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Chuthip P, Sitthinamsuwan B, Witthiwej T, Tansirisithikul C, Khumpalikit I, Nunta-aree S. Predictors for the Differentiation between Glioblastoma, Primary Central Nervous System Lymphoma, and Metastasis in Patients with a Solitary Enhancing Intracranial Mass. Asian J Neurosurg 2024; 19:186-201. [PMID: 38974428 PMCID: PMC11226298 DOI: 10.1055/s-0044-1787051] [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] [Indexed: 07/09/2024] Open
Abstract
Introduction Differentiation between glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastasis is important in decision-making before surgery. However, these malignant brain tumors have overlapping features. This study aimed to identify predictors differentiating between GBM, PCNSL, and metastasis. Materials and Methods Patients with a solitary intracranial enhancing tumor and a histopathological diagnosis of GBM, PCNSL, or metastasis were investigated. All patients with intracranial lymphoma had PCNSL without extracranial involvement. Demographic, clinical, and radiographic data were analyzed to determine their associations with the tumor types. Results The predictors associated with GBM were functional impairment ( p = 0.001), large tumor size ( p < 0.001), irregular tumor margin ( p < 0.001), heterogeneous contrast enhancement ( p < 0.001), central necrosis ( p < 0.001), intratumoral hemorrhage ( p = 0.018), abnormal flow void ( p < 0.001), and hypodensity component on noncontrast cranial computed tomography (CT) scan ( p < 0.001). The predictors associated with PCNSL comprised functional impairment ( p = 0.005), deep-seated tumor location ( p = 0.006), homogeneous contrast enhancement ( p < 0.001), absence of cystic appearance ( p = 0.008), presence of hypointensity component on precontrast cranial T1-weighted magnetic resonance imaging (MRI; p = 0.027), and presence of isodensity component on noncontrast cranial CT ( p < 0.008). Finally, the predictors for metastasis were an infratentorial ( p < 0.001) or extra-axial tumor location ( p = 0.035), smooth tumor margin ( p < 0.001), and presence of isointensity component on cranial fluid-attenuated inversion recovery MRI ( p = 0.047). Conclusion These predictors may be used to differentiate between GBM, PCNSL, and metastasis, and they are useful in clinical management.
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Affiliation(s)
- Pornthida Chuthip
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Department of Surgery, Pattani Hospital, Pattani, Thailand
| | - Bunpot Sitthinamsuwan
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Theerapol Witthiwej
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Chottiwat Tansirisithikul
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Inthira Khumpalikit
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Sarun Nunta-aree
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
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Shi J, Chen H, Wang X, Cao R, Chen Y, Cheng Y, Pang Z, Huang C. Using Radiomics to Differentiate Brain Metastases From Lung Cancer Versus Breast Cancer, Including Predicting Epidermal Growth Factor Receptor and human Epidermal Growth Factor Receptor 2 Status. J Comput Assist Tomogr 2023; 47:924-933. [PMID: 37948368 DOI: 10.1097/rct.0000000000001499] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
OBJECTIVE We evaluated the feasibility of using multiregional radiomics to identify brain metastasis (BM) originating from lung adenocarcinoma (LA) and breast cancer (BC) and assess the epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) status. METHODS Our experiment included 160 patients with BM originating from LA (n = 70), BC (n = 67), and other tumor types (n = 23), between November 2017 and December 2021. All patients underwent contrast-enhanced T1- and T2-weighted magnetic resonance imaging (MRI) scans. A total of 1967 quantitative MRI features were calculated from the tumoral active area and peritumoral edema area and selected using least absolute shrinkage and selection operator regression with 5-fold cross-validation. We constructed radiomic signatures (RSs) based on the most predictive features for preoperative assessment of the metastatic origins, EGFR mutation, and HER2 status. Prediction performance of the constructed RSs was evaluated based on the receiver operating characteristic curve analysis. RESULTS The developed multiregion RSs generated good area under the receiver operating characteristic curve (AUC) for identifying the LA and BC origin in the training (AUCs, RS-LA vs RS-BC, 0.767 vs 0.898) and validation (AUCs, RS-LA vs RS-BC, 0.778 and 0.843) cohort and for predicting the EGFR and HER2 status in the training (AUCs, RS-EGFR vs RS-HER2, 0.837 vs 0.894) and validation (AUCs, RS-EGFR vs RS-HER2, 0.729 vs 0.784) cohorts. CONCLUSIONS Our results revealed associations between brain MRI-based radiomics and their metastatic origins, EGFR mutations, and HER2 status. The developed multiregion combined RSs may be considered noninvasive predictive markers for planning early treatment for BM patients.
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Affiliation(s)
- Jiaxin Shi
- From the School of Intelligent Medicine, China Medical University
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, People's Republic of China
| | - Ran Cao
- From the School of Intelligent Medicine, China Medical University
| | - Yu Chen
- From the School of Intelligent Medicine, China Medical University
| | - Yuan Cheng
- From the School of Intelligent Medicine, China Medical University
| | - Ziyan Pang
- From the School of Intelligent Medicine, China Medical University
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3
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Eraky AM. Radiological Biomarkers for Brain Metastases Prognosis: Quantitative Magnetic Resonance Imaging (MRI) Modalities As Non-invasive Biomarkers for the Effect of Radiotherapy. Cureus 2023; 15:e38353. [PMID: 37266043 PMCID: PMC10229388 DOI: 10.7759/cureus.38353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2023] [Indexed: 06/03/2023] Open
Abstract
Radiotherapy effect is achieved by its ability to cause DNA damage and induce apoptosis. In contrast, radiation can induce tumor cells' proliferation, invasiveness, and epithelial-mesenchymal transition (EMT). Besides developing radioresistance, this paradoxical effect of radiotherapy is considered a challenging problem in the field of radiotherapy. This highlights the importance of developing new modalities to diagnose radioresistance early to avoid any unnecessary exposure to radiation and differentiate between metastases recurrence versus post-radiation changes. Quantitative magnetic resonance imaging (MRI) techniques including diffusion-weighted imaging (DWI), dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast-enhanced (DCE) represent potential biomarkers to diagnose metastases recurrence and radioresistance. In this review, we will focus on recent studies discussing the possibility of using DWI, DSC, ASL, and DCE to diagnose radioresistance and recurrence in patients with brain metastases.
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Affiliation(s)
- Akram M Eraky
- Neurological Surgery, Medical College of Wisconsin, Milwaukee, USA
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4
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Zhao LM, Hu R, Xie FF, Clay Kargilis D, Imami M, Yang S, Guo JQ, Jiao X, Chen RT, Wei-Hua L, Li L. Radiomic-Based MRI for Classification of Solitary Brain Metastases Subtypes From Primary Lymphoma of the Central Nervous System. J Magn Reson Imaging 2023; 57:227-235. [PMID: 35652509 DOI: 10.1002/jmri.28276] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/16/2022] [Accepted: 05/17/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Differential diagnosis of brain metastases subtype and primary central nervous system lymphoma (PCNSL) is necessary for treatment decisions. The application of machine learning facilitates the classification of brain tumors, but prior investigations into primary lymphoma and brain metastases subtype classification have been limited. PURPOSE To develop a machine-learning model to classify PCNSL, brain metastases with primary lung and non-lung origin. STUDY TYPE Retrospective. POPULATION A total of 211 subjects with pathologically confirmed PCNSL or brain metastases (training cohort 168 and testing cohort 43). FIELD STRENGTH/SEQUENCE A 3.0 T axial contrast-enhanced T1-weighted spin-echo inversion recovery sequence (T1WI-CE), axial T2-weighted fluid-attenuation inversion recovery sequence (T2FLAIR) ASSESSMENT: Several machine-learning models (support vector machine, random forest, and K-nearest neighbors) were built with least absolute shrinkage and selection operator (LASSO) using features from T1WI-CE, T2FLAIR, and clinical. The model with the highest performance in the training cohort was selected to differentiate lesions in the testing cohort. Then, three radiologists conducted a two-round classification (with and without model reference) using images and clinical information from testing cohorts. STATISTICAL TESTS Five-fold cross-validation was used for model evaluation and calibration. Model performance was assessed based on sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). RESULTS Twenty-five image features were selected by LASSO analysis. Random forest classifier was selected for its highest performance on the training set with an AUC of 0.73. After calibration, this model achieved an accuracy of 0.70 on the testing set. Accuracies of all three radiologists improved under model reference (0.49 vs. 0.70, 0.60 vs. 0.77, 0.58 vs. 0.72, respectively). DATA CONCLUSION The random forest model based on conventional MRI and clinical data can diagnose PCNSL and brain metastases subtypes (lung and non-lung origin). Model classification can help foster the diagnostic accuracy of specialists and streamline prognostication workflow. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Lin-Mei Zhao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Rong Hu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Fang-Fang Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Daniel Clay Kargilis
- Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Maliha Imami
- Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shuai Yang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Jiu-Qing Guo
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiao Jiao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Rui-Ting Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Liao Wei-Hua
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Lang Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
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5
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Shen J, Zhang W, Zhu JJ, Xue L, Yuan M, Xu H, Xu XQ, Yu TF, Wu FY. Multiparametric Magnetic Resonance Imaging for Assessing Thymic Epithelial Tumors: Correlation With Pathological Subtypes and Clinical Stages. J Magn Reson Imaging 2022; 56:1487-1496. [PMID: 35417074 DOI: 10.1002/jmri.28198] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND World Health Organization classification and Masaoka-Koga stage are widely used for thymic epithelial tumors (TETs). Reduced field-of-view (rFOV) diffusion-weighed imaging (DWI) proved to improve the image quality. Dynamic contrast-enhanced (DCE) MRI was commonly used in evaluating tumors. PURPOSE To investigate the value of multiparametric MRI in evaluating TETs. STUDY TYPE Retrospective. SUBJECTS Eighty-seven participants including 38 low risk (52.08 ± 14.19 years), 30 high risk (52.40 ± 11.35 years), and 19 thymic carcinoma patients (59.76 ± 10.78 years). FIELD STRENGTH/SEQUENCE A 3 T, turbo spin echo imaging, echo planar imaging, volumetric interpolated breath-hold examination with radial acquisition trajectory. ASSESSMENT DCE-MRI and apparent diffusion coefficient (ADC) variables were compared. Diagnostic performances of single significant factor and combined model were compared. STATISTICAL TESTS Parameters were compared using one-way ANOVA or independent-samples t test. Logistic regression was employed to investigate the combined model. Receiver operating curves (ROC) and DeLong's test were used to compare the diagnostic efficiency. RESULTS ADC, Ktrans , and kep values were significantly different among low-risk, high-risk and carcinoma group (ADC, 1.279 ± 0.345 × 10-3 mm2 /sec, 0.978 ± 0.260 × 10-3 mm2 /sec, 0.661 ± 0.134 × 10-3 mm2 /sec; Ktrans 0.167 ± 0.071 min-1 , 0.254 ± 0.136 min-1 , 0.393 ± 0.110 min-1 ; kep 0.345 ± 0.113 min-1 , 0.560 ± 0.269 min-1 , 0.872 ± 0.149 min-1 ). They were significantly different for early stage and advanced stage (ADC, 1.270 ± 0.356 × 10-3 mm2 /sec vs. 0.845 ± 0.251 × 10-3 mm2 /sec; Ktrans 0.179 ± 0.092 min-1 vs. 0.304 ± 0.142 min-1 ; kep 0.370 ± 0.181 min-1 vs. 0.674 ± 0.362 min-1 ). The combination of them had highest diagnostic efficiency for WHO classification (AUC, 0.925; sensitivity, 83.7%; specificity, 89.5%), clinical stage (AUC, 0.879; sensitivity, 80.9%; specificity, 82.5%). DATA CONCLUSION Multiparametric MRI model may be useful for discriminating WHO classification and clinical stage of TETs. EVIDENCE LEVEL 4 TECHNICAL EFFICIENCY: Stage 2.
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Affiliation(s)
- Jie Shen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wei Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jia-Jia Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Xue
- Department of Thoracic surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Mei Yuan
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hai Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Tong-Fu Yu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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6
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Scola E, Desideri I, Bianchi A, Gadda D, Busto G, Fiorenza A, Amadori T, Mancini S, Miele V, Fainardi E. Assessment of brain tumors by magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging and computed tomography perfusion: a comparison study. LA RADIOLOGIA MEDICA 2022; 127:664-672. [PMID: 35441970 DOI: 10.1007/s11547-022-01470-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 02/11/2022] [Indexed: 12/30/2022]
Abstract
PURPOSE To investigate the association and agreement between magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and computed tomography perfusion (CTP) in determining vascularity and permeability of primary and secondary brain tumors. MATERIAL AND METHODS DSC-PWI and CTP studies from 97 patients with high-grade glioma, low-grade glioma and solitary brain metastasis were retrospectively reviewed. Normalized cerebral blood flow (nCBF), cerebral blood volume (nCBV), capillary transfer constant (nK2) and permeability surface area product (nPS) values were obtained. Variables among groups were compared, and correlation and agreement between DSC-PWI and CTP were tested. RESULTS All DSC-PWI and CTP parameters were higher in high-grade than in low-grade gliomas (p < 0.01 and p < 0.001). Metastases had greater DSC-PWI nCBV (p < 0.05), nCTP-CBF (p < 0.05), nCTP-CBV (p < 0.01) and nCTP-PS (p < 0.0001) than low-grade gliomas and more elevated nCTP-PS (p < 0.01) than high-grade gliomas. The correlation was strong between DSC-PWI nCBF and CTP nCBF (r = 0.79; p < 0.00001) and between DSC-PWI nCBV and CTP nCBV (r = 0.83; p < 0.00001), weaker between DSC-PWI nK2 and CTP nPS (r = 0.29; p < 0.01). Bland-Altman plots indicated that the agreement was strong between DSC-PWI nCBF and CTP nCBF, good between DSC-PWI nCBV and CTP nCBV and poorer between DSC-PWI nK2 and CTP nPS. CONCLUSION DSC-PWI and CTP CBF and CBV maps were comparable and interchangeable in the assessment of tumor vascularity, unlike DSC-PWI K2 and CTP PS maps that were more discordant in the analysis of tumor permeability. CTP could be an alternative method to quantify tumor neoangiogenesis when MRI is not available or when the patient does not tolerate it.
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Affiliation(s)
- Elisa Scola
- Struttura Organizzativa Dipartimentale di Neuroradiologia, Dipartimento di Radiologia, Ospedale Universitario Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Ilaria Desideri
- Struttura Organizzativa Dipartimentale di Neuroradiologia, Dipartimento di Radiologia, Ospedale Universitario Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Andrea Bianchi
- Struttura Organizzativa Dipartimentale di Neuroradiologia, Dipartimento di Radiologia, Ospedale Universitario Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Davide Gadda
- Struttura Organizzativa Dipartimentale di Neuroradiologia, Dipartimento di Radiologia, Ospedale Universitario Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Giorgio Busto
- Struttura Organizzativa Dipartimentale di Neuroradiologia, Dipartimento di Radiologia, Ospedale Universitario Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Alessandro Fiorenza
- Radiodiagnostic Unit N. 2, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Tommaso Amadori
- Radiodiagnostic Unit N. 2, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Sara Mancini
- Radiodiagnostic Unit N. 2, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| | - Enrico Fainardi
- Struttura Organizzativa Dipartimentale di Neuroradiologia, Dipartimento di Radiologia, Ospedale Universitario Careggi, Largo Brambilla 3, 50134, Florence, Italy.,Neuroradiology Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy
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7
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Role of intra-tumoral vasculature imaging features on susceptibility weighted imaging in differentiating primary central nervous system lymphoma from glioblastoma: a multiparametric comparison with pathological validation. Neuroradiology 2022; 64:1801-1818. [DOI: 10.1007/s00234-022-02946-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/04/2022] [Indexed: 10/18/2022]
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8
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Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading. Cancers (Basel) 2022; 14:cancers14061432. [PMID: 35326580 PMCID: PMC8946242 DOI: 10.3390/cancers14061432] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/01/2022] [Accepted: 03/08/2022] [Indexed: 11/17/2022] Open
Abstract
Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment-glioblastomas, in particular, have a dismal prognosis and are currently incurable-their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers.
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9
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Aparici-Robles F, Davidhi A, Carot-Sierra JM, Perez-Girbes A, Carreres-Polo J, Mazon Momparler M, Juan-Albarracín J, Fuster-Garcia E, Garcia-Gomez JM. Glioblastoma versus solitary brain metastasis: MRI differentiation using the edema perfusion gradient. J Neuroimaging 2021; 32:127-133. [PMID: 34468052 DOI: 10.1111/jon.12920] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/18/2021] [Accepted: 08/05/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND AND PURPOSE Differentiation between glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) remains a challenge in neuroradiology with up to 40% of the cases to be incorrectly classified using only conventional MRI. The inclusion of perfusion MRI parameters provides characteristic features that could support the distinction of these pathological entities. On these grounds, we aim to use a perfusion gradient in the peritumoral edema. METHODS Twenty-four patients with GBM or an SBM underwent conventional and perfusion MR imaging sequences before tumors' surgical resection. After postprocessing of the images, quantification of dynamic susceptibility contrast (DSC) perfusion parameters was made. Three concentric areas around the tumor were defined in each case. The monocompartimental and pharmacokinetics parameters of perfusion MRI were analyzed in both series. RESULTS DSC perfusion MRI models can provide useful information for the differentiation between GBM and SBM. It can be observed that most of the perfusion MR parameters (relative cerebral blood volume, relative cerebral blood flow, relative Ktrans, and relative volume fraction of the interstitial space) clearly show higher gradient for GBM than SBM. GBM also demonstrates higher heterogeneity in the peritumoral edema and most of the perfusion parameters demonstrate higher gradients in the area closest to the enhancing tumor. CONCLUSION Our results show that there is a difference in the perfusion parameters of the edema between GBM and SBM demonstrating a vascularization gradient. This could help not only for the diagnosis, but also for planning surgical or radiotherapy treatments delineating the real extension of the tumor.
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Affiliation(s)
- Fernando Aparici-Robles
- Servicio de Radiología, Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Andjoli Davidhi
- Servicio de Radiología, Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - José Miguel Carot-Sierra
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Alexandre Perez-Girbes
- Servicio de Radiología, Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Joan Carreres-Polo
- Servicio de Radiología, Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Miguel Mazon Momparler
- Servicio de Radiología, Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Javier Juan-Albarracín
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Elies Fuster-Garcia
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Juan Miguel Garcia-Gomez
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
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10
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Barajas RF, Politi LS, Anzalone N, Schöder H, Fox CP, Boxerman JL, Kaufmann TJ, Quarles CC, Ellingson BM, Auer D, Andronesi OC, Ferreri AJM, Mrugala MM, Grommes C, Neuwelt EA, Ambady P, Rubenstein JL, Illerhaus G, Nagane M, Batchelor TT, Hu LS. Consensus recommendations for MRI and PET imaging of primary central nervous system lymphoma: guideline statement from the International Primary CNS Lymphoma Collaborative Group (IPCG). Neuro Oncol 2021; 23:1056-1071. [PMID: 33560416 PMCID: PMC8248856 DOI: 10.1093/neuonc/noab020] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Advanced molecular and pathophysiologic characterization of primary central nervous system lymphoma (PCNSL) has revealed insights into promising targeted therapeutic approaches. Medical imaging plays a fundamental role in PCNSL diagnosis, staging, and response assessment. Institutional imaging variation and inconsistent clinical trial reporting diminishes the reliability and reproducibility of clinical response assessment. In this context, we aimed to: (1) critically review the use of advanced positron emission tomography (PET) and magnetic resonance imaging (MRI) in the setting of PCNSL; (2) provide results from an international survey of clinical sites describing the current practices for routine and advanced imaging, and (3) provide biologically based recommendations from the International PCNSL Collaborative Group (IPCG) on adaptation of standardized imaging practices. The IPCG provides PET and MRI consensus recommendations built upon previous recommendations for standardized brain tumor imaging protocols (BTIP) in primary and metastatic disease. A biologically integrated approach is provided to addresses the unique challenges associated with the imaging assessment of PCNSL. Detailed imaging parameters facilitate the adoption of these recommendations by researchers and clinicians. To enhance clinical feasibility, we have developed both “ideal” and “minimum standard” protocols at 3T and 1.5T MR systems that will facilitate widespread adoption.
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Affiliation(s)
- Ramon F Barajas
- Department of Radiology, Neuroradiology Section, Oregon Health & Science University, Portland Oregon, USA.,Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Knight Cancer Institute Translational Oncology Program, Oregon Health & Science University, Portland, Oregon, USA
| | - Letterio S Politi
- Humanitas University and Humanitas Research and Clinical Center - IRCCS, Milan, Italy.,Boston Children's Hospital, Boston, Massachusetts, USA
| | - Nicoletta Anzalone
- Neuroradiology Unit, IRCCS San Raffaele Hospital and Vita-Salute University, Milan, Italy
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Christopher P Fox
- Department of Clinical Haematology, Nottingham University Hospitals NHS Trust, School of Medicine, University of Nottingham, Nottingham, UK
| | - Jerrold L Boxerman
- Department of Diagnostic Imaging, Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | | | - C Chad Quarles
- Department of Neuroimaging Research & Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Departments of Radiological Sciences and Psychiatry, David Geffen School of Medicine, University of California - Los Angeles, Los Angeles, California, USA.,Departments of Radiological Sciences, Psychiatry, and Biobehavioral Sciences, David Geffen School of Medicine, University of California - Los Angeles, Los Angeles, California, USA
| | - Dorothee Auer
- Versus Arthritis Pain Centre, University of Nottingham, Nottingham, UK.,NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Ovidiu C Andronesi
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Andres J M Ferreri
- Lymphoma Unit, Department of Onco-Hematology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maciej M Mrugala
- Department of Medicine, Division of Hematology and Oncology, Mayo Clinic Cancer Center, Phoenix, Arizona, USA.,Department of Neurology, Mayo Clinic, Phoenix, Arizona, USA
| | - Christian Grommes
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Neurology, Weill Cornell Medical School, New York, New York, USA
| | - Edward A Neuwelt
- Blood-Brain Barrier Program, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurological Surgery, Oregon Health & Science University, Portland, Oregon, USA.,Portland Veterans Affairs Medical Center, Portland, Oregon, USA
| | - Prakash Ambady
- Blood-Brain Barrier Program, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - James L Rubenstein
- Division of Hematology/Oncology, University of California, San Francisco, California, USA.,Department of Medicine, University of California, San Francisco, California, USA.,Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
| | - Gerald Illerhaus
- Clinic of Hematology, Oncology and Palliative Care, Klinikum Stuttgart, Stuttgart, Germany
| | - Motoo Nagane
- Department of Neurosurgery, Kyorin University Faculty of Medicine, Tokyo, Japan
| | - Tracy T Batchelor
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leland S Hu
- Department of Radiology, Neuroradiology Division, Mayo Clinic, Phoenix, Arizona, USA
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11
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Kamimura K, Nakajo M, Bohara M, Nagano D, Fukukura Y, Fujio S, Takajo T, Tabata K, Iwanaga T, Imai H, Nickel MD, Yoshiura T. Consistency of Pituitary Adenoma: Prediction by Pharmacokinetic Dynamic Contrast-Enhanced MRI and Comparison with Histologic Collagen Content. Cancers (Basel) 2021; 13:cancers13153914. [PMID: 34359814 PMCID: PMC8345382 DOI: 10.3390/cancers13153914] [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: 07/19/2021] [Accepted: 07/29/2021] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Transsphenoidal resection of hard pituitary adenomas have a particularly high risk of residual tumor and complications. Therefore, prediction of tumor consistency is valuable for planning pituitary adenoma surgery. We prospectively examined whether quantitative pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is useful for predicting consistency of pituitary adenoma in 49 participants. We found that the measure of volume of extravascular extracellular space per unit volume of tissue derived from DCE-MRI could predict the consistency of pituitary adenomas. Furthermore, the volume of extravascular extracellular space per unit volume of tissue was significantly positively correlated with histopathologic collagen content of the adenoma. Our results suggest that volume of extravascular extracellular space per unit volume of tissue derived from quantitative pharmacokinetic analysis of DCE-MRI has a predictive value for consistency of pituitary adenomas. Abstract Prediction of tumor consistency is valuable for planning transsphenoidal surgery for pituitary adenoma. A prospective study was conducted involving 49 participants with pituitary adenoma to determine whether quantitative pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is useful for predicting consistency of adenomas. Pharmacokinetic parameters in the adenomas including volume of extravascular extracellular space (EES) per unit volume of tissue (ve), blood plasma volume per unit volume of tissue (vp), volume transfer constant between blood plasma and EES (Ktrans), and rate constant between EES and blood plasma (kep) were obtained. The pharmacokinetic parameters and the histologic percentage of collagen content (PCC) were compared between soft and hard adenomas using Mann–Whitney U test. Pearson’s correlation coefficient was used to correlate pharmacokinetic parameters with PCC. Hard adenomas showed significantly higher PCC (44.08 ± 15.14% vs. 6.62 ± 3.47%, p < 0.01), ve (0.332 ± 0.124% vs. 0.221 ± 0.104%, p < 0.01), and Ktrans (0.775 ± 0.401/min vs. 0.601 ± 0.612/min, p = 0.02) than soft adenomas. Moreover, a significant positive correlation was found between ve and PCC (r = 0.601, p < 0.01). The ve derived using DCE-MRI may have predictive value for consistency of pituitary adenoma.
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Affiliation(s)
- Kiyohisa Kamimura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan; (M.N.); (M.B.); (D.N.); (Y.F.); (T.Y.)
- Correspondence: ; Tel.: +81-99-275-5417
| | - Masanori Nakajo
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan; (M.N.); (M.B.); (D.N.); (Y.F.); (T.Y.)
| | - Manisha Bohara
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan; (M.N.); (M.B.); (D.N.); (Y.F.); (T.Y.)
| | - Daigo Nagano
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan; (M.N.); (M.B.); (D.N.); (Y.F.); (T.Y.)
| | - Yoshihiko Fukukura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan; (M.N.); (M.B.); (D.N.); (Y.F.); (T.Y.)
| | - Shingo Fujio
- Department of Neurosurgery, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan; (S.F.); (T.T.)
| | - Tomoko Takajo
- Department of Neurosurgery, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan; (S.F.); (T.T.)
| | - Kazuhiro Tabata
- Department of Pathology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan;
| | - Takashi Iwanaga
- Department of Radiological Technology, Kagoshima University Hospital, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan;
| | - Hiroshi Imai
- MR Research & Collaboration, Siemens Healthcare K.K., 1-11-1 Osaki, Shinagawa, Tokyo 141-8644, Japan;
| | | | - Takashi Yoshiura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan; (M.N.); (M.B.); (D.N.); (Y.F.); (T.Y.)
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12
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Su CQ, Chen XT, Duan SF, Zhang JX, You YP, Lu SS, Hong XN. A radiomics-based model to differentiate glioblastoma from solitary brain metastases. Clin Radiol 2021; 76:629.e11-629.e18. [PMID: 34092362 DOI: 10.1016/j.crad.2021.04.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 04/21/2021] [Indexed: 11/18/2022]
Abstract
AIM To differentiate glioblastoma (GBM) from solitary brain metastases (MET) using radiomic analysis. MATERIALS AND METHODS Two hundred and fifty-three patients with solitary brain tumours (157 GBM and 98 solitary brain MET) were split into a training cohort (n=178) and a validation cohort (n=77) by stratified sampling using computer-generated random numbers at a ratio of 7:3. After feature extraction, minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to build the radiomics signature on the training cohort and validation cohort. Performance was assessed by radiomics score (Rad-score), receiver operating characteristic (ROC) curve, calibration, and clinical usefulness. RESULTS Eleven radiomic features were selected as significant features in the training cohort. The Rad-score was significantly associated with the differentiation between GBM and solitary brain MET (p<0.001) both in the training and validation cohorts. The radiomics signature yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and validation cohorts to distinguish between GBM and solitary brain MET. CONCLUSIONS The radiomics model might be a useful supporting tool for the preoperative differentiation of GBM from solitary brain MET, which could aid pretreatment decision-making.
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Affiliation(s)
- C-Q Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - X-T Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - S-F Duan
- GE Healthcare China, NO.1, Huatuo Road, Pudong New Town, Shanghai 210000, China
| | - J-X Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - Y-P You
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - S-S Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
| | - X-N Hong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
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13
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Kang KM, Choi SH, Chul-Kee P, Kim TM, Park SH, Lee JH, Lee ST, Hwang I, Yoo RE, Yun TJ, Kim JH, Sohn CH. Differentiation between glioblastoma and primary CNS lymphoma: application of DCE-MRI parameters based on arterial input function obtained from DSC-MRI. Eur Radiol 2021; 31:9098-9109. [PMID: 34003350 DOI: 10.1007/s00330-021-08044-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 04/06/2021] [Accepted: 05/04/2021] [Indexed: 11/24/2022]
Abstract
OBJECTIVE This study aimed to evaluate whether arterial input functions (AIFs) obtained from dynamic susceptibility contrast (DSC)-MRI (AIFDSC) improve the reliability and diagnostic accuracy of dynamic contrast-enhanced (DCE)-derived pharmacokinetic (PK) parameters for differentiating glioblastoma from primary CNS lymphoma (PCNSL) compared with AIFs derived from DCE-MRI (AIFDCE). METHODS This retrospective study included 172 patients with glioblastoma (n = 147) and PCNSL (n = 25). All patients had undergone preoperative DSC- and DCE-MRI. The volume transfer constant (Ktrans), volume of the vascular plasma space (vp), and volume of the extravascular extracellular space (ve) were acquired using AIFDSC and AIFDCE. The relative cerebral blood volume (rCBV) was obtained from DSC-MRI. Intraclass correlation coefficients (ICC) and ROC curves were used to assess the reliability and diagnostic accuracy of individual parameters. RESULTS The mean Ktrans, vp, and ve values revealed better ICCs with AIFDSC than with AIFDCE (Ktrans, 0.911 vs 0.355; vp, 0.766 vs 0.503; ve, 0.758 vs 0.657, respectively). For differentiating all glioblastomas from PCNSL, the mean rCBV (AUC = 0.856) was more accurate than the AIFDSC-driven mean Ktrans, which had the largest AUC (0.711) among the DCE-derived parameters (p = 0.02). However, for glioblastomas with low rCBV (≤ 75th percentile of PCNSL; n = 30), the AIFDSC-driven mean Ktrans and vp were more accurate than rCBV (AUC: Ktrans, 0.807 vs rCBV, 0.515, p = 0.004; vp, 0.715 vs rCBV, p = 0.045). CONCLUSION DCE-derived PK parameters using the AIFDSC showed improved reliability and diagnostic accuracy for differentiating glioblastoma with low rCBV from PCNSL. KEY POINTS • An accurate differential diagnosis of glioblastoma and PCNSL is crucial because of different therapeutic strategies. • In contrast to the rCBV from DSC-MRI, another perfusion imaging technique, the DCE parameters for the differential diagnosis have been limited because of the low reliability of AIFs from DCE-MRI. • When we analyzed DCE-MRI data using AIFs from DSC-MRI (AIFDSC), AIFDSC-driven DCE parameters showed improved reliability and better diagnostic accuracy than rCBV for differentiating glioblastoma with low rCBV from PCNSL.
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Affiliation(s)
- Koung Mi Kang
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Seung Hong Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea. .,Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea. .,Center for Nanoparticle Research, Institute for Basic Science, and School of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea.
| | - Park Chul-Kee
- Department of Neurosurgery and Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Tae Min Kim
- Department of Internal Medicine and Cancer Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sung-Hye Park
- Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Joo Ho Lee
- Department of Radiation Oncology and Cancer Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Soon-Tae Lee
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Inpyeong Hwang
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Roh-Eul Yoo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Tae Jin Yun
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Ji-Hoon Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea
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14
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Tepe M, Saylisoy S, Toprak U, Inan I. The Potential Role of Peritumoral Apparent Diffusion Coefficient Evaluation in Differentiating Glioblastoma and Solitary Metastatic Lesions of the Brain. Curr Med Imaging 2021; 17:1200-1208. [PMID: 33726654 DOI: 10.2174/1573405617666210316120314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 02/14/2021] [Accepted: 02/16/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Differentiating glioblastoma (GBM) and solitary metastasis is not always possible using conventional magnetic resonance imaging (MRI) techniques. In conventional brain MRI, GBM and brain metastases are lesions with mostly similar imaging findings. In this study, we investigated whether apparent diffusion coefficient (ADC) ratios, ADC gradients, and minimum ADC values in the peritumoral edema tissue can be used to discriminate between these two tumors. METHODS This retrospective study was approved by the local institutional review board with a waiver of written informed consent. Prior to surgical and medical treatment, conventional brain MRI and diffusion-weighted MRI (b = 0 and b = 1000) images were taken from 43 patients (12 GBM and 31 solitary metastasis cases). Quantitative ADC measurements were performed on the peritumoral tissue from the nearest segment to the tumor (ADC1), the middle segment (ADC2), and the most distant segment (ADC3). The ratios of these three values were determined proportionally to calculate the peritumoral ADC ratios. In addition, these three values were subtracted from each other to obtain the peritumoral ADC gradients. Lastly, the minimum peritumoral and tumoral ADC values, and the quantitative ADC values from the normal appearing ipsilateral white matter, contralateral white matter and ADC values from cerebrospinal fluid (CSF) were recorded. RESULTS For the differentiation of GBM and solitary metastasis, ADC3 / ADC1 was the most powerful parameter with a sensitivity of 91.7% and specificity of 87.1% at the cut-off value of 1.105 (p < 0.001), followed by ADC3 / ADC2 with a cut-off value of 1.025 (p = 0.001), sensitivity of 91.7%, and specificity of 74.2%. The cut-off, sensitivity and specificity of ADC2 / ADC1 were 1.055 (p = 0.002), 83.3%, and 67.7%, respectively. For ADC3 - ADC1, the cut-off value, sensitivity and specificity were calculated as 150 (p < 0.001), 91.7% and 83.9%, respectively. ADC3 - ADC2 had a cut-off value of 55 (p = 0.001), sensitivity of 91.7%, and specificity of 77.4 whereas ADC2 - ADC1 had a cut-off value of 75 (p = 0.003), sensitivity of 91.7%, and specificity of 61.3%. Among the remaining parameters, only the ADC3 value successfully differentiated between GBM and metastasis (GBM 1802.50 ± 189.74 vs. metastasis 1634.52 ± 212.65, p = 0.022). CONCLUSION The integration of the evaluation of peritumoral ADC ratio and ADC gradient into conventional MR imaging may provide valuable information for differentiating GBM from solitary metastatic lesions.
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Affiliation(s)
- Murat Tepe
- Yunus Emre State Hospital, Department of Radiology, Tepebasi Eskisehir. Turkey
| | - Suzan Saylisoy
- Eskisehir Osmangazi University, Faculty of Medicine, Department of Radiology, Eskisehir. Turkey
| | - Ugur Toprak
- Eskisehir Osmangazi University, Faculty of Medicine, Department of Radiology, Eskisehir. Turkey
| | - Ibrahim Inan
- Adiyaman University, Training and Research Hospital, Department of Radiology, Adiyaman. Turkey
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15
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Lundy P, Domino J, Ryken T, Fouke S, McCracken DJ, Ormond DR, Olson JJ. The role of imaging for the management of newly diagnosed glioblastoma in adults: a systematic review and evidence-based clinical practice guideline update. J Neurooncol 2020; 150:95-120. [DOI: 10.1007/s11060-020-03597-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 08/08/2020] [Indexed: 12/11/2022]
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16
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Zhang HW, Lyu GW, He WJ, Lei Y, Lin F, Feng YN, Wang MZ. Differential diagnosis of central lymphoma and high-grade glioma: dynamic contrast-enhanced histogram. Acta Radiol 2020; 61:1221-1227. [PMID: 31902220 DOI: 10.1177/0284185119896519] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND In clinical diagnosis, some central nervous system lymphomas (CNSL) are difficult to distinguish from high-grade gliomas (HGG). PURPOSE To evaluate the diagnostic efficacy of the histogram analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in the identification of CNSL and HGG. MATERIAL AND METHODS In all, 43 patients diagnosed with HGG (n = 28) and CNSL (n = 15) by histopathology underwent DCE-MRI scanning. Differences in histogram parameters based on DCE-MRI between HGG and CNSL were analyzed by Mann-Whitney U test. In addition, receiver operating characteristic (ROC) analysis was performed. Short-term follow-up of patients was performed using Kaplan-Meier analysis to explore the survival rates of HGG and CNSL. RESULTS For the ROC curve analysis, we demonstrate that the 10th percentile of Ktrans (area under the curve [AUC] = 0.912, sensitivity = 86.7%, specificity = 92.9%), Kep (AUC = 0.940, sensitivity = 93.3%, specificity = 79.6%), Ve (AUC = 0.907, sensitivity = 86.7%, specificity = 89.3%), and AUC (AUC = 0.904, sensitivity = 86.7%, specificity = 92.9%) were significantly different between the CNSL and HGG groups (P < 0.001), with high diagnostic efficiency. Table 2 shows that the histogram features based on AUC maps (10th, 25th, median, 75th, 90th, and mean) were always significantly higher in the CNSL group than in the HGG group (P < 0.001). There was no significant difference in Vp or in the 75th, 90th and mean of Ktrans, Kep, and Ve between the CNSL and HGG groups (P > 0.05). CONCLUSION A histogram analysis of DCE-MRI identified significant differences between HGG and CNSL, and this will help in the clinical differential diagnosis of these conditions.
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Affiliation(s)
- Han-wen Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen, PR China
| | - Gui-wen Lyu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen, PR China
| | - Wen-jie He
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen, PR China
| | - Yi Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen, PR China
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen, PR China
| | - Yu-ning Feng
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen, PR China
| | - Meng-zhu Wang
- Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, Guangdong Province, PR China
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17
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Simplified perfusion fraction from diffusion-weighted imaging in preoperative prediction of IDH1 mutation in WHO grade II-III gliomas: comparison with dynamic contrast-enhanced and intravoxel incoherent motion MRI. Radiol Oncol 2020; 54:301-310. [PMID: 32559177 PMCID: PMC7409598 DOI: 10.2478/raon-2020-0037] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 05/13/2020] [Indexed: 11/20/2022] Open
Abstract
Background Effect of isocitr ate dehydrogenase 1 (IDH1) mutation in neovascularization might be linked with tissue perfusion in gliomas. At present, the need of injection of contrast agent and the increasing scanning time limit the application of perfusion techniques. We used a simplified intravoxel incoherent motion (IVIM)-derived perfusion fraction (SPF) calculated from diffusion-weighted imaging (DWI) using only three b-values to quantitatively assess IDH1-linked tissue perfusion changes in WHO grade II-III gliomas (LGGs). Additionally, by comparing accuracy with dynamic contrast-enhanced (DCE) and full IVIM MRI, we tried to find the optimal imaging markers to predict IDH1 mutation status. Patients and methods Thirty patients were prospectively examined using DCE and multi-b-value DWI. All parameters were compared between the IDH1 mutant and wild-type LGGs using the Mann-Whitney U test, including the DCE MRI-derived Ktrans, ve and vp, the conventional apparen t diffusion coefficient (ADC0,1000), IVIM-de rived perfusion fraction (f), diffusion coefficient (D) and pseudo-diffusion coefficient (D*), SPF. We evaluated the diagnostic performance by receive r operating characteristic (ROC) analysis. Results Significant differences were detected between WHO grade II-III gliomas for all perfusion and diffusion parameters (P < 0.05). When compared to IDH1 mutant LGGs, IDH1 wild-type LGGs exhibited significantly higher perfusion metrics (P < 0.05) and lower diffusion metrics (P < 0.05). Among all parameters, SPF showed a higher diagnostic performance (area under the curve 0.861), with 94.4% sensitivity and 75% specificity. Conclusions DWI, DCE and IVIM MRI may noninvasively help discriminate IDH1 mutation statuses in LGGs. Specifically, simplified DWI-derived SPF showed a superior diagnostic performance.
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18
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Wang Y, Song L, Guo J, Xian J. Value of quantitative multiparametric MRI in differentiating pleomorphic adenomas from malignant epithelial tumors in lacrimal gland. Neuroradiology 2020; 62:1141-1147. [PMID: 32430642 DOI: 10.1007/s00234-020-02455-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Accepted: 05/05/2020] [Indexed: 02/02/2023]
Abstract
PURPOSE To evaluate the diagnostic performance of the quantitative parameters derived from diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI in differentiating lacrimal gland pleomorphic adenomas (LGPAs) from lacrimal gland malignant epithelial tumors (LGMETs). METHODS Seventy-seven cases with LG epithelial tumors confirmed by histopathology (47 LGPAs and 30 LGMETs) underwent DWI and DCE-MRI. The quantitative parameters including the apparent diffusion coefficient (ADC), the volume transfer constant (Ktrans), the efflux rate constant from the extravascular extracellular space (EES) to blood plasma (Kep), and the extravascular extracellular volume fraction (Ve) were used to differentiate LGPAs from LGMETs. Independent-samples t test was conducted to compare these parameters. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS Compared with LGPAs, LGMETs had significantly lower ADC value (1.090 ± 0.169mm2/s) (P < 0.001), higher Ktrans value (0.892 ± 0.517/min) (P = 0.001), and Kep value (1.300 ± 1.131/min) (P = 0.002). ADC as a diagnostic index showed a better diagnostic efficacy in predicting malignant tumors (AUC 0.914, sensitivity 90.0%, specificity 85.1%, and accuracy 87.0%) than Ktrans and Kep alone. The combination of ADC and Ktrans presented the optimal diagnostic performance for the differentiation (AUC 0.938, sensitivity 93.3%, specificity 87.2%, accuracy 89.6%). CONCLUSION The quantitative parameters including ADC, Ktrans, and Kep derived from DWI and DCE-MRI might be potential imaging biomarkers in differentiating LGPAs from LGMETs. The combination of ADC and Ktrans is superior to other quantitative parameters in distinguishing LGPAs from LGMETs.
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Affiliation(s)
- Yongzhe Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1, Dongjiaominxiang, Dongcheng District, Beijing, 100730, China.,Clinical Center for Eye Tumors, Capital Medical University, Beijing, China
| | - Liyuan Song
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1, Dongjiaominxiang, Dongcheng District, Beijing, 100730, China.,Clinical Center for Eye Tumors, Capital Medical University, Beijing, China
| | - Jian Guo
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1, Dongjiaominxiang, Dongcheng District, Beijing, 100730, China.,Clinical Center for Eye Tumors, Capital Medical University, Beijing, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1, Dongjiaominxiang, Dongcheng District, Beijing, 100730, China. .,Clinical Center for Eye Tumors, Capital Medical University, Beijing, China.
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19
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Kamimura K, Nakajo M, Yoneyama T, Bohara M, Nakanosono R, Fujio S, Iwanaga T, Nickel MD, Imai H, Fukukura Y, Yoshiura T. Quantitative pharmacokinetic analysis of high-temporal-resolution dynamic contrast-enhanced MRI to differentiate the normal-appearing pituitary gland from pituitary macroadenoma. Jpn J Radiol 2020; 38:649-657. [PMID: 32162178 DOI: 10.1007/s11604-020-00942-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 02/26/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE To evaluate the usefulness of high-temporal-resolution dynamic contrast-enhanced (DCE) MRI and quantitative pharmacokinetic analysis to differentiate the normal-appearing pituitary gland from a pituitary macroadenoma. MATERIALS AND METHODS Twenty-seven patients with macroadenomas underwent preoperative DCE-MRI with a temporal resolution of 5 s using compressed sensing to obtain pharmacokinetic parameters. Two independent observers localized the normal-appearing pituitary gland on post-contrast T1-weighted images before and after referring to the corresponding Ktrans maps. Agreements between the localizations and intraoperative findings were evaluated using the kappa statistics. The Mann-Whitney U test was used to compare the pharmacokinetic parameters of the normal-appearing pituitary gland and adenoma. RESULTS For both observers, the agreement between the MRI-based localization and the intraoperative findings increased after referring to the Ktrans maps (observer 1, 0.930-1; observer 2, 0.636-0.855). The normal-appearing pituitary gland had significantly higher Ktrans [/min] (1.50 ± 0.80 vs 0.58 ± 0.49, P < 0.0001), kep [/min] (3.19 ± 1.29 vs 2.15 ± 1.18, P = 0.0049), and ve (0.43 ± 0.15 vs 0.25 ± 0.17, P = 0.0003) than adenoma. CONCLUSION High-temporal-resolution DCE-MRI and quantitative pharmacokinetic analysis help accurately localize the normal-appearing pituitary gland in patients with macroadenomas. The normal-appearing pituitary gland was characterized by higher Ktrans, kep, and ve than macroadenoma. Dynamic contrast-enhanced MRI with high-temporal-resolution using compressed sensing was used for quantitative pharmacokinetic analysis of pituitary macroadenomas. An observer study, the use of Ktrans maps improved accuracy in localizing the normal-appearing pituitary gland. As compared to an adenoma, the normal-appearing pituitary gland had significantly higher Ktrans, kep, and ve values.
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Affiliation(s)
- Kiyohisa Kamimura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Masanori Nakajo
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Tomohide Yoneyama
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Manisha Bohara
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Ryota Nakanosono
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Shingo Fujio
- Department of Neurosurgery, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Takashi Iwanaga
- Clinical Engineering Department Radiation Section, Kagoshima University Hospital, Kagoshima, Japan
| | | | | | - Yoshihiko Fukukura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Yoshiura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
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Kang D, Park JE, Kim YH, Kim JH, Oh JY, Kim J, Kim Y, Kim ST, Kim HS. Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro Oncol 2019; 20:1251-1261. [PMID: 29438500 DOI: 10.1093/neuonc/noy021] [Citation(s) in RCA: 98] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Background Radiomics is a rapidly growing field in neuro-oncology, but studies have been limited to conventional MRI, and external validation is critically lacking. We evaluated technical feasibility, diagnostic performance, and generalizability of a diffusion radiomics model for identifying atypical primary central nervous system lymphoma (PCNSL) mimicking glioblastoma. Methods A total of 1618 radiomics features were extracted from diffusion and conventional MRI from 112 patients (training set, 70 glioblastomas and 42 PCNSLs). Feature selection and classification were optimized using a machine-learning algorithm. The diagnostic performance was tested in 42 patients of internal and external validation sets. The performance was compared with that of human readers (2 neuroimaging experts), cerebral blood volume (90% histogram cutoff, CBV90), and apparent diffusion coefficient (10% histogram, ADC10) using the area under the receiver operating characteristic curve (AUC). Results The diffusion radiomics was optimized with the combination of recursive feature elimination and a random forest classifier (AUC 0.983, stability 2.52%). In internal validation, the diffusion model (AUC 0.984) showed similar performance with conventional (AUC 0.968) or combined diffusion and conventional radiomics (AUC 0.984) and better than human readers (AUC 0.825-0.908), CBV90 (AUC 0.905), or ADC10 (AUC 0.787) in atypical PCNSL diagnosis. In external validation, the diffusion radiomics showed robustness (AUC 0.944) and performed better than conventional radiomics (AUC 0.819) and similar to combined radiomics (AUC 0.946) or human readers (AUC 0.896-0.930). Conclusion The diffusion radiomics model had good generalizability and yielded a better diagnostic performance than conventional radiomics or single advanced MRI in identifying atypical PCNSL mimicking glioblastoma.
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Affiliation(s)
- Daesung Kang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Young-Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Joo Young Oh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jungyoun Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Yikyung Kim
- Department of Radiology, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Sung Tae Kim
- Department of Radiology, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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Okuchi S, Rojas-Garcia A, Ulyte A, Lopez I, Ušinskienė J, Lewis M, Hassanein SM, Sanverdi E, Golay X, Thust S, Panovska-Griffiths J, Bisdas S. Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: A systematic review and meta-analysis. Cancer Med 2019; 8:5564-5573. [PMID: 31389669 PMCID: PMC6745862 DOI: 10.1002/cam4.2369] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 05/19/2019] [Accepted: 06/10/2019] [Indexed: 02/06/2023] Open
Abstract
Background T1‐weighted dynamic contrast‐enhanced (DCE) perfusion magnetic resonance imaging (MRI) has been broadly utilized in the evaluation of brain tumors. We aimed at assessing the diagnostic accuracy of DCE‐MRI in discriminating between low‐grade gliomas (LGGs) and high‐grade gliomas (HGGs), between tumor recurrence and treatment‐related changes, and between primary central nervous system lymphomas (PCNSLs) and HGGs. Methods We performed this study based on the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis of Diagnostic Test Accuracy Studies criteria. We systematically surveyed studies evaluating the diagnostic accuracy of DCE‐MRI for the aforementioned entities. Meta‐analysis was conducted with the use of a random effects model. Results Twenty‐seven studies were included after screening of 2945 possible entries. We categorized the eligible studies into three groups: those utilizing DCE‐MRI to differentiate between HGGs and LGGs (14 studies, 546 patients), between recurrence and treatment‐related changes (9 studies, 298 patients) and between PCNSLs and HGGs (5 studies, 224 patients). The pooled sensitivity, specificity, and area under the curve for differentiating HGGs from LGGs were 0.93, 0.90, and 0.96, for differentiating tumor relapse from treatment‐related changes were 0.88, 0.86, and 0.89, and for differentiating PCNSLs from HGGs were 0.78, 0.81, and 0.86, respectively. Conclusions Dynamic contrast‐enhanced‐Magnetic resonance imaging is a promising noninvasive imaging method that has moderate or high accuracy in stratifying gliomas. DCE‐MRI shows high diagnostic accuracy in discriminating between HGGs and their low‐grade counterparts, and moderate diagnostic accuracy in discriminating recurrent lesions and treatment‐related changes as well as PCNSLs and HGGs.
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Affiliation(s)
- Sachi Okuchi
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | | | - Agne Ulyte
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Ingeborg Lopez
- Neuroradiology, Institute of Neurosurgery Dr. A. Asenjo, Santiago, Chile
| | - Jurgita Ušinskienė
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, National Cancer Institute, Vilnius University, Vilnius, Lithuania
| | - Martin Lewis
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Sara M Hassanein
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK.,Diagnostic Radiology Department, Faculty of Medicine, Assiut University, Assiut, Egypt
| | - Eser Sanverdi
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Xavier Golay
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Stefanie Thust
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
| | | | - Sotirios Bisdas
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
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Xi YB, Kang XW, Wang N, Liu TT, Zhu YQ, Cheng G, Wang K, Li C, Guo F, Yin H. Differentiation of primary central nervous system lymphoma from high-grade glioma and brain metastasis using arterial spin labeling and dynamic contrast-enhanced magnetic resonance imaging. Eur J Radiol 2019; 112:59-64. [DOI: 10.1016/j.ejrad.2019.01.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 12/02/2018] [Accepted: 01/07/2019] [Indexed: 01/22/2023]
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23
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Xu XQ, Qian W, Hu H, Su GY, Liu H, Shi HB, Wu FY. Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging for differentiating malignant from benign orbital lymphproliferative disorders. Acta Radiol 2019; 60:239-246. [PMID: 29804475 DOI: 10.1177/0284185118778873] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been used for assessing orbital lymphoproliferative disorders (OLPDs). However, only the mean values of quantitative parameters were obtained in previous studies and tumor heterogeneity was ignored. PURPOSE To assess the value of DCE-MRI derived histogram parameters in differentiating malignant from benign OLPDs. MATERIAL AND METHODS Forty-eight OLPDs patients (25 malignant and 23 benign) who had undergone DCE-MRI for pre-treatment evaluation were retrospectively included. Histogram parameters of Ktrans, kep, and ve were calculated and compared between two groups using the independent sample's t-test. Receiver operating characteristic (ROC) curve analyses were used to determine the diagnostic value of each significant parameter. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of malignant OLPDs. RESULTS Tenth kep, mean kep, median kep, and 90th kep were significantly higher in the malignant OLPD group than in the benign OLPD group. Tenth ve was significantly lower in the malignant OLPD group than in the benign OLPD group. Ninetieth kep was the only independent predictor of malignant OLPDs ( P = 0.019), with an area under ROC curve of 0.828, a sensitivity of 92.00%, and a specificity of 78.26% at a cut-off value of 1.057 min-1. CONCLUSION Histogram analysis of DCE-MRI derived parameters may help to differentiate malignant from benign OLPDs. The 90th kep hold the potential as an independent predictor for malignant OLPDs.
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Affiliation(s)
- Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Wen Qian
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Hao Hu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Guo-Yi Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Hu Liu
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Hai-Bin Shi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
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Kniep HC, Madesta F, Schneider T, Hanning U, Schönfeld MH, Schön G, Fiehler J, Gauer T, Werner R, Gellissen S. Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type. Radiology 2018; 290:479-487. [PMID: 30526358 DOI: 10.1148/radiol.2018180946] [Citation(s) in RCA: 152] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Purpose To investigate the feasibility of tumor type prediction with MRI radiomic image features of different brain metastases in a multiclass machine learning approach for patients with unknown primary lesion at the time of diagnosis. Materials and methods This single-center retrospective analysis included radiomic features of 658 brain metastases from T1-weighted contrast material-enhanced, T1-weighted nonenhanced, and fluid-attenuated inversion recovery (FLAIR) images in 189 patients (101 women, 88 men; mean age, 61 years; age range, 32-85 years). Images were acquired over a 9-year period (from September 2007 through December 2016) with different MRI units, reflecting heterogeneous image data. Included metastases originated from breast cancer (n = 143), small cell lung cancer (n = 151), non-small cell lung cancer (n = 225), gastrointestinal cancer (n = 50), and melanoma (n = 89). A total of 1423 quantitative image features and basic clinical data were evaluated by using random forest machine learning algorithms. Validation was performed with model-external fivefold cross validation. Comparative analysis of 10 randomly drawn cross-validation sets verified the stability of the results. The classifier performance was compared with predictions from a respective conventional reading by two radiologists. Results Areas under the receiver operating characteristic curve of the five-class problem ranged between 0.64 (for non-small cell lung cancer) and 0.82 (for melanoma); all P values were less than .01. Prediction performance of the classifier was superior to the radiologists' readings. Highest differences were observed for melanoma, with a 17-percentage-point gain in sensitivity compared with the sensitivity of both readers; P values were less than .02. Conclusion Quantitative features of routine brain MR images used in a machine learning classifier provided high discriminatory accuracy in predicting the tumor type of brain metastases. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Helge C Kniep
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Frederic Madesta
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Tanja Schneider
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Uta Hanning
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Michael H Schönfeld
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Gerhard Schön
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Jens Fiehler
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Tobias Gauer
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - René Werner
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Susanne Gellissen
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
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Di N, Cheng W, Chen H, Zhai F, Liu Y, Mu X, Chu Z, Lu N, Liu X, Wang B. Utility of arterial spin labelling MRI for discriminating atypical high-grade glioma from primary central nervous system lymphoma. Clin Radiol 2018; 74:165.e1-165.e9. [PMID: 30415766 DOI: 10.1016/j.crad.2018.10.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 10/09/2018] [Indexed: 01/19/2023]
Abstract
AIM To evaluate the ability of arterial spin labelling (ASL) magnetic resonance imaging (MRI) in differentiating primary central nervous system lymphoma (PCNSL) from atypical high-grade glioma (HGG), as well as exploring the underlying pathological mechanisms. METHODS AND MATERIALS Twenty-three patients with PCNSL and 17 patients with atypical HGG who underwent ASL-MRI were identified retrospectively. Absolute cerebral blood flow (aCBF) and normalised cerebral blood flow (nCBF) values were obtained, and were compared between PCNSL and atypical HGG using the Mann-Whitney U-test. The performance in discriminating between PCNSL and atypical HGG was evaluated using receiver-operating characteristics analysis and area-under-the-curve (AUC) values for aCBF and nCBF. The correlation between microvessel density (MVD) and aCBF was determined by Spearman's correlation analysis. RESULTS Atypical HGG demonstrated significantly higher aCBF, nCBF, and MVD values than PCNSL (p<0.05). The diagnostic accuracy of discriminating PCNSL from atypical HGG showed AUC=0.877 (95% confidence interval [CI] 0.735-0.959) for aCBF, and AUC=0.836 (95% confidence interval [CI] 0.685-0.934) for nCBF. There was a moderate positive correlation between aCBF values of region of interest (ROI >30 mm2) in the enhanced area and MVD values (rho=0.579, p=0.0001), and a strong positive correlation between aCBF values MVD based on "point-to-point biopsy" (rho=0.83, p=0.0029). Interobserver agreements for aCBF and nCBF were excellent (ICC >0.75). CONCLUSIONS ASL perfusion MRI is a useful imaging technique for the discrimination between atypical HGG and PCNSL, which may be determined by the difference of MVD between them.
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Affiliation(s)
- N Di
- Department of Radiology, Binzhou Medical University Hospital, 661 Huanghe 2nd Rd, 256603 Binzhou, China; Department of Radiology, Huashan Hospital Fudan University, 12 Wulumuqi Rd. Middle, 200040 Shanghai, China
| | - W Cheng
- Department of Pharmacy, Binzhou Medical University Hospital, 661 Huanghe 2nd Rd, 256603 Binzhou, China
| | - H Chen
- Department of Radiology, Weifang Traditional Chinese Hospital, 1055 Weizhou Rd, 261000 Weifang, China
| | - F Zhai
- Department of Radiology, Binzhou Medical University Hospital, 661 Huanghe 2nd Rd, 256603 Binzhou, China
| | - Y Liu
- Department of Pediatrics, Binzhou Medical University Hospital, 661 Huanghe 2nd Rd, 256603 Binzhou, China
| | - X Mu
- Department of Radiology, Binzhou Medical University Hospital, 661 Huanghe 2nd Rd, 256603 Binzhou, China
| | - Z Chu
- Department of Radiology, Binzhou Medical University Hospital, 661 Huanghe 2nd Rd, 256603 Binzhou, China
| | - N Lu
- Department of Radiology, Huashan Hospital Fudan University, 12 Wulumuqi Rd. Middle, 200040 Shanghai, China
| | - X Liu
- Department of Radiology, Binzhou Medical University Hospital, 661 Huanghe 2nd Rd, 256603 Binzhou, China.
| | - B Wang
- Department of Medical Imaging and Nuclear, Binzhou Medical University, 346 Guanhai Rd, 264000 Yantai, China.
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Zorofchian S, El-Achi H, Yan Y, Esquenazi Y, Ballester LY. Characterization of genomic alterations in primary central nervous system lymphomas. J Neurooncol 2018; 140:509-517. [PMID: 30171453 DOI: 10.1007/s11060-018-2990-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 08/22/2018] [Indexed: 01/01/2023]
Abstract
PURPOSE Primary central nervous system lymphoma (PCNSL) is a non-Hodgkin lymphoma that affects the central nervous system (CNS). Although previous studies have reported the most common mutated genes in PCNSL, including MYD88 and CD79b, our understanding of genetic characterizations in primary CNS lymphomas is limited. The aim of this study was to perform a retrospective analysis investigating the most frequent mutation types, and their frequency, in PCNSL. METHODS Fifteen patients with a diagnosis of PCNSL from our institution were analyzed for mutations in 406 genes and rearrangements in 31 genes by next generation sequencing (NGS). RESULTS Missense mutations were identified as the most common mutation type (32%) followed by frame shift mutations (23%). The highest mutation rate was reported in the MYD88 (33.3%), CDKN2A/B (33.3%), and TP53 (26.7%) genes. Intermediate tumor mutation burden (TMB) and high TMB was detected in 13.3% and 26.7% of PCNSL, respectively. The most frequent gene rearrangement involved the IGH-BCL6 genes (20%). CONCLUSIONS This study shows the most common genetic alterations in PCNSL as determined by a commercial next generation sequencing assay. MYD88 and CD79b are frequently mutated in PCNSL, IGH-BCL6 is the most frequent gene rearrangement and approximately 1/4 of cases show a high TMB. Mutations in multiple genes, in addition to high TMB and gene rearrangements, highlights the complex molecular heterogeneity of PCNSL. Knowledge about genetic alterations in PCNSL can inform the development of novel targets for diagnosis and treatment.
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Affiliation(s)
- Soheil Zorofchian
- Department of Pathology and Laboratory Medicine, University of Texas Health Science Center, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA
| | - Hanadi El-Achi
- Department of Pathology and Laboratory Medicine, University of Texas Health Science Center, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA
| | - Yuanqing Yan
- Vivian L. Smith Department of Neurosurgery, University of Texas Health Science Center, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA
| | - Yoshua Esquenazi
- Vivian L. Smith Department of Neurosurgery, University of Texas Health Science Center, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA.
| | - Leomar Y Ballester
- Department of Pathology and Laboratory Medicine, University of Texas Health Science Center, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA. .,Vivian L. Smith Department of Neurosurgery, University of Texas Health Science Center, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA.
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Lee B, Park JE, Bjørnerud A, Kim JH, Lee JY, Kim HS. Clinical Value of Vascular Permeability Estimates Using Dynamic Susceptibility Contrast MRI: Improved Diagnostic Performance in Distinguishing Hypervascular Primary CNS Lymphoma from Glioblastoma. AJNR Am J Neuroradiol 2018; 39:1415-1422. [PMID: 30026384 DOI: 10.3174/ajnr.a5732] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 05/01/2018] [Indexed: 12/17/2022]
Abstract
BACKGROUND AND PURPOSE A small subset of primary central nervous system lymphomas exhibits high cerebral blood volume, which is indistinguishable from that in glioblastoma on dynamic susceptibility contrast MR imaging. Our study aimed to test whether estimates of combined perfusion and vascular permeability metrics derived from DSC-MR imaging can improve the diagnostic performance in differentiating hypervascular primary central nervous system lymphoma from glioblastoma. MATERIALS AND METHODS A total of 119 patients (with 30 primary central nervous system lymphomas and 89 glioblastomas) exhibited hypervascular foci using the reference method of leakage-corrected CBV (reference-normalized CBV). An alternative postprocessing method used the tissue residue function to calculate vascular permeability (extraction fraction), leakage-corrected CBV, cerebral blood flow, and mean transit time. Parameters were compared using Mann-Whitney U tests, and the diagnostic performance to distinguish primary central nervous system lymphoma from glioblastoma was calculated using the area under the curve from the receiver operating characteristic curve and was cross-validated with bootstrapping. RESULTS Hypervascular primary central nervous system lymphoma showed similar leakage-corrected normalized CBV and leakage-corrected CBV compared with glioblastoma (P > .05); however, primary central nervous system lymphoma exhibited a significantly higher extraction fraction (P < .001) and CBF (P = .01) and shorter MTT (P < .001) than glioblastoma. The extraction fraction showed the highest diagnostic performance (the area under the receiver operating characteristic curve [AUC], 0.78; 95% confidence interval, 0.69-0.85) for distinguishing hypervascular primary central nervous system lymphoma from glioblastoma, with a significantly higher performance than both CBV (AUC, 0.53-0.59, largest P = .02) and CBF (AUC, 0.72) and MTT (AUC, 0.71). CONCLUSIONS Estimation of vascular permeability with DSC-MR imaging further characterizes hypervascular primary central nervous system lymphoma and improves diagnostic performance in glioblastoma differentiation.
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Affiliation(s)
- B Lee
- From the Department of Radiology (B.L.), Seoul Metropolitan Government-Seoul National University, Boramae Medical Center, Seoul, Korea
| | - J E Park
- Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K.), University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - A Bjørnerud
- Department of Diagnostic Physics (A.B.), Rikshopitalet University Hospital, Oslo, Norway
| | - J H Kim
- NordicNeuroLab (J.H.K.), Seoul, Korea
| | - J Y Lee
- Department of Radiology (J.Y.L.), Soonchunhyang University Bucheon Hospital, Bucheon, Korea
| | - H S Kim
- Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K.), University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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Conventional MRI. HANDBOOK OF CLINICAL NEUROLOGY 2018. [PMID: 29903441 DOI: 10.1016/b978-0-444-63956-1.00013-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
Conventional magnetic resonance imaging (MRI) allows for a detailed noninvasive visualization/examination of posterior fossa structures and represents a fundamental step in the diagnostic workup of many cerebellar disorders. In the first part of this chapter methodologic issues, like the correct choice of hardware (magnets, coils), pro and cons of the different MRI sequences, and patient management during the examination are discussed. In the second part, the MRI anatomy of the cerebellum, as noted on the various conventional MRI sequences, as well as a detailed description of cerebellar maturational processes from birth to childhood and into adulthood, are reported. Volumetric studies on the cerebellar growth based on three-dimensional MRI sequences are also presented. Moreover, we briefly discuss two main topics regarding conventional MRI of the cerebellum that have generated some debate in recent years: the differentiation between cerebellar atrophy, hypoplasia, and pontocerebellar hypoplasia, and signal changes of dentate nuclei after repetitive gadolinium-based contrast injections. The advantages and benefits of advanced neuroimaging techniques, including 1H magnetic resonance spectroscopy, diffusion-weighted imaging, diffusion tensor imaging, and perfusion-weighted imaging are discussed in the last section of the chapter.
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Di N, Cheng W, Jiang X, Liu X, Zhou J, Xie Q, Chu Z, Chen H, Wang B. Can dynamic contrast-enhanced MRI evaluate VEGF expression in brain glioma? An MRI-guided stereotactic biopsy study. J Neuroradiol 2018; 46:186-192. [PMID: 29752976 DOI: 10.1016/j.neurad.2018.04.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 02/16/2018] [Accepted: 04/21/2018] [Indexed: 12/18/2022]
Abstract
PURPOSE To investigate whether pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to evaluate vascular endothelial growth factor (VEGF) expression in brain glioma based on a point-to-point basis. MATERIALS AND METHODS Forty-seven patients with treatment-naïve glioma received preoperative DCE-MRI before stereotactic biopsy. We histologically quantified VEGF from section of stereotactic biopsies, and co-registered biopsy locations with localized measurements of DCE-MRI parameters including volume transfer coefficient (Ktrans), reverse reflux rate constant (Kep), extracellular extravascular volume fraction (Ve) and blood plasma volume (Vp). The correlations between DCE-MRI parameters (Ktrans, Kep, Ve and Vp) and VEGF were determined using Spearman correlation coefficient. P≤.05 was considered statistically significant. RESULTS Seventy-nine biopsy samples were obtained and graded into 45 high-grade gliomas (HGGs) and 34 low-grade gliomas (LGGs). Ktrans showed a significant positive correlation with VEGF expression in HGGs group (ρ=0.505, P<0.001) and in combined group (LGGs+HGGs) (ρ=0.549, P<0.001), but not in LGGs group (P>0.05). Kep, Ve or Vp was not correlated with VEGF even though a positive trend showed (P>0.05). CONCLUSIONS DCE-MRI is a useful, non-invasive imaging technique for quantitative evaluation of VEGF, and its parameter Ktrans other than Kep, Ve or Vp may be used as a surrogate for VEGF expression in brain gliomas.
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Affiliation(s)
- Ningning Di
- Department of Radiology, Binzhou Medical University Hospital, 661, Huanghe road, 256600 Binzhou, China; Department of Radiology, Huashan Hospital Fudan University, 12, Wulumuqi road Middle, 200040 Shanghai, China.
| | - Wenna Cheng
- Department of Pharmacy, Binzhou Medical University Hospital, 661, Huanghe road, 256600 Binzhou, China.
| | - Xingyue Jiang
- Department of Radiology, Binzhou Medical University Hospital, 661, Huanghe road, 256600 Binzhou, China.
| | - Xinjiang Liu
- Department of Radiology, Binzhou Medical University Hospital, 661, Huanghe road, 256600 Binzhou, China.
| | - Jinliang Zhou
- Department of Radiology, Binzhou Medical University Hospital, 661, Huanghe road, 256600 Binzhou, China.
| | - Qian Xie
- Department of Radiology, Huashan Hospital Fudan University, 12, Wulumuqi road Middle, 200040 Shanghai, China.
| | - Zhihui Chu
- Department of Radiology, Binzhou Medical University Hospital, 661, Huanghe road, 256600 Binzhou, China.
| | - Huacheng Chen
- Department of Radiology, Weifang Traditional Chinese Hospital, 1055, Weizhou road, 256600 Weifang, China.
| | - Bin Wang
- Department of Medical Imaging and Nuclear, Binzhou Medical University, 346, Guanhai road, 264000 Yantai, China.
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Perfusion MRI as a diagnostic biomarker for differentiating glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol 2018; 28:3819-3831. [PMID: 29619517 DOI: 10.1007/s00330-018-5335-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 01/01/2018] [Accepted: 01/16/2018] [Indexed: 10/17/2022]
Abstract
OBJECTIVES Differentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters. METHODS A computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity. RESULTS Eighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84-94%) and 91% (95% CI, 84-95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94-0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84-97%) and the pooled specificity was 94% (95% CI, 85-98%). CONCLUSIONS Although various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis. KEY POINTS • Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis. • The pooled sensitivity was 90% and pooled specificity was 91%. • Peritumoral rCBV derived from DSC is a relatively well-validated.
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31
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You SH, Yun TJ, Choi HJ, Yoo RE, Kang KM, Choi SH, Kim JH, Sohn CH. Differentiation between primary CNS lymphoma and glioblastoma: qualitative and quantitative analysis using arterial spin labeling MR imaging. Eur Radiol 2018; 28:3801-3810. [DOI: 10.1007/s00330-018-5359-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 01/19/2018] [Accepted: 01/29/2018] [Indexed: 10/17/2022]
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Nandu H, Wen PY, Huang RY. Imaging in neuro-oncology. Ther Adv Neurol Disord 2018; 11:1756286418759865. [PMID: 29511385 PMCID: PMC5833173 DOI: 10.1177/1756286418759865] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 01/18/2018] [Indexed: 12/11/2022] Open
Abstract
Imaging plays several key roles in managing brain tumors, including diagnosis, prognosis, and treatment response assessment. Ongoing challenges remain as new therapies emerge and there are urgent needs to find accurate and clinically feasible methods to noninvasively evaluate brain tumors before and after treatment. This review aims to provide an overview of several advanced imaging modalities including magnetic resonance imaging and positron emission tomography (PET), including advances in new PET agents, and summarize several key areas of their applications, including improving the accuracy of diagnosis and addressing the challenging clinical problems such as evaluation of pseudoprogression and anti-angiogenic therapy, and rising challenges of imaging with immunotherapy.
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Affiliation(s)
- Hari Nandu
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02445, USA
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Chen Y, Li Z, Wu G, Yu J, Wang Y, Lv X, Ju X, Chen Z. Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features. Int J Neurosci 2017; 128:608-618. [PMID: 29183170 DOI: 10.1080/00207454.2017.1408613] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
PURPOSE OF THE STUDY Due to the totally different therapeutic regimens needed for primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), accurate differentiation of the two diseases by noninvasive imaging techniques is important for clinical decision-making. MATERIALS AND METHODS Thirty cases of PCNSL and 66 cases of GBM with conventional T1-contrast magnetic resonance imaging (MRI) were analyzed in this study. Convolutional neural networks was used to segment tumor automatically. A modified scale invariant feature transform (SIFT) method was utilized to extract three-dimensional local voxel arrangement information from segmented tumors. Fisher vector was proposed to normalize the dimension of SIFT features. An improved genetic algorithm (GA) was used to extract SIFT features with PCNSL and GBM discrimination ability. The data-set was divided into a cross-validation cohort and an independent validation cohort by the ratio of 2:1. Support vector machine with the leave-one-out cross-validation based on 20 cases of PCNSL and 44 cases of GBM was employed to build and validate the differentiation model. RESULTS Among 16,384 high-throughput features, 1356 features show significant differences between PCNSL and GBM with p < 0.05 and 420 features with p < 0.001. A total of 496 features were finally chosen by improved GA algorithm. The proposed method produces PCNSL vs. GBM differentiation with an area under the curve (AUC) curve of 99.1% (98.2%), accuracy 95.3% (90.6%), sensitivity 85.0% (80.0%) and specificity 100% (95.5%) on the cross-validation cohort (and independent validation cohort). CONCLUSIONS Since the local voxel arrangement characterization provided by SIFT features, proposed method produced more competitive PCNSL and GBM differentiation performance by using conventional MRI than methods based on advanced MRI.
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Affiliation(s)
- Yinsheng Chen
- a Department of Neurosurgery/Neuro-oncology , Sun Yat-Sen University Cancer Center , Guangzhou , China.,c State Key Laboratory of Oncology in South China , Guangzhou , China
| | - Zeju Li
- b Department of Electronic Engineering , Fudan University , Shanghai , China
| | - Guoqing Wu
- b Department of Electronic Engineering , Fudan University , Shanghai , China
| | - Jinhua Yu
- b Department of Electronic Engineering , Fudan University , Shanghai , China.,d The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai , Shanghai , China
| | - Yuanyuan Wang
- b Department of Electronic Engineering , Fudan University , Shanghai , China.,d The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai , Shanghai , China
| | - Xiaofei Lv
- a Department of Neurosurgery/Neuro-oncology , Sun Yat-Sen University Cancer Center , Guangzhou , China
| | - Xue Ju
- a Department of Neurosurgery/Neuro-oncology , Sun Yat-Sen University Cancer Center , Guangzhou , China.,c State Key Laboratory of Oncology in South China , Guangzhou , China
| | - Zhongping Chen
- a Department of Neurosurgery/Neuro-oncology , Sun Yat-Sen University Cancer Center , Guangzhou , China.,c State Key Laboratory of Oncology in South China , Guangzhou , China
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Xu XQ, Qian W, Ma G, Hu H, Su GY, Liu H, Shi HB, Wu FY. Combined diffusion-weighted imaging and dynamic contrast-enhanced MRI for differentiating radiologically indeterminate malignant from benign orbital masses. Clin Radiol 2017; 72:903.e9-903.e15. [PMID: 28501096 DOI: 10.1016/j.crad.2017.04.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 03/30/2017] [Accepted: 04/10/2017] [Indexed: 11/29/2022]
Abstract
AIM To evaluate the performance of the combination of diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for differentiating radiologically indeterminate malignant from benign orbital masses. MATERIALS AND METHODS Sixty-five patients with orbital masses (36 benign and 29 malignant) underwent DW and DCE MRI examinations for pre-treatment evaluation. The apparent diffusion coefficient (ADC) was derived from DW imaging data using the mono-exponential model. The volume transfer constant (Ktrans), the flux rate constant between the extravascular extracellular space and the plasma (Kep), and the extravascular extracellular volume fraction (Ve) were calculated using modified Tofts model. Differences in quantitative metrics were tested using independent-samples t test. Receiver operating characteristic (ROC) curve analyses were used to determine and compare the diagnostic ability of each significant metric. RESULTS The malignant group demonstrated significantly lower ADC (0.711±0.260 versus 1.187±0.389, p<0.001) and higher Kep values (1.265±0.637 versus 0.871±0.610, p=0.008) than the benign group. Optimal diagnostic performance (area under the ROC curve [AUC], 0.941; sensitivity, 0.966; specificity, 0.917) could be achieved using combined ADC and Kep values as the diagnostic index. The diagnostic performance of the combination of ADC and Kep was significantly better than Kep alone (p=0.006). Compared with ADC alone, combined ADC and Kep values also showed higher AUC (0.941 versus 0.898), although the difference did not reach statistical significance (p=0.220). CONCLUSION Kep and ADC could help to differentiate radiologically indeterminate malignant from benign orbital masses. The combination of DW and DCE MRI might improve the differentiating performance.
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Affiliation(s)
- X-Q Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - W Qian
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - G Ma
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - H Hu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - G-Y Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - H Liu
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - H-B Shi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - F-Y Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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35
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Rochetams BB, Marechal B, Cottier JP, Gaillot K, Sembely-Taveau C, Sirinelli D, Morel B. T1-weighted dynamic contrast-enhanced brain magnetic resonance imaging: A preliminary study with low infusion rate in pediatric patients. Neuroradiol J 2017; 30:429-436. [PMID: 28556691 DOI: 10.1177/1971400917709626] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background The aim of this preliminary study is to evaluate the results of T1-weighted dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in pediatric patients at 1.5T, with a low peripheral intravenous gadoteric acid injection rate of 1 ml/s. Materials and methods Children with neurological symptoms were examined prospectively with conventional MRI and T1-weighted DCE MRI. An magnetic resonance perfusion analysis method was used to obtain time-concentration curves (persistent pattern, type-I; plateau pattern, type-II; washout pattern, type-III) and to calculate pharmacokinetic parameters. A total of two radiologists manually defined regions of interest (ROIs) in the part of the lesion exhibiting the greatest contrast enhancement and in the surrounding normal or contralateral tissue. Lesion/surrounding tissue or contralateral tissue pharmacokinetic parameter ratios were calculated. Tumors were categorized by grade (I-IV) using the World Health Organization (WHO) Grade. Mann-Whitney testing and receiver-operating characteristic (ROC) curves were performed. Results A total of nine boys and nine girls (mean age 10.5 years) were included. Lesions consisted of 10 brain tumors, 3 inflammatory lesions, 3 arteriovenous malformations and 2 strokes. We obtained analyzable concentration-time curves for all patients (6 type-I, 9 type-II, 3 type-III). Ktrans between tumor tissue and surrounding or contralateral tissue was significantly different ( p = 0.034). Ktrans ratios were significantly different between grade I tumors and grade IV tumors ( p = 0.027) and a Ktrans ratio value superior to 0.63 appeared to be discriminant to determine a grade IV of malignancy. Conclusions Our results confirm the feasibility of pediatric T1-weighted DCE MRI at 1.5T with a low injection rate, which could be of great value in differentiating brain tumor grades.
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Affiliation(s)
| | - Bénédicte Marechal
- 2 Advanced Clinical Imaging Technology, Siemens Healthcare HC CEMEA SUI DI PI, Lausanne, Switzerland.,3 Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland
| | - Jean-Philippe Cottier
- 4 Department of Neuroradiology, Bretonneau Hospital, CHRU, Tours, France.,5 Francois Rabelais University, Faculty of Medicine, Tours, France
| | - Kathleen Gaillot
- 4 Department of Neuroradiology, Bretonneau Hospital, CHRU, Tours, France.,5 Francois Rabelais University, Faculty of Medicine, Tours, France
| | | | - Dominique Sirinelli
- 1 Department of Pediatric Radiology, Clocheville Hospital, CHRU, Tours, France.,5 Francois Rabelais University, Faculty of Medicine, Tours, France
| | - Baptiste Morel
- 1 Department of Pediatric Radiology, Clocheville Hospital, CHRU, Tours, France.,5 Francois Rabelais University, Faculty of Medicine, Tours, France
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Xu W, Wang Q, Shao A, Xu B, Zhang J. The performance of MR perfusion-weighted imaging for the differentiation of high-grade glioma from primary central nervous system lymphoma: A systematic review and meta-analysis. PLoS One 2017; 12:e0173430. [PMID: 28301491 PMCID: PMC5354292 DOI: 10.1371/journal.pone.0173430] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 02/19/2017] [Indexed: 12/16/2022] Open
Abstract
It is always a great challenge to distinguish high-grade glioma (HGG) from primary central nervous system lymphoma (PCNSL). We conducted a meta-analysis to assess the performance of MR perfusion-weighted imaging (PWI) in differentiating HGG from PCNSL. The heterogeneity and threshold effect were evaluated, and the sensitivity (SEN), specificity (SPE) and areas under summary receiver operating characteristic curve (SROC) were calculated. Fourteen studies with a total of 598 participants were included in this meta-analysis. The results indicated that PWI had a high level of accuracy (area under the curve (AUC) = 0.9415) for differentiating HGG from PCNSL by using the best parameter from each study. The dynamic susceptibility-contrast (DSC) technique might be an optimal index for distinguishing HGGs from PCNSLs (AUC = 0.9812). Furthermore, the DSC had the best sensitivity 0.963 (95%CI: 0.924, 0.986), whereas the arterial spin-labeling (ASL) displayed the best specificity 0.896 (95% CI: 0.781, 0.963) among those techniques. However, the variability of the optimal thresholds from the included studies suggests that further evaluation and standardization are needed before the techniques can be extensively clinically used.
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Affiliation(s)
- Weilin Xu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qun Wang
- Department of Neurosurgery, Chinese PLA General Hospital, Haidian District, Beijing, China
| | - Anwen Shao
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Bainan Xu
- Department of Neurosurgery, Chinese PLA General Hospital, Haidian District, Beijing, China
- * E-mail: (JZ); (BX)
| | - Jianmin Zhang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Brain Research Institute, Zhejiang University, Hangzhou, Zhejiang, China
- Collaborative Innovation Center for Brain Science, Zhejiang University, Hangzhou, Zhejiang, China
- * E-mail: (JZ); (BX)
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