level of 50% in terms of test accuracy and test AUROC, respectively. The test AUROC and accuracy of the first-place solution of the BraTS 2021 challenge were 56.2% and 54.8%, respectively, as validated on the SNUH dataset. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MR images even using deep learning.
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He J, Ren J, Niu G, Liu A, Wu Q, Xie S, Ma X, Li B, Wang P, Shen J, Wu J, Gao Y. Multiparametric MR radiomics in brain glioma: models comparation to predict biomarker status. BMC Med Imaging 2022; 22:137. [PMID: 35931979 PMCID: PMC9354364 DOI: 10.1186/s12880-022-00865-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 07/28/2022] [Indexed: 11/10/2022] Open
Abstract
Background Genotype status of glioma have important significance to clinical treatment and prognosis. At present, there are few studies on the prediction of multiple genotype status in glioma by method of multi-sequence radiomics. The purpose of the study is to compare the performance of clinical features (age, sex, WHO grade, MRI morphological features etc.), radiomics features from multi MR sequence (T2WI, T1WI, DWI, ADC, CE-MRI (contrast enhancement)), and a combined multiple features model in predicting biomarker status (IDH, MGMT, TERT, 1p/19q of glioma. Methods In this retrospective analysis, 81 glioma patients confirmed by histology were enrolled in this study. Five MRI sequences were used for radiomic feature extraction. Finally, 107 features were extracted from each sequence on Pyradiomics software, separately. These included 18 first-order metrics, such as the mean, standard deviation, skewness, and kurtosis etc., 14 shape features and second-order metrics including 24 grey level run length matrix (GLCM), 16 grey level run length matrix (GLRLM), 16 grey level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix (NGTDM), and 14 grey level dependence matrix (GLDM). Then, Univariate analysis and LASSO (Least absolute shrinkage and selection operator regression model were used to data dimension reduction, feature selection, and radiomics signature building. Significant features (p < 0.05 by multivariate logistic regression were retained to establish clinical model, T1WI model, T2WI model, T1 + C (T1WI contrast enhancement model, DWI model and ADC model, multi sequence model. Clinical features were combined with multi sequence model to establish a combined model. The predictive performance was validated by receiver operating characteristic curve (ROC analysis and decision curve analysis (DCA). Results The combined model showed the better performance in some groups of genotype status among some models (IDH AUC = 0.93, MGMT AUC = 0.88, TERT AUC = 0.76). Multi sequence model performed better than single sequence model in IDH, MGMT, TERT. There was no significant difference among the models in predicting 1p/19q status. Decision curve analysis showed combined model has higher clinical benefit than multi sequence model. Conclusion Multi sequence model is an effective method to identify the genotype status of cerebral glioma. Combined with clinical models can better distinguish genotype status of glioma. Key Points The combined model showed the higher performance compare with other models in predicting genotype status of IDH, MGMT, TERT. Multi sequence model showed a better predictive model than that of a single sequence model. Compared with other models, the combined model and multi sequence model show no advantage in prediction of 1p/19q status.
Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00865-8.
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Affiliation(s)
- Jinlong He
- Graduate School, Tianjin Medical University, Tianjin, 300070, China.,Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Jialiang Ren
- GE Healthcare Co., Ltd., Shanghai, 210000, China
| | - Guangming Niu
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Aishi Liu
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Qiong Wu
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Shenghui Xie
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Xueying Ma
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Bo Li
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Peng Wang
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Jing Shen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China.
| | - Yang Gao
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China.
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Springer E, Cardoso PL, Strasser B, Bogner W, Preusser M, Widhalm G, Nittka M, Koerzdoerfer G, Szomolanyi P, Hangel G, Hainfellner JA, Marik W, Trattnig S. MR Fingerprinting-A Radiogenomic Marker for Diffuse Gliomas. Cancers (Basel) 2022; 14:cancers14030723. [PMID: 35158990 PMCID: PMC8833555 DOI: 10.3390/cancers14030723] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/22/2022] [Accepted: 01/28/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Advanced MR imaging (MRI) of brain tumors is mainly based on qualitative contrast images. MR Fingerprinting (MRF) offers a novel approach. The purpose of this study was to use MRF-derived T1 and T2 relaxation maps to differentiate diffuse gliomas according to isocitrate dehydrogenase (IDH) mutation. (2) Methods: Twenty-four patients with histologically verified diffuse gliomas (14 IDH-mutant, four 1p/19q-codeleted, 10 IDH-wildtype) were enrolled. MRF T1 and T2 relaxation times were compared to apparent diffusion coefficient (ADC), relative cerebral blood volume (rCBV) within solid tumor, peritumoral edema, and normal-appearing white matter (NAWM), using contrast-enhanced MRI, diffusion-, perfusion-, and susceptibility-weighted imaging. For perfusion imaging, a T2* weighted perfusion sequence with leakage correction was used. Correlations of MRF T1 and T2 times with two established conventional sequences for T1 and T2 mapping were assessed (a fast double inversion recovery-based MR sequence ('MP2RAGE') for T1 quantification and a multi-contrast spin echo-based sequence for T2 quantification). (3) Results: MRF T1 and T2 relaxation times were significantly higher in the IDH-mutant than in IDH-wildtype gliomas within the solid part of the tumor (p = 0.024 for MRF T1, p = 0.041 for MRF T2). MRF T1 and T2 relaxation times were significantly higher in the IDH-wildtype than in IDH-mutant gliomas within peritumoral edema less than or equal to 1cm adjacent to the tumor (p = 0.038 for MRF T1 mean, p = 0.010 for MRF T2 mean). In the solid part of the tumor, there was a high correlation between MRF and conventionally measured T1 and T2 values (r = 0.913, p < 0.001 for T1, r = 0.775, p < 0.001 for T2), as well as between MRF and ADC values (r = 0.813, p < 0.001 for T2, r = 0.697, p < 0.001 for T1). The correlation was weak between the MRF and rCBV values (r = -0.374, p = 0.005 for T2, r = -0.181, p = 0.181 for T1). (4) Conclusions: MRF enables fast, single-sequence based, multi-parametric, quantitative tissue characterization of diffuse gliomas and may have the potential to differentiate IDH-mutant from IDH-wildtype gliomas.
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Affiliation(s)
- Elisabeth Springer
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Institute of Radiology, Hietzing Hospital, 1130 Vienna, Austria
| | - Pedro Lima Cardoso
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
| | - Bernhard Strasser
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
| | - Wolfgang Bogner
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Correspondence: ; Tel.: +431-40-400-64710
| | - Matthias Preusser
- Division of Oncology, Department of Internal Medicine I, Medical University of Vienna, 1090 Vienna, Austria;
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria;
| | - Mathias Nittka
- Siemens Healthineers, 91052 Erlangen, Germany; (M.N.); (G.K.)
| | | | - Pavol Szomolanyi
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Department of Imaging Methods, Institute of Measurement Science, Slovak Academy of Sciences, 84104 Bratislava, Slovakia
| | - Gilbert Hangel
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria;
| | - Johannes A. Hainfellner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria;
| | - Wolfgang Marik
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria;
| | - Siegfried Trattnig
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Medical University of Vienna, 1090 Vienna, Austria
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Yogananda CGB, Shah BR, Nalawade SS, Murugesan GK, Yu FF, Pinho MC, Wagner BC, Mickey B, Patel TR, Fei B, Madhuranthakam AJ, Maldjian JA. MRI-Based Deep-Learning Method for Determining Glioma MGMT Promoter Methylation Status. AJNR Am J Neuroradiol 2021; 42:845-852. [PMID: 33664111 PMCID: PMC8115363 DOI: 10.3174/ajnr.a7029] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/21/2020] [Indexed: 01/14/2023]
Abstract
BACKGROUND AND PURPOSE O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation confers an improved prognosis and treatment response in gliomas. We developed a deep learning network for determining MGMT promoter methylation status using T2 weighted Images (T2WI) only. MATERIALS AND METHODS Brain MR imaging and corresponding genomic information were obtained for 247 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. One hundred sixty-three subjects had a methylated MGMT promoter. A T2WI-only network (MGMT-net) was developed to determine MGMT promoter methylation status and simultaneous single-label tumor segmentation. The network was trained using 3D-dense-UNets. Three-fold cross-validation was performed to generalize the performance of the networks. Dice scores were computed to determine tumor-segmentation accuracy. RESULTS The MGMT-net demonstrated a mean cross-validation accuracy of 94.73% across the 3 folds (95.12%, 93.98%, and 95.12%, [SD, 0.66%]) in predicting MGMT methylation status with a sensitivity and specificity of 96.31% [SD, 0.04%] and 91.66% [SD, 2.06%], respectively, and a mean area under the curve of 0.93 [SD, 0.01]. The whole tumor-segmentation mean Dice score was 0.82 [SD, 0.008]. CONCLUSIONS We demonstrate high classification accuracy in predicting MGMT promoter methylation status using only T2WI. Our network surpasses the sensitivity, specificity, and accuracy of histologic and molecular methods. This result represents an important milestone toward using MR imaging to predict prognosis and treatment response.
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Affiliation(s)
- C G B Yogananda
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - B R Shah
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - S S Nalawade
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - G K Murugesan
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - F F Yu
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - M C Pinho
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - B C Wagner
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - B Mickey
- Department of Neurological Surgery (B.M., T.R.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - T R Patel
- Department of Neurological Surgery (B.M., T.R.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - B Fei
- Department of Bioengineering (B.F.), University of Texas at Dallas, Richardson, Texas
| | - A J Madhuranthakam
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - J A Maldjian
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
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Association between MGMT status and response to alkylating agents in patients with neuroendocrine neoplasms: a systematic review and meta-analysis. Biosci Rep 2021; 40:222307. [PMID: 32141507 PMCID: PMC7098124 DOI: 10.1042/bsr20194127] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 02/28/2020] [Accepted: 03/01/2020] [Indexed: 12/28/2022] Open
Abstract
Background: O6-methylguanine-DNA methyltransferase (MGMT) is a specific DNA damage reversal repair protein. The influence of MGMT status on alkylating agent sensitivity in patients with neuroendocrine neoplasms (NENs) is controversial. We conducted a meta-analysis to assess the influence of MGMT status on the therapeutic sensitivity of alkylating agents in patients with NENs. Methods: We searched PubMed, EmBase, and Cochrane library public databases through 3 July 2019. The objective response rate (ORR) was the outcome data of interest. Subgroup analysis was performed according based on MGMT methylation and expression of MGMT protein. Results: Eleven studies were included in the meta-analysis. The proportion of patients with NENs that achieved an ORR after alkylating agent treatment was higher in the MGMT-deficient group than the non-deficient group (OR: 5.00; 95% CI: 3.04–8.22; P < 0.001; I2: 3%). Similar results were noted in the MGMT methylation and MGMT protein expression subgroups. Conclusion: Patients with NENs and MGMT methylation or low protein expression had a higher ORR proportion than patients without MGMT methylation or high protein expression. The MGMT status can be used as a biological indicator of the response to alkylating agent treatment in patients with NENs.
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Patel SH, Batchala PP, Muttikkal TJE, Ferrante SS, Patrie JT, Fadul CE, Schiff D, Lopes MB, Jain R. Fluid attenuation in non-contrast-enhancing tumor (nCET): an MRI Marker for Isocitrate Dehydrogenase (IDH) mutation in Glioblastoma. J Neurooncol 2021; 152:523-531. [PMID: 33661425 DOI: 10.1007/s11060-021-03720-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/12/2021] [Accepted: 02/15/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE The WHO 2016 update classifies glioblastomas (WHO grade IV) according to isocitrate dehydrogenase (IDH) gene mutation status. We aimed to determine MRI-based metrics for predicting IDH mutation in glioblastoma. METHODS This retrospective study included glioblastoma cases (n = 199) with known IDH mutation status and pre-operative MRI (T1WI, T2WI, FLAIR, contrast-enhanced T1W1 at minimum). Two neuroradiologists determined the following MRI metrics: (1) primary lobe of involvement (frontal or non-frontal); (2) presence/absence of contrast-enhancement; (3) presence/absence of necrosis; (4) presence/absence of fluid attenuation in the non-contrast-enhancing tumor (nCET); (5) maximum width of peritumoral edema (cm); (6) presence/absence of multifocal disease. Inter-reader agreement was determined. After resolving discordant measurements, multivariate association between consensus MRI metrics/patient age and IDH mutation status was determined. RESULTS Among 199 glioblastomas, 16 were IDH-mutant. Inter-reader agreement was calculated for contrast-enhancement (ĸ = 0.49 [- 0.11-1.00]), necrosis (ĸ = 0.55 [0.34-0.76]), fluid attenuation in nCET (ĸ = 0.83 [0.68-0.99]), multifocal disease (ĸ = 0.55 [0.39-0.70]), and primary lobe (ĸ = 0.85 [0.80-0.91]). Mean difference for peritumoral edema width between readers was 0.3 cm [0.2-0.5], p < 0.001. Multivariate analysis uncovered significant associations between IDH-mutation and fluid attenuation in nCET (OR 82.9 [19.22, ∞], p < 0.001), younger age (OR 0.93 [0.86, 0.98], p = 0.009), frontal lobe location (OR 11.08 [1.14, 352.97], p = 0.037), and less peritumoral edema (OR 0.15 [0, 0.65], p = 0.044). CONCLUSIONS Conventional MRI metrics and patient age predict IDH-mutation status in glioblastoma. Among MRI markers, fluid attenuation in nCET represents a novel marker with high inter-reader agreement that is strongly associated with Glioblastoma, IDH-mutant.
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Affiliation(s)
- Sohil H Patel
- Department of Radiology and Medical Imaging, University of Virginia Health System, PO Box 800170, Charlottesville, VA, 22908, USA.
| | - Prem P Batchala
- Department of Radiology and Medical Imaging, University of Virginia Health System, PO Box 800170, Charlottesville, VA, 22908, USA
| | - Thomas J Eluvathingal Muttikkal
- Department of Radiology and Medical Imaging, University of Virginia Health System, PO Box 800170, Charlottesville, VA, 22908, USA
| | - Sergio S Ferrante
- Department of Radiology and Medical Imaging, University of Virginia Health System, PO Box 800170, Charlottesville, VA, 22908, USA
| | - James T Patrie
- Department of Public Health Sciences, University of Virginia Health System, Charlottesville, VA, USA
| | - Camilo E Fadul
- Division of Neuro-Oncology, Department of Neurology, University of Virginia Health System, Charlottesville, VA, USA
| | - David Schiff
- Division of Neuro-Oncology, Department of Neurology, University of Virginia Health System, Charlottesville, VA, USA
| | - M Beatriz Lopes
- Department of Pathology, Divisions of Neuropathology and Molecular Diagnostics, University of Virginia Health System, Charlottesville, VA, USA
| | - Rajan Jain
- Department of Radiology, New York University School of Medicine, 550 1st Avenue, New York, NY, 10016, USA.,Department of Neurosurgery, New York University School of Medicine, 550 1st Avenue, New York, NY, 10016, USA
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10
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Choi HJ, Choi SH, You SH, Yoo RE, Kang KM, Yun TJ, Kim JH, Sohn CH, Park CK, Park SH. MGMT Promoter Methylation Status in Initial and Recurrent Glioblastoma: Correlation Study with DWI and DSC PWI Features. AJNR Am J Neuroradiol 2021; 42:853-860. [PMID: 33632732 DOI: 10.3174/ajnr.a7004] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 11/16/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in primary and recurrent glioblastoma may change during treatment. The purpose of this study was to correlate MGMT promoter methylation status changes with DWI and DSC PWI features in patients with recurrent glioblastoma after standard treatment. MATERIALS AND METHODS Between January 2008 and November 2016, forty patients with histologically confirmed recurrent glioblastoma were enrolled. Patients were divided into 3 groups according to the MGMT promoter methylation status for the initial and recurrent tumors: 2 groups whose MGMT promoter methylation status remained, group methylated (n = 13) or group unmethylated (n = 18), and 1 group whose MGMT promoter methylation status changed from methylated to unmethylated (n = 9). Normalized ADC and normalized relative CBV values were obtained from both the enhancing and nonenhancing regions, from which histogram parameters were calculated. The ANOVA and the Kruskal-Wallis test followed by post hoc tests were performed to compare histogram parameters among the 3 groups. The t test and Mann-Whitney U test were used to compare parameters between group methylated and group methylated to unmethylated. Receiver operating characteristic curve analysis was used to measure the predictive performance of the normalized relative CBV values between the 2 groups. RESULTS Group methylated to unmethylated showed significantly higher means and 90th and 95th percentiles of the cumulative normalized relative CBV values of the nonenhancing region of the initial tumor than group methylated and group unmethylated (all P < .05). The mean normalized relative CBV value of the nonenhancing region of the initial tumor was the best predictor of methylation status change (P < .001), with a sensitivity of 77.78% and specificity of 92.31% at a cutoff value of 2.594. CONCLUSIONS MGMT promoter methylation status might change in recurrent glioblastoma after standard treatment. The normalized relative CBV values of the nonenhancing region at the first preoperative MR imaging were higher in the MGMT promoter methylation change group from methylation to unmethylation in recurrent glioblastoma.
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Affiliation(s)
- H J Choi
- From the Department of Radiology (H.J.C.), Cha Bundang Medical Center, Cha University, Seongnam, Korea
| | - S H Choi
- Department of Radiology (S.H.C., R.-E.Y., K.M.K., T.J.Y., J.-h.K., C.-H.S.), Seoul National University Hospital, Seoul, Korea
| | - S-H You
- Department of Radiology (S.-H.Y.), Korea University Hospital, Seoul, Korea
| | - R-E Yoo
- Department of Radiology (S.H.C., R.-E.Y., K.M.K., T.J.Y., J.-h.K., C.-H.S.), Seoul National University Hospital, Seoul, Korea
| | - K M Kang
- Department of Radiology (S.H.C., R.-E.Y., K.M.K., T.J.Y., J.-h.K., C.-H.S.), Seoul National University Hospital, Seoul, Korea
| | - T J Yun
- Department of Radiology (S.H.C., R.-E.Y., K.M.K., T.J.Y., J.-h.K., C.-H.S.), Seoul National University Hospital, Seoul, Korea
| | - J-H Kim
- Department of Radiology (S.H.C., R.-E.Y., K.M.K., T.J.Y., J.-h.K., C.-H.S.), Seoul National University Hospital, Seoul, Korea
| | - C-H Sohn
- Department of Radiology (S.H.C., R.-E.Y., K.M.K., T.J.Y., J.-h.K., C.-H.S.), Seoul National University Hospital, Seoul, Korea
| | - C-K Park
- Department of Neurosurgery (C.-K.P.), Seoul National University Hospital, Seoul, Korea
| | - S-H Park
- Department of Pathology (S.-H.P.), Seoul National University Hospital, Seoul, Korea
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Wang Y, Meng X, Zhou S, Zhu Y, Xu J, Tao R. Apatinib Plus Temozolomide for Recurrent Glioblastoma: An Uncontrolled, Open-Label Study. Onco Targets Ther 2019; 12:10579-10585. [PMID: 31819537 PMCID: PMC6899067 DOI: 10.2147/ott.s226804] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Accepted: 11/23/2019] [Indexed: 12/17/2022] Open
Abstract
Objective This study aimed to determine the efficacy and tolerability of apatinib plus dose-dense temozolomide (TMZ) as first-line treatment for recurrent glioblastoma (rGBM). Methods Patients with rGBM were enrolled in this study. Patients were subjected to concurrent treatment of apatinib (500 mg qd) and dose-dense TMZ (100 mg/m2, 7 days on with 7 days off) until disease progression or intolerable toxicity. Efficacy was evaluated using Response Assessment in Neuro-Oncology criteria for high-grade glioma. Safety was assessed using NCI-CTCAE 4.0. Survival was estimated with Kaplan–Meier curve and log rank test. Results From March 2016 to January 2018, 20 eligible patients who had relapsed from the standard chemoradiotherapy regimen (TMZ and radiotherapy) were enrolled in this study. The median follow-up time was 12 months. All patients were eligible for efficacy analysis. The objective response rate (ORR) was 45%. The disease control rate (DCR) was 90%. The median progress-free survival time was 6 months (95% CI, 5.3 to 7.8 months). The 6-month progression-free survival rate was 50%. The median overall survival was 9 months (95% CI, 8.2 to 12.2 months). The most common treatment-related adverse events were hypertension (21%), hand–foot syndrome (16%), leukopenia (14%), and thrombocytopenia (12%). Conclusion Apatinib combined with dose-dense TMZ was effective in terms of PFS, ORR, and DCR and was well tolerated after appropriate dose reduction in the Chinese population tested. Further randomized controlled clinical studies are needed to confirm the efficacy of apatinib combined with TMZ for treatment of rGBM.
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Affiliation(s)
- Yong Wang
- Department of Neurosurgery, Shandong Cancer Hospital Affiliated to Shandong University; Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, People's Republic of China
| | - Xiangji Meng
- Department of Neurosurgery, Shandong Cancer Hospital Affiliated to Shandong University; Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, People's Republic of China
| | - Shizhen Zhou
- Department of Neurosurgery, Shandong Cancer Hospital Affiliated to Shandong University; Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, People's Republic of China
| | - Yufang Zhu
- Department of Neurosurgery, Shandong Cancer Hospital Affiliated to Shandong University; Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, People's Republic of China
| | - Jun Xu
- Department of Neurosurgery, Shandong Cancer Hospital Affiliated to Shandong University; Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, People's Republic of China
| | - Rongjie Tao
- Department of Neurosurgery, Shandong Cancer Hospital Affiliated to Shandong University; Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, People's Republic of China
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