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Liu X, Zeng S, Tao T, Yang Z, Wu X, Zhao J, Zhang N. A comparative study of diffusion kurtosis imaging and diffusion tensor imaging in detecting corticospinal tract impairment in diffuse glioma patients. Neuroradiology 2024; 66:785-796. [PMID: 38478062 DOI: 10.1007/s00234-024-03332-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 03/04/2024] [Indexed: 04/21/2024]
Abstract
PURPOSE This study aimed to investigate the diagnostic performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in identifying aberrations in the corticospinal tract (CST), whilst elucidating the relationship between abnormalities of CST and patients' motor function. METHODS Altogether 21 patients with WHO grade II or grade IV glioma were enrolled and divided into Group 1 and Group 2, according to the presence or absence of preoperative paralysis. DKI and DTI metrics were generated and projected onto the CST. Histograms of the CST along x, y, and z axes were developed based on DKI and DTI metrics, and compared subsequently to determine regions of aberrations on the fibers. The receiver operating characteristic curve was performed to investigate the diagnostic efficacy of DKI and DTI metrics. RESULTS In Group 1, a significantly lower fractional anisotropy, radial kurtosis and mean kurtosis, and a higher mean diffusivity were found in the ipsilateral CST as compared to the contralateral CST. Significantly higher relative axial diffusivity, relative radial diffusivity, and relative mean diffusivity (rMD) were found in Group 1, as compared to Group 2. The relative volume of ipsilateral CST abnormalities higher than the maximum value of mean kurtosis combined with rMD exhibited the best diagnostic performance in distinguishing dysfunction of CST with an AUC of 0.93. CONCLUSION DKI is sensitive in detecting subtle changes of CST distal from the tumor. The combination of DKI and DTI is feasible for evaluating the impairment of the CST.
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Affiliation(s)
- Xinman Liu
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangdong Province, Guangzhou, China
| | - Shanmei Zeng
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangdong Province, Guangzhou, China
| | - Tao Tao
- Department of Informatics, The First Affiliated Hospital of Sun Yat-sen University, Guangdong Province, Guangzhou, China
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangdong Province, Guangzhou, China
| | - Xinjian Wu
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangdong Province, Guangzhou, China
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangdong Province, Guangzhou, China.
| | - Nu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangdong Province, Guangzhou, China.
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Romano A, Palizzi S, Romano A, Moltoni G, Di Napoli A, Maccioni F, Bozzao A. Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review. Cancers (Basel) 2023; 15:cancers15030618. [PMID: 36765575 PMCID: PMC9913305 DOI: 10.3390/cancers15030618] [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] [Received: 12/19/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.
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Affiliation(s)
- Andrea Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Serena Palizzi
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Allegra Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Giulia Moltoni
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- Correspondence: ; Tel.: +39-3347906958
| | - Alberto Di Napoli
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Francesca Maccioni
- Department of Radiology, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Alessandro Bozzao
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
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Du N, Zhou X, Mao R, Shu W, Xiao L, Ye Y, Xu X, Shen Y, Lin G, Fang X, Li S. Preoperative and Noninvasive Prediction of Gliomas Histopathological Grades and IDH Molecular Types Using Multiple MRI Characteristics. Front Oncol 2022; 12:873839. [PMID: 35712483 PMCID: PMC9196247 DOI: 10.3389/fonc.2022.873839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/05/2022] [Indexed: 01/30/2023] Open
Abstract
Background and Purpose Gliomas are one of the most common tumors in the central nervous system. This study aimed to explore the correlation between MRI morphological characteristics, apparent diffusion coefficient (ADC) parameters and pathological grades, as well as IDH gene phenotypes of gliomas. Methods Preoperative MRI data from 166 glioma patients with pathological confirmation were retrospectively analyzed to compare the differences of MRI characteristics and ADC parameters between the low-grade and high-grade gliomas (LGGs vs. HGGs), IDH mutant and wild-type gliomas (IDHmut vs. IDHwt). Multivariate models were constructed to predict the pathological grades and IDH gene phenotypes of gliomas and the performance was assessed by the receiver operating characteristic (ROC) analysis. Results Two multivariable logistic regression models were developed by incorporating age, ADC parameters, and MRI morphological characteristics to predict pathological grades, and IDH gene phenotypes of gliomas, respectively. The Noninvasive Grading Model classified tumor grades with areas under the ROC curve (AUROC) of 0.934 (95% CI=0.895-0.973), sensitivity of 91.2%, and specificity of 78.6%. The Noninvasive IDH Genotyping Model differentiated IDH types with an AUROC of 0.857 (95% CI=0.787-0.926), sensitivity of 88.2%, and specificity of 63.8%. Conclusion MRI features were correlated with glioma grades and IDH mutation status. Multivariable logistic regression models combined with MRI morphological characteristics and ADC parameters may provide a noninvasive and preoperative approach to predict glioma grades and IDH mutation status.
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Affiliation(s)
- Ningfang Du
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Xiaotao Zhou
- Department of Emergency, Changhai Hospital, Naval Medical University, Second Military Medical University, Shanghai, China
| | - Renling Mao
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Weiquan Shu
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Li Xiao
- Department of Pathology, Huadong Hospital, Fudan University, Shanghai, China
| | - Yao Ye
- Department of Pathology, Huadong Hospital, Fudan University, Shanghai, China
| | - Xinxin Xu
- Clinical Research Center for Gerontology, Huadong Hospital, Fudan University, Shanghai, China
| | - Yilang Shen
- Institute of Business Analytics, Adelphi University, Garden City, NY, United States
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Xuhao Fang
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Shihong Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
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Jiang L, Zhou L, Ai Z, Xiao C, Liu W, Geng W, Chen H, Xiong Z, Yin X, Chen YC. Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading. J Clin Med 2022; 11:jcm11092310. [PMID: 35566437 PMCID: PMC9105194 DOI: 10.3390/jcm11092310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/12/2022] [Accepted: 04/19/2022] [Indexed: 02/05/2023] Open
Abstract
Glioma grading plays an important role in surgical resection. We investigated the ability of different feature reduction methods in support vector machine (SVM)-based diffusion kurtosis imaging (DKI) histogram parameters to distinguish glioma grades. A total of 161 glioma patients who underwent magnetic resonance imaging (MRI) from January 2017 to January 2020 were included retrospectively. The patients were divided into low-grade (n = 61) and high-grade (n = 100) groups. Parametric DKI maps were derived, and 45 features from the DKI maps were extracted semi-automatically for analysis. Three feature selection methods [principal component analysis (PCA), recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO)] were used to establish the glioma grading model with an SVM classifier. To evaluate the performance of SVM models, the receiver operating characteristic (ROC) curves of SVM models for distinguishing glioma grades were compared with those of conventional statistical methods. The conventional ROC analysis showed that mean diffusivity (MD) variance, MD skewness and mean kurtosis (MK) C50 could effectively distinguish glioma grades, particularly MD variance. The highest classification distinguishing AUC was found using LASSO at 0.904 ± 0.069. In comparison, classification AUC by PCA was 0.866 ± 0.061, and 0.899 ± 0.079 by RFE. The SVM-PCA model with the lowest AUC among the SVM models was significantly better than the conventional ROC analysis (z = 1.947, p = 0.013). These findings demonstrate the superiority of DKI histogram parameters by LASSO analysis and SVM for distinguishing glioma grades.
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Affiliation(s)
- Liang Jiang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210029, China; (L.J.); (L.Z.); (Z.A.); (W.G.); (H.C.)
| | - Leilei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210029, China; (L.J.); (L.Z.); (Z.A.); (W.G.); (H.C.)
| | - Zhongping Ai
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210029, China; (L.J.); (L.Z.); (Z.A.); (W.G.); (H.C.)
| | - Chaoyong Xiao
- Department of Radiology, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China; (C.X.); (W.L.)
| | - Wen Liu
- Department of Radiology, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China; (C.X.); (W.L.)
| | - Wen Geng
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210029, China; (L.J.); (L.Z.); (Z.A.); (W.G.); (H.C.)
| | - Huiyou Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210029, China; (L.J.); (L.Z.); (Z.A.); (W.G.); (H.C.)
| | - Zhenyu Xiong
- Department of Radiation Oncology, Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ 08901, USA;
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210029, China; (L.J.); (L.Z.); (Z.A.); (W.G.); (H.C.)
- Correspondence: (X.Y.); (Y.-C.C.); Tel.: +86-2552271452 (Y.-C.C.)
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210029, China; (L.J.); (L.Z.); (Z.A.); (W.G.); (H.C.)
- Correspondence: (X.Y.); (Y.-C.C.); Tel.: +86-2552271452 (Y.-C.C.)
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Nuessle NC, Behling F, Tabatabai G, Castaneda Vega S, Schittenhelm J, Ernemann U, Klose U, Hempel JM. ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging. J Clin Med 2021; 10:jcm10163451. [PMID: 34441747 PMCID: PMC8397197 DOI: 10.3390/jcm10163451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 08/02/2021] [Indexed: 11/16/2022] Open
Abstract
PURPOSE To investigate the diagnostic performance of in vivo ADC-based stratification of integrated molecular glioma grades. MATERIALS AND METHODS Ninety-seven patients with histopathologically confirmed glioma were evaluated retrospectively. All patients underwent pre-interventional MRI-examination including diffusion-weighted imaging (DWI) with implemented b-values of 500, 1000, 1500, 2000, and 2500 s/mm2. Apparent Diffusion Coefficient (ADC), Mean Kurtosis (MK), and Mean Diffusivity (MD) maps were generated. The average values were compared among the molecular glioma subgroups of IDH-mutant and IDH-wildtype astrocytoma, and 1p/19q-codeleted oligodendroglioma. One-way ANOVA with post-hoc Games-Howell correction compared average ADC, MD, and MK values between molecular glioma groups. A Receiver Operating Characteristic (ROC) analysis determined the area under the curve (AUC). RESULTS Two b-value-dependent ADC-based evaluations presented statistically significant differences between the three molecular glioma sub-groups (p < 0.001, respectively). CONCLUSIONS High-b-value ADC from preoperative DWI may be used to stratify integrated molecular glioma subgroups and save time compared to diffusion kurtosis imaging. Higher b-values of up to 2500 s/mm2 may present an important step towards increasing diagnostic accuracy compared to standard DWI protocol.
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Affiliation(s)
- Nils C. Nuessle
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany; (U.E.); (U.K.); (J.-M.H.)
- Correspondence:
| | - Felix Behling
- Department of Neurosurgery, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany;
- Departments of Neurology and Interdisciplinary Neuro-Oncology, University Hospital Tübingen, Hertie Institute for Clinical Brain Research, Eberhard Karls University, 72076 Tübingen, Germany;
| | - Ghazaleh Tabatabai
- Departments of Neurology and Interdisciplinary Neuro-Oncology, University Hospital Tübingen, Hertie Institute for Clinical Brain Research, Eberhard Karls University, 72076 Tübingen, Germany;
| | - Salvador Castaneda Vega
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany;
| | - Jens Schittenhelm
- Department of Pathology and Neuropathology, University Hospital Tübingen, Institute of Neuropathology, Eberhard Karls University, 72076 Tübingen, Germany;
| | - Ulrike Ernemann
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany; (U.E.); (U.K.); (J.-M.H.)
| | - Uwe Klose
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany; (U.E.); (U.K.); (J.-M.H.)
| | - Johann-Martin Hempel
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany; (U.E.); (U.K.); (J.-M.H.)
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Glioma-Specific Diffusion Signature in Diffusion Kurtosis Imaging. J Clin Med 2021; 10:jcm10112325. [PMID: 34073442 PMCID: PMC8199055 DOI: 10.3390/jcm10112325] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 05/17/2021] [Accepted: 05/20/2021] [Indexed: 02/06/2023] Open
Abstract
Purpose: This study aimed to assess the relationship between mean kurtosis (MK) and mean diffusivity (MD) values from whole-brain diffusion kurtosis imaging (DKI) parametric maps in preoperative magnetic resonance (MR) images from 2016 World Health Organization Classification of Tumors of the Central Nervous System integrated glioma groups. Methods: Seventy-seven patients with histopathologically confirmed treatment-naïve glioma were retrospectively assessed between 1 August 2013 and 30 October 2017. The area on scatter plots with a specific combination of MK and MD values, not occurring in the healthy brain, was labeled, and the corresponding voxels were visualized on the fluid-attenuated inversion recovery (FLAIR) images. Reversely, the labeled voxels were compared to those of the manually segmented tumor volume, and the Dice similarity coefficient was used to investigate their spatial overlap. Results: A specific combination of MK and MD values in whole-brain DKI maps, visualized on a two-dimensional scatter plot, exclusively occurs in glioma tissue including the perifocal infiltrative zone and is absent in tissue of the normal brain or from other intracranial compartments. Conclusions: A unique diffusion signature with a specific combination of MK and MD values from whole-brain DKI can identify diffuse glioma without any previous segmentation. This feature might influence artificial intelligence algorithms for automatic tumor segmentation and provide new aspects of tumor heterogeneity.
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D'Amore F, Grinberg F, Mauler J, Galldiks N, Blazhenets G, Farrher E, Filss C, Stoffels G, Mottaghy FM, Lohmann P, Shah NJ, Langen KJ. Combined 18F-FET PET and diffusion kurtosis MRI in posttreatment glioblastoma: differentiation of true progression from treatment-related changes. Neurooncol Adv 2021; 3:vdab044. [PMID: 34013207 PMCID: PMC8117449 DOI: 10.1093/noajnl/vdab044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background Radiological differentiation of tumor progression (TPR) from treatment-related changes (TRC) in pretreated glioblastoma is crucial. This study aimed to explore the diagnostic value of diffusion kurtosis MRI combined with information derived from O-(2-[18F]-fluoroethyl)-l-tyrosine (18F-FET) PET for the differentiation of TPR from TRC in patients with pretreated glioblastoma. Methods Thirty-two patients with histomolecularly defined and pretreated glioblastoma suspected of having TPR were included in this retrospective study. Twenty-one patients were included in the TPR group, and 11 patients in the TRC group, as assessed by neuropathology or clinicoradiological follow-up. Three-dimensional (3D) regions of interest were generated based on increased 18F-FET uptake using a tumor-to-brain ratio of 1.6. Furthermore, diffusion MRI kurtosis maps were obtained from the same regions of interest using co-registered 18F-FET PET images, and advanced histogram analysis of diffusion kurtosis map parameters was applied to generated 3D regions of interest. Diagnostic accuracy was analyzed by receiver operating characteristic curve analysis and combinations of PET and MRI parameters using multivariate logistic regression. Results Parameters derived from diffusion MRI kurtosis maps show high diagnostic accuracy, up to 88%, for differentiating between TPR and TRC. Logistic regression revealed that the highest diagnostic accuracy of 94% (area under the curve, 0.97; sensitivity, 94%; specificity, 91%) was achieved by combining the maximum tumor-to-brain ratio of 18F-FET uptake and diffusion MRI kurtosis metrics. Conclusions The combined use of 18F-FET PET and MRI diffusion kurtosis maps appears to be a promising approach to improve the differentiation of TPR from TRC in pretreated glioblastoma and warrants further investigation.
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Affiliation(s)
- Francesco D'Amore
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Neuroradiology, Circolo Hospital and Macchi Foundation, Varese, Italy
| | - Farida Grinberg
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany
| | - Jörg Mauler
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany
| | - Norbert Galldiks
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Germany
| | - Ganna Blazhenets
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Nuclear Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ezequiel Farrher
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany
| | - Christian Filss
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Nuclear Medicine, RWTH Aachen University, Aachen, Germany
| | - Gabriele Stoffels
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany
| | - Felix M Mottaghy
- Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Germany.,Department of Nuclear Medicine, RWTH Aachen University, Aachen, Germany.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nadim Jon Shah
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Neurology, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN-Translational Medicine, Aachen, Germany
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Germany.,Department of Nuclear Medicine, RWTH Aachen University, Aachen, Germany
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Pogosbekian EL, Pronin IN, Zakharova NE, Batalov AI, Turkin AM, Konakova TA, Maximov II. Feasibility of generalised diffusion kurtosis imaging approach for brain glioma grading. Neuroradiology 2021; 63:1241-1251. [PMID: 33410948 PMCID: PMC8295088 DOI: 10.1007/s00234-020-02613-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 11/23/2020] [Indexed: 01/02/2023]
Abstract
Purpose An accurate differentiation of brain glioma grade constitutes an important clinical issue. Powerful non-invasive approach based on diffusion MRI has already demonstrated its feasibility in glioma grade stratification. However, the conventional diffusion tensor (DTI) and kurtosis imaging (DKI) demonstrated moderate sensitivity and performance in glioma grading. In the present work, we apply generalised DKI (gDKI) approach in order to assess its diagnostic accuracy and potential application in glioma grading. Methods Diffusion scalar metrics were obtained from 50 patients with different glioma grades confirmed by histological tests following biopsy or surgery. All patients were divided into two groups with low- and high-grade gliomas as grade II versus grades III and IV, respectively. For a comparison, trained radiologists segmented the brain tissue into three regions with solid tumour, oedema, and normal appearing white matter. For each region, we estimated the conventional and gDKI metrics including DTI maps. Results We found high correlations between DKI and gDKI metrics in high-grade glioma. Further, gDKI metrics enabled introduction of a complementary measure for glioma differentiation based on correlations between the conventional and generalised approaches. Both conventional and generalised DKI metrics showed quantitative maps of tumour heterogeneity and oedema behaviour. gDKI approach demonstrated largely similar sensitivity and specificity in low-high glioma differentiation as in the case of conventional DKI method. Conclusion The generalised diffusion kurtosis imaging enables differentiation of low- and high-grade gliomas at the same level as the conventional DKI. Additionally, gDKI exhibited higher sensitivity to tumour heterogeneity and tissue contrast between tumour and healthy tissue and, thus, may contribute as a complementary source of information on tumour differentiation.
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Affiliation(s)
- E L Pogosbekian
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation.,General and Clinical Neurophysiology Lab, Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russian Federation
| | - I N Pronin
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation
| | - N E Zakharova
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation
| | - A I Batalov
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation
| | - A M Turkin
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation
| | - T A Konakova
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation
| | - I I Maximov
- Department of Psychology, University of Oslo, Oslo, Norway. .,Department of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway. .,Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
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9
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Lesbats C, Kelly CL, Czanner G, Poptani H. Diffusion kurtosis imaging for characterizing tumor heterogeneity in an intracranial rat glioblastoma model. NMR IN BIOMEDICINE 2020; 33:e4386. [PMID: 32729637 DOI: 10.1002/nbm.4386] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 07/14/2020] [Accepted: 07/15/2020] [Indexed: 06/11/2023]
Abstract
The utility of diffusion kurtosis imaging (DKI) for assessing intra-tumor heterogeneity was evaluated in a rat model of glioblastoma multiforme. Longitudinal MRI including T2 -weighted and diffusion-weighted MRI (DWI) was performed on six female Fischer rats 8, 11 and 14 days after intracranial transplantation of F98 cells. T2 -weighted images were used to measure the tumor volumes and DWI images were used to compute diffusion tensor imaging (DTI) and DWI based parametric maps including mean diffusivity (MD), mean kurtosis (MK), axial diffusivity (AD), axial kurtosis, radial diffusivity, radial kurtosis, fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA). Median values from the segmented normal contralateral cortex, tumor and edema from the diffusion parameters were compared at the three imaging time points to assess any changes in tumor heterogeneity over time. ex vivo DKI was also performed in a representative sample and compared with histology. Significant differences were observed between normal cortex, tumor and edema in both the DTI and DKI parameters. Notably, at the earliest time point MK and KFA were significantly different between normal cortex and tumor in comparison with MD or FA. Although a decreasing trend in MD, AD and FA values of the tumor were observed as the tumor grew, no significant changes in any of the DTI or DKI parameters were observed longitudinally. While DKI was equally sensitive to DTI in differentiating tumor from edema and normal brain, it was unable to detect longitudinal increases in intra-tumoral heterogeneity in the F98 model of glioblastoma multiforme.
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Affiliation(s)
- Clémentine Lesbats
- Centre for Preclinical Imaging, Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Claire Louise Kelly
- Centre for Preclinical Imaging, Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Gabriela Czanner
- Department of Applied Mathematics, Liverpool John Moores University, Liverpool, UK
| | - Harish Poptani
- Centre for Preclinical Imaging, Institute of Translational Medicine, University of Liverpool, Liverpool, UK
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Jiang Y, Li C, Liu Y, Shi K, Zhang W, Liu M, Chen M. Histogram analysis in prostate cancer: a comparison of diffusion kurtosis imaging model versus monoexponential model. Acta Radiol 2020; 61:1431-1440. [PMID: 32008343 DOI: 10.1177/0284185120901504] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND There is still little research about histogram analysis of diffusion kurtosis imaging (DKI) using in prostate cancer at present. PURPOSE To verify the utility of histogram analysis of DKI model in detection and assessment of aggressiveness of prostate cancer, compared with monoexponential model (MEM). MATERIAL AND METHODS Twenty-three patients were enrolled in this study. For DKI model and MEM, the Dapp, Kapp, and apparent diffusion coefficient (ADC) were obtained by using single-shot echo-planar imaging sequence. The pathologies were confirmed by in-bore magnetic resonance (MR)-guided biopsy. Regions of interest (ROI) were drawn manually in the position where biopsy needle was put. The mean values and histogram parameters in cancer and noncancerous foci were compared using independent-samples T test. Receiver operating characteristic curves were used to investigate the diagnostic efficiency. Spearman's test was used to evaluate the correlation of parameters and Gleason scores. RESULTS The mean, 10th, 25th, 50th, 75th, and 90th percentiles of ADC and Dapp were significantly lower in prostate cancer than non-cancerous foci (P < 0.001). The mean, 50th, 75th, and 90th percentiles of Kapp were significantly higher in prostate cancer (P < 0.05). There was no significant difference between the AUCs of two models (0.971 vs. 0.963, P > 0.05). With the increasing Gleason scores, the 10th ADC decreased (ρ = -0.583, P = 0.018), but the 90th Kapp increased (ρ = 0.642, P = 0.007). CONCLUSION Histogram analysis of DKI model is feasible for diagnosing and grading prostate cancer, but it has no significant advantage over MEM.
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Affiliation(s)
- Yuwei Jiang
- Peking University Fifth School of Clinical Medicine, Beijing, China
- Radiology Department, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Chunmei Li
- Peking University Fifth School of Clinical Medicine, Beijing, China
- Radiology Department, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Ying Liu
- Radiology Department, Beijing Hospital, National Center of Gerontology, Beijing, China
- Radiology Department, Civil Aviation General Hospital, Civil Aviation Clinical Medical College of Peking University, Beijing, China
| | | | - Wei Zhang
- Pathology Department, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Ming Liu
- Urological Surgical Department, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Min Chen
- Peking University Fifth School of Clinical Medicine, Beijing, China
- Radiology Department, Beijing Hospital, National Center of Gerontology, Beijing, China
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11
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Wang H, Lin J, Zheng L, Zhao J, Song B, Dai Y. Texture analysis based on ADC maps and T2-FLAIR images for the assessment of the severity and prognosis of ischaemic stroke. Clin Imaging 2020; 67:152-159. [PMID: 32739735 DOI: 10.1016/j.clinimag.2020.06.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 05/12/2020] [Accepted: 06/07/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVES To explore the feasibility of texture analysis based on T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images and apparent diffusion coefficient (ADC) maps in the assessment of the severity and prognosis of ischaemic stroke using the National Institutes of Health Stroke Scale (NIHSS) and modified Rankin scale (mRS) scores, respectively. METHODS Overall, 116 patients diagnosed with subacute ischaemic stroke were included in this retrospective study. Based on T2-FLAIR images and ADC maps, 15 texture features were extracted from the ROIs of each patient using grey-level co-occurrence matrix (GLCM) and local binary pattern histogram Fourier (LBP-HF) methods. The correlations of NIHSS score on admission (NIHSSbaseline), NIHSS score 24 h after stroke onset (NIHSS24h) and mRS score with the texture features were evaluated using Spearman's partial correlations. The receiver operating characteristic (ROC) curve was used to compare the performance of the selected texture features in the evaluation of stroke severity and prognosis. RESULTS Texture features derived from the T2-FLAIR images and ADC maps were correlated with NIHSS score and mRS score. EntropyADC and 0.75QuantileT2-FLAIR showed the best diagnostic performance for assessing stroke severity. The combination of EntropyADC and 0.75QuantileT2-FLAIR achieved a better performance in the evaluation of stroke severity (AUC = 0.7, p = 0.01) than either feature alone. Only 0.05QuantileT2-FLAIR was found to be correlated with mRS score, and none of the texture features were predictive of mRS score. CONCLUSION Texture features derived from T2-FLAIR images and ADC maps might serve as biomarkers to evaluate stroke severity, but were insufficient to predict stroke prognosis.
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Affiliation(s)
- Hao Wang
- Department of Radiology, Minhang Hospital, Fudan University, Shanghai, China
| | - Jixian Lin
- Department of Neurology, Minhang Hospital, Fudan University, Shanghai, China; Department of Electronic Engineering, Fudan University, Shanghai, China
| | - Liyun Zheng
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Jing Zhao
- Department of Neurology, Minhang Hospital, Fudan University, Shanghai, China
| | - Bin Song
- Department of Radiology, Minhang Hospital, Fudan University, Shanghai, China.
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, Shanghai, China
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12
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Abdalla G, Dixon L, Sanverdi E, Machado PM, Kwong JSW, Panovska-Griffiths J, Rojas-Garcia A, Yoneoka D, Veraart J, Van Cauter S, Abdel-Khalek AM, Settein M, Yousry T, Bisdas S. The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis. Neuroradiology 2020; 62:791-802. [PMID: 32367349 PMCID: PMC7311378 DOI: 10.1007/s00234-020-02425-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 03/27/2020] [Indexed: 12/18/2022]
Abstract
Purpose We aim to illustrate the diagnostic performance of diffusional kurtosis imaging (DKI) in the diagnosis of gliomas. Methods A review protocol was developed according to the (PRISMA-P) checklist, registered in the international prospective register of systematic reviews (PROSPERO) and published. A literature search in 4 databases was performed using the keywords ‘glioma’ and ‘diffusional kurtosis’. After applying a robust inclusion/exclusion criteria, included articles were independently evaluated according to the QUADAS-2 tool and data extraction was done. Reported sensitivities and specificities were used to construct 2 × 2 tables and paired forest plots using the Review Manager (RevMan®) software. A random-effect model was pursued using the hierarchical summary receiver operator characteristics. Results A total of 216 hits were retrieved. Considering duplicates and inclusion criteria, 23 articles were eligible for full-text reading. Ultimately, 19 studies were eligible for final inclusion. The quality assessment revealed 9 studies with low risk of bias in the 4 domains. Using a bivariate random-effect model for data synthesis, summary ROC curve showed a pooled area under the curve (AUC) of 0.92 and estimated sensitivity of 0.87 (95% CI 0.78–0.92) in high-/low-grade gliomas’ differentiation. A mean difference in mean kurtosis (MK) value between HGG and LGG of 0.22 (95% CI 0.25–0.19) was illustrated (p value = 0.0014) with moderate heterogeneity (I2 = 73.8%). Conclusion DKI shows good diagnostic accuracy in the differentiation of high- and low-grade gliomas further supporting its potential role in clinical practice. Further exploration of DKI in differentiating IDH status and in characterising non-glioma CNS tumours is however needed. Electronic supplementary material The online version of this article (10.1007/s00234-020-02425-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gehad Abdalla
- The Neuroradiological Academic Unit, BRR, UCL IoN, London, UK.
- Department of Radiology, Mansoura university hospitals, Mansoura, Egypt.
- Imaging Analysis Centre, Queen Square 8-11, London, WC1N 3BG, UK.
| | - Luke Dixon
- The Neuroradiological Academic Unit, BRR, UCL IoN, London, UK
- The National Hospital for Neurology and Neurosurgery UCL Hospitals NHS Trust, London, UK
| | - Eser Sanverdi
- The Neuroradiological Academic Unit, BRR, UCL IoN, London, UK
- The National Hospital for Neurology and Neurosurgery UCL Hospitals NHS Trust, London, UK
| | - Pedro M Machado
- MRC Centre for Neuromuscular Diseases & Centre for Rheumatology, University College London, London, UK
| | - Joey S W Kwong
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Jasmina Panovska-Griffiths
- NIHR CLAHRC North Thames, Department of Applied Health Research, University College London, London, UK
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Antonio Rojas-Garcia
- NIHR CLAHRC North Thames, Department of Applied Health Research, University College London, London, UK
| | - Daisuke Yoneoka
- Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Jelle Veraart
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | | | | | - Magdy Settein
- Department of Radiology, Mansoura university hospitals, Mansoura, Egypt
| | - Tarek Yousry
- The Neuroradiological Academic Unit, BRR, UCL IoN, London, UK
- The National Hospital for Neurology and Neurosurgery UCL Hospitals NHS Trust, London, UK
| | - Sotirios Bisdas
- The Neuroradiological Academic Unit, BRR, UCL IoN, London, UK
- The National Hospital for Neurology and Neurosurgery UCL Hospitals NHS Trust, London, UK
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13
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Maynard J, Okuchi S, Wastling S, Busaidi AA, Almossawi O, Mbatha W, Brandner S, Jaunmuktane Z, Koc AM, Mancini L, Jäger R, Thust S. World Health Organization Grade II/III Glioma Molecular Status: Prediction by MRI Morphologic Features and Apparent Diffusion Coefficient. Radiology 2020; 296:111-121. [PMID: 32315266 DOI: 10.1148/radiol.2020191832] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background A readily implemented MRI biomarker for glioma genotyping is currently lacking. Purpose To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) status in patients with glioma. Materials and Methods In this retrospective study of patients studied from July 2008 to February 2019, untreated World Health Organization (WHO) grade II/III gliomas were analyzed by three neuroradiologists blinded to tissue results. Apparent diffusion coefficient (ADC) minimum (ADCmin) and mean (ADCmean) regions of interest were defined in tumor and normal appearing white matter (ADCNAWM). A visual rating of anatomic features (T1 weighted, T1 weighted with contrast enhancement, T2 weighted, and fluid-attenuated inversion recovery) was performed. Interobserver comparison (intraclass correlation coefficient and Cohen κ) was followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between ADC metrics and glioma genotypes, including Bonferroni correction for multiple testing. Descriptors with sufficient concordance (intraclass correlation coefficient, >0.8; κ > 0.6) underwent univariable analysis. Predictive variables (P < .05) were entered into a multivariable logistic regression and tested in an additional test sample of patients with glioma. Results The study included 290 patients (median age, 40 years; interquartile range, 33-52 years; 169 male patients) with 82 IDH wild-type, 107 IDH mutant/1p19q intact, and 101 IDH mutant/1p19q codeleted gliomas. Two predictive models incorporating ADCmean-to-ADCNAWM ratio, age, and morphologic characteristics, with model A mandating calcification result and model B recording cyst formation, classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.91, 0.97) and 0.96 (95% CI: 0.93, 0.98), respectively. In the test sample of 49 gliomas (nine IDH wild type, 21 IDH mutant/1p19q intact, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% CI: 71%, 92%) for model A and 42 of 49 gliomas (86%; 95% CI: 76%, 96%) for model B. Conclusion Two algorithms that incorporated apparent diffusion coefficient values, age, and tumor morphologic characteristics predicted isocitrate dehydrogenase status in World Health Organization grade II/III gliomas on the basis of standard clinical MRI sequences alone. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- John Maynard
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Sachi Okuchi
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Stephen Wastling
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Ayisha Al Busaidi
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Ofran Almossawi
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Wonderboy Mbatha
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Sebastian Brandner
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Zane Jaunmuktane
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Ali Murat Koc
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Laura Mancini
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Rolf Jäger
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Stefanie Thust
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
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Di Trani MG, Nezzo M, Caporale AS, De Feo R, Miano R, Mauriello A, Bove P, Manenti G, Capuani S. Performance of Diffusion Kurtosis Imaging Versus Diffusion Tensor Imaging in Discriminating Between Benign Tissue, Low and High Gleason Grade Prostate Cancer. Acad Radiol 2019; 26:1328-1337. [PMID: 30545680 DOI: 10.1016/j.acra.2018.11.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 11/19/2018] [Accepted: 11/21/2018] [Indexed: 12/25/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in discriminating benign tissue, low- and high-grade prostate adenocarcinoma (PCa). MATERIALS AND METHODS Forty-eight patients with biopsy-proven PCa of different Gleason grade (GG), who provided written informed consent, were enrolled. All subjects underwent 3T DWI examinations by using b values 0, 500, 1000, 1500, 2000, and 2500 s/mm2 and six gradient directions. Mean diffusivity, fractional anisotropy (FA), apparent kurtosis (K), apparent kurtosis-derived diffusivity (D), and proxy fractional kurtosis anisotropy (KFA) maps were obtained. Regions of interest were selected in PCa, in the contralateral benign zone, and in the peritumoral area. Histogram analysis was performed by measuring mean, 10th, 25th, and 90th (p90) percentile of the whole-lesion volume. Kruskal-Wallis test with Bonferroni correction was used to assess significant differences between different regions of interest. The correlation between diffusion metrics and GG and between DKI and DTI parameters was evaluated with Pearson's test. ROC curve analysis was carried out to analyze the ability of histogram variables to differentiate low- and high-GG PCa. RESULTS All metrics significantly discriminated PCa from benign and from peritumoral tissue (except for K, KFAp90, and FA). Kp90 showed the highest correlation with GG and the best diagnostic ability (area under the curve = 0.84) in discriminating low- from high-risk PCa. CONCLUSION Compared to DTI, DKI provides complementary and additional information about prostate cancer tissue, resulting more sensitive to PCa-derived modifications and more accurate in discriminating low- and high-risk PCa.
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Affiliation(s)
- Maria Giovanna Di Trani
- Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Rome, Italy; Department of Anatomical, Histological, Forensic and Locomotor System Science, Sapienza University of Rome, Via A. Scarpa 16, Rome 00161, Italy.
| | - Marco Nezzo
- Department of Diagnostic and Interventional Radiology, Molecular Imaging and Radiotherapy, PTV Foundation, Tor Vergata University of Rome, Rome, Italy
| | - Alessandra S Caporale
- Department of Physics, CNR ISC, UOS Roma Sapienza, Sapienza University of Rome, Rome, Italy; Department of Radiology, University of Pennsylvania Hospital, Founders Pavilion, Philadelphia, Pennsylvania
| | - Riccardo De Feo
- Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Rome, Italy; Department of Physics, CNR ISC, UOS Roma Sapienza, Sapienza University of Rome, Rome, Italy
| | - Roberto Miano
- Urology Unit, Department of Experimental Medicine and Surgery, PTV Foundation, Tor Vergata University of Rome, Rome, Italy
| | - Alessandro Mauriello
- Anatomic Pathology, Department of Experimental Medicine and Surgery, PTV Foundation, Tor Vergata University of Rome, Rome, Italy
| | - Pierluigi Bove
- Urology Unit, Department of Experimental Medicine and Surgery, PTV Foundation, Tor Vergata University of Rome, Rome, Italy
| | - Guglielmo Manenti
- Department of Diagnostic and Interventional Radiology, Molecular Imaging and Radiotherapy, PTV Foundation, Tor Vergata University of Rome, Rome, Italy
| | - Silvia Capuani
- Department of Physics, CNR ISC, UOS Roma Sapienza, Sapienza University of Rome, Rome, Italy
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15
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Lewis MA, Ganeshan B, Barnes A, Bisdas S, Jaunmuktane Z, Brandner S, Endozo R, Groves A, Thust SC. Filtration-histogram based magnetic resonance texture analysis (MRTA) for glioma IDH and 1p19q genotyping. Eur J Radiol 2019; 113:116-123. [PMID: 30927935 DOI: 10.1016/j.ejrad.2019.02.014] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 02/12/2019] [Indexed: 10/27/2022]
Abstract
BACKGROUND To determine if filtration-histogram based texture analysis (MRTA) of clinical MR imaging can non-invasively identify molecular subtypes of untreated gliomas. METHODS Post Gadolinium T1-weighted (T1+Gad) images, T2-weighted (T2) images and apparent diffusion coefficient (ADC) maps of 97 gliomas (54 = WHO II, 20 = WHO III, 23 = WHO IV) between 2010 and 2016 were studied. Whole-tumor segmentations were performed on a proprietary texture analysis research platform (TexRAD, Cambridge, UK) using the software's freehand drawing tool. MRTA commences with a filtration step, followed by quantification of texture using histogram texture parameters. Results were correlated using non-parametric statistics with a logistic regression model generated. RESULTS T1+Gad performed best for IDH typing of glioblastoma (sensitivity 91.9%, specificity 100%, AUC 0.945) and ADC for non-Gadolinium-enhancing gliomas (sensitivity 85.7%, specificity 78.4%, AUC 0.877). T2 was moderately precise (sensitivity 83.1%, specificity 78.9%, AUC 0.821). Excellent results for IDH typing were achieved from a combination of the three sequences (sensitivity 90.5%, specificity 94.5%, AUC = 0.98). For discriminating 1p19q genotypes, ADC produced the best results using unfiltered textures (sensitivity 80.6%, specificity 89.3%, AUC 0.811). CONCLUSION Preoperative glioma genotyping with MRTA appears valuable with potential for clinical translation. The optimal choice of texture parameters is influenced by sequence choice, tumour morphology and segmentation method.
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Affiliation(s)
- Martin A Lewis
- Institute of Neurology, University College London, London, UK
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anna Barnes
- Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK
| | - Sotirios Bisdas
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK; Department of Brain Rehabilitation and Repair, UCL Institute of Neurology, Queen Square, London, UK
| | - Zane Jaunmuktane
- Division of Neuropathology, National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, UK
| | - Sebastian Brandner
- Division of Neuropathology, National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, UK
| | - Raymond Endozo
- Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK
| | - Ashley Groves
- Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK
| | - Stefanie C Thust
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK; Department of Brain Rehabilitation and Repair, UCL Institute of Neurology, Queen Square, London, UK.
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Hempel JM, Brendle C, Bender B, Bier G, Kraus MS, Skardelly M, Richter H, Eckert F, Schittenhelm J, Ernemann U, Klose U. Diffusion kurtosis imaging histogram parameter metrics predicting survival in integrated molecular subtypes of diffuse glioma: An observational cohort study. Eur J Radiol 2019; 112:144-152. [PMID: 30777204 DOI: 10.1016/j.ejrad.2019.01.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 11/22/2018] [Accepted: 01/14/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE The aim of the study was to assess the predictive value of preoperatively assessed diffusion kurtosis imaging (DKI) metrics as prognostic factors in the 2016 World Health Organization Classification of Tumors of the Central Nervous System integrated glioma groups. MATERIAL AND METHODS Seventy-seven patients with histopathologically confirmed treatment-naïve glioma were retrospectively assessed between 08/2013 and 10/2017 using mean kurtosis (MK) and mean diffusivity (MD) histogram parameters from DKI, overall and progression-free survival, and relevant prognostic molecular data (isocitrate dehydrogenase, [IDH]; alpha-thalassemia/mental retardation syndrome X-linked, [ATRX]; chromosome 1p/19q loss of heterozygosity). Receiver operating characteristic (ROC) analysis was performed on metric variables to determine the optimal cutoff-values. The Kaplan-Meier method was used to assess univariate survival data. A multivariate Cox proportional hazards model was performed on significant results from the univariate analysis. RESULTS There were significant differences in overall and progression-free survival between patient age (p = 0.001), resection statuses (p = 0.002), WHO glioma grades (p < 0.0001), and integrated molecular profiles (p < 0.0001). Survival was significantly better in patients with lower MK and higher MD values globally (p = 0.009), in gliomas without chromosome 1p/19q LOH (p < 0.0001), and those with retained ATRX expression (p = 0.008). CONCLUSIONS Patient age and MK from DKI from DKI are relevant factors for preoperatively predicting overall and progression-free survival. Regarding the molecular subgroups, they seem to be predictive in gliomas with ATRX retention, representing a feature of IDH wild-type gliomas.
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Affiliation(s)
- Johann-Martin Hempel
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany; Center for CNS Tumors, Comprehensive Cancer Center Tübingen-Stuttgart, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany.
| | - Cornelia Brendle
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany; Center for CNS Tumors, Comprehensive Cancer Center Tübingen-Stuttgart, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany
| | - Benjamin Bender
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany; Center for CNS Tumors, Comprehensive Cancer Center Tübingen-Stuttgart, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany
| | - Georg Bier
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany; Center for CNS Tumors, Comprehensive Cancer Center Tübingen-Stuttgart, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany
| | - Mareen Sarah Kraus
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany
| | - Marco Skardelly
- Department of Neurosurgery, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany; Interdisciplinary Division of Neuro-Oncology, Departments of Neurology and Neurosurgery, University Hospital Tübingen, Hertie Institute for Clinical Brain Research, Eberhard Karls University, Tübingen, Germany; Center for CNS Tumors, Comprehensive Cancer Center Tübingen-Stuttgart, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany
| | - Hardy Richter
- Interdisciplinary Division of Neuro-Oncology, Departments of Neurology and Neurosurgery, University Hospital Tübingen, Hertie Institute for Clinical Brain Research, Eberhard Karls University, Tübingen, Germany
| | - Franziska Eckert
- Department of Radiation Oncology, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany; Center for CNS Tumors, Comprehensive Cancer Center Tübingen-Stuttgart, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany
| | - Jens Schittenhelm
- Institute of Neuropathology, Department of Pathology and Neuropathology, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany; Center for CNS Tumors, Comprehensive Cancer Center Tübingen-Stuttgart, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany
| | - Ulrike Ernemann
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany; Center for CNS Tumors, Comprehensive Cancer Center Tübingen-Stuttgart, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany
| | - Uwe Klose
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany
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Abdalla G, Sanverdi E, Machado PM, Kwong JSW, Panovska-Griffiths J, Rojas-Garcia A, Yoneoka D, Yousry T, Bisdas S. Role of diffusional kurtosis imaging in grading of brain gliomas: a protocol for systematic review and meta-analysis. BMJ Open 2018; 8:e025123. [PMID: 30552282 PMCID: PMC6303635 DOI: 10.1136/bmjopen-2018-025123] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
INTRODUCTION Central nervous system (CNS) gliomas are the most common primary intra-axial brain tumours and pose variable treatment response according to their grade, therefore, precise staging is mandatory. Histopathological analysis of surgical tumour samples is still deemed as the state-of-the-art staging technique for gliomas due to the moderate specificity of the available non-invasive imaging modalities. A recently evolved analysis of the tissue water diffusion properties, known as diffusional kurtosis imaging (DKI), is a dimensionless metric, which quantifies water molecules' degree of non-Gaussian diffusion, hence reflects tissue microenvironment's complexity by means of non-invasive diffusion-weighted MRI acquisitions. The objective of this systematic review and meta-analysis is to explore the performance of DKI in the presurgical grading of gliomas, both regarding the differentiation between high-grade and low-grade gliomas as well as the discrimination between gliomas and other intra-axial brain tumours. METHODS AND ANALYSIS We will search PubMed, Medline via Ovid, Embase and Scopus in July 2018 for research studies published between January 1990 and June 2018 with no language restrictions, which have reported on the performance of DKI in diagnosing CNS gliomas. Robust inclusion/exclusion criteria will be applied for selection of eligible articles. Two authors will separately perform quality assessment according to the quality assessment of diagnostic accuracy studies-2 tool. Data will be extracted in a predesigned spreadsheet. A meta-analysis will be held using a random-effects model if substantial statistical heterogeneity is expected. The heterogeneity of studies will be evaluated, and sensitivity analyses will be conducted according to individual study quality. ETHICS AND DISSEMINATION This work will be based on published studies; hence, it does not require institutional review board approval or ethics clearance. The results will be published in peer-reviewed journals. PROSPERO REGISTRATION NUMBER CRD42018099192.
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Affiliation(s)
- Gehad Abdalla
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery UCL Hospitals NHS Trust, London, UK
| | - Eser Sanverdi
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery UCL Hospitals NHS Trust, London, UK
| | - Pedro M Machado
- MRC Centre for Neuromuscular Diseases & Centre for Rheumatology, University College London, London, UK
| | - Joey S W Kwong
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong kong, China
| | - Jasmina Panovska-Griffiths
- NIHR CLAHRC North Thames, Department of Applied Health Research, University College London, London, UK
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
| | - Antonio Rojas-Garcia
- NIHR CLAHRC North Thames, Department of Applied Health Research, University College London, London, UK
| | - Daisuke Yoneoka
- Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Tarek Yousry
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery UCL Hospitals NHS Trust, London, UK
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, United Kingdom
| | - Sotirios Bisdas
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery UCL Hospitals NHS Trust, London, UK
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, United Kingdom
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Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status. J Neurooncol 2018; 141:195-203. [PMID: 30414095 DOI: 10.1007/s11060-018-03025-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 10/03/2018] [Indexed: 10/27/2022]
Abstract
INTRODUCTION Few studies have applied diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) for the comprehensive assessment of gliomas [tumour grade, isocitrate dehydrogenase-1 (IDH-1) mutation status and tumour proliferation rate (Ki-67)]. This study describes the efficacy of DKI and DTI to comprehensively evaluate gliomas, compares their results. METHODS Fifty-two patients (18 females; median age, 47.5 years) with pathologically proved gliomas were prospectively included. All cases underwent DKI examination. DKI (mean kurtosis: MK, axial kurtosis: Ka, radial kurtosis: Kr) and DTI (mean diffusivity: MD, fractional anisotropy: FA) maps of each metric was derived. Three ROIs were manually drawn. RESULTS MK, Ka, Kr and FA were significantly higher in HGGs than in LGGs, whereas MD was significantly lower in HGGs than in LGGs (P < 0.01). ROC analysis demonstrated that MK (specificity: 100% sensitivity: 79%) and Ka (specificity: 96% sensitivity: 82%) had the same and highest (AUC: 0.93) diagnostic value. Moreover, MK, Ka, and Kr were significantly higher in grade III than II gliomas (P ≦ 0.01). Further, DKI and DTI can significantly identify IDH-1 mutation status (P ≦ 0.03). Ka (sensitivity: 74%, specificity: 75%, AUC: 0.72) showed the highest diagnostic value. In addition, DKI metrics and MD showed significant correlations with Ki-67 (P ≦ 0.01) and Ka had the highest correlation coefficient (rs = 0.72). CONCLUSIONS Compared with DTI, DKI has great advantages for the comprehensive assessment of gliomas. Ka might serve as a promising imaging index in predicting glioma grading, tumour cell proliferation rate and IDH-1 gene mutation status.
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Suh CH, Kim HS, Jung SC, Choi CG, Kim SJ. Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis. Eur Radiol 2018; 29:745-758. [PMID: 30003316 DOI: 10.1007/s00330-018-5608-7] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 05/12/2018] [Accepted: 06/14/2018] [Indexed: 11/24/2022]
Abstract
OBJECTIVES To evaluate the imaging features of isocitrate dehydrogenase (IDH) mutant glioma and to assess the diagnostic performance of magnetic resonance imaging (MRI) for prediction of IDH mutation in patients with glioma. METHODS A systematic search of Ovid-MEDLINE and EMBASE up to 10 October 2017 was conducted to find relevant studies. The search terms combined synonyms for 'glioma', 'IDH mutation' and 'MRI'. Studies evaluating the imaging features of IDH mutant glioma and the diagnostic performance of MRI for prediction of IDH mutation in patients with glioma were selected. The pooled summary estimates of sensitivity and specificity and their 95% confidence intervals (CIs) were calculated using a bivariate random-effects model. The results of multiple subgroup analyses are reported. RESULTS Twenty-eight original articles in a total of 2,146 patients with glioma were included. IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high apparent diffusion coefficient (ADC) value and low relative cerebral blood volume (rCBV) value. For the meta-analysis that included 18 original articles, the summary sensitivity was 86% (95% CI, 79%-91%) and the summary specificity was 87% (95% CI, 78-92%). In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate magnetic resonance spectroscopy (MRS) [96% (95% CI, 91-100%)] was higher than the summary sensitivities of other imaging modalities. CONCLUSIONS IDH mutant glioma consistently demonstrated less aggressive imaging features than IDH wild-type glioma. Despite the variety of different MRI techniques used, MRI showed the potential to non-invasively predict IDH mutation in patients with glioma. 2-Hydroxyglutarate MRS shows higher pooled sensitivity than other imaging modalities. KEY POINTS • IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high ADC value, and low rCBV value. • The diagnostic performance of MRI for prediction of IDH mutation in patients with glioma is within a clinically acceptable range, the summary sensitivity was 86% (95% CI, 79-91%) and the summary specificity was 87% (95% CI, 78-92%). • In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate MRS [96% (95% CI, 91-100%)] was higher than the summary sensitivities of other imaging modalities.
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Affiliation(s)
- Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 138-736, Republic of Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 138-736, Republic of Korea.
| | - Seung Chai Jung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 138-736, Republic of Korea
| | - Choong Gon Choi
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 138-736, Republic of Korea
| | - Sang Joon Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 138-736, Republic of Korea
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Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study. Sci Rep 2018; 8:6108. [PMID: 29666413 PMCID: PMC5904150 DOI: 10.1038/s41598-018-24438-4] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 03/07/2018] [Indexed: 12/27/2022] Open
Abstract
We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors. This resulted in 81.1% accuracy (sensitivity = 0.96, specificity = 0.45, AUC 0.59) for IDH mutation diagnosis. There were non-significant differences in average MDK and skewness among the different tumour grades. When texture analysis and SVM were utilized, the grading accuracy achieved by DKI biomarkers was 78.1% (sensitivity 0.77, specificity 0.79, AUC 0.79); the prediction accuracy for IDH mutation reached 83.8% (sensitivity 0.96, specificity 0.55, AUC 0.87). For the IDH mutation task, DKI outperformed significantly the FLAIR imaging. When using selected biomarkers after RFE, the prediction accuracy achieved 83.8% (sensitivity 0.92, specificity 0.64, AUC 0.88). These findings demonstrate the superiority of DKI enhanced by texture analysis and SVM, compared to conventional imaging, for gliomas staging and prediction of IDH mutational status.
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Zhao J, Li JB, Wang JY, Wang YL, Liu DW, Li XB, Song YK, Tian YS, Yan X, Li ZH, He SF, Huang XL, Jiang L, Yang ZY, Chu JP. Quantitative analysis of neurite orientation dispersion and density imaging in grading gliomas and detecting IDH-1 gene mutation status. NEUROIMAGE-CLINICAL 2018; 19:174-181. [PMID: 30023167 PMCID: PMC6050458 DOI: 10.1016/j.nicl.2018.04.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 03/26/2018] [Accepted: 04/09/2018] [Indexed: 12/17/2022]
Abstract
Background and purpose Neurite orientation dispersion and density imaging (NODDI) is a new diffusion MRI technique that has rarely been applied for glioma grading. The purpose of this study was to quantitatively evaluate the diagnostic efficiency of NODDI in tumour parenchyma (TP) and peritumoural area (PT) for grading gliomas and detecting isocitrate dehydrogenase-1 (IDH-1) mutation status. Methods Forty-two patients (male: 23, female: 19, mean age: 44.5 y) were recruited and underwent whole brain NODDI examination. Intracellular volume fraction (icvf) and orientation dispersion index (ODI) maps were derived. Three ROIs were manually placed on TP and PT regions for each case. The corresponding average values of icvf and ODI were calculated, and their diagnostic efficiency was assessed. Results Tumours with high icvfTP (≥0.306) and low icvfPT (≤0.331) were more likely to be high-grade gliomas (HGGs), while lesions with low icvfTP (<0.306) and high icvfPT (>0.331) were prone to be low-grade gliomas (LGGs) (P < 0.001). A multivariate logistic regression model including patient age and icvf values in TP and PT regions most accurately predicted glioma grade (AUC = 0.92, P < 0.001), with a sensitivity and specificity of 92% and 89%, respectively. However, no significant differences were found in NODDI metrics for differentiating IDH-1 mutation status. Conclusions The quantitative NODDI metrics in the TP and PT regions are highly valuable for glioma grading. A multivariate logistic regression model using the patient age and the icvf values in TP and PT regions showed very high predictive power. However, the utility of NODDI metrics for detecting IDH-1 mutation status has not been fully explored, as a larger sample size may be necessary to uncover benefits. Neurite orientation dispersion and density imaging (NODDI) is a new diffusion MRI technique Quantitative NOODI metrics in TP and PT area could help grading gliomas Age, icvf in TP and PT area were significantly associated with glioma grading The utility of NODDI in detecting IDH-1 mutation status has not been fully explored
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Affiliation(s)
- Jing Zhao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China
| | - Ji-Bin Li
- Department of Clinical Research, Sun Yat-sen University Cancer Center, 651, Dong Feng Dong Lu Road, Guangzhou, Guangdong 510060, China
| | - Jing-Yan Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China
| | - Yu-Liang Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China
| | - Da-Wei Liu
- Department of Pathology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China
| | - Xin-Bei Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China
| | - Yu-Kun Song
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China
| | - Yi-Su Tian
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China
| | - Xu Yan
- MR Collaboration NE Asia, Siemens Healthcare 278, Zhou Zhu Road, Nanhui, Shanghai 201318, China
| | - Zhu-Hao Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China
| | - Shao-Fu He
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China
| | - Xiao-Long Huang
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Ying Feng Lu Road, Hai Zhu district, Guangzhou, Guangdong 510235, China
| | - Li Jiang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China
| | - Zhi-Yun Yang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China
| | - Jian-Ping Chu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China.
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