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Batool SM, Escobedo AK, Hsia T, Ekanayake E, Khanna SK, Gamblin AS, Zheng H, Skog J, Miller JJ, Stemmer-Rachamimov AO, Cahill DP, Balaj L, Carter BS. Clinical utility of a blood based assay for the detection of IDH1.R132H-mutant gliomas. Nat Commun 2024; 15:7074. [PMID: 39152110 PMCID: PMC11329733 DOI: 10.1038/s41467-024-51332-7] [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: 02/09/2024] [Accepted: 08/05/2024] [Indexed: 08/19/2024] Open
Abstract
Glioma represents the most common central nervous system neoplasm in adults. Current classification scheme utilizes molecular alterations, particularly IDH1.R132H, to stratify lesions into distinct prognostic groups. Identification of the single nucleotide variant through traditional tissue biopsy assessment poses procedural risks and does not fully reflect the heterogeneous and evolving tumor landscape. Here, we introduce a liquid biopsy assay, mt-IDH1dx. The blood-based test allows minimally invasive detection of tumor-derived extracellular vesicle RNA using only 2 ml plasma volume. We perform rigorous, blinded validation testing across the study population (n = 133), comprising of IDH1.R132H patients (n = 80), IDH1 wild-type gliomas (n = 44), and age matched healthy controls (n = 9). Results from our plasma testing demonstrate an overall sensitivity of 75.0% (95% CI: 64.1%-84.0%), specificity 88.7% (95% CI: 77.0%-95.7%), positive predictive value 90.9%, and negative predictive value 70.1% compared to the tissue gold standard. In addition to fundamental diagnostic applications, the study also highlights the utility of mt-IDH1dx platform for blood-based monitoring and surveillance, offering valuable prognostic information. Finally, the optimized workflow enables rapid and efficient completion of both tumor tissue and plasma testing in under 4 hours from the time of sampling.
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Affiliation(s)
- Syeda Maheen Batool
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ana K Escobedo
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tiffaney Hsia
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Emil Ekanayake
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sirena K Khanna
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Austin S Gamblin
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hui Zheng
- Center for Biostatistics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Johan Skog
- Exosome Diagnostics, a Bio-Techne Brand, Waltham, MA, USA
| | - Julie J Miller
- Translational Neuro-Oncology Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Leonora Balaj
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Bob S Carter
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Ma H, Zeng S, Xie D, Zeng W, Huang Y, Mazu L, Zhu N, Yang Z, Chu J, Zhao J. Looking through the imaging perspective: the importance of imaging necrosis in glioma diagnosis and prognostic prediction - single centre experience. Radiol Oncol 2024; 58:23-32. [PMID: 38378035 PMCID: PMC10878771 DOI: 10.2478/raon-2024-0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/01/2023] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND The aim of the study was to investigate the diagnostic value of imaging necrosis (Imnecrosis) in grading, predict the genotype and prognosis of gliomas, and further assess tumor necrosis by dynamic contrast-enhanced MR perfusion imaging (DCE-MRI). PATIENTS AND METHODS We retrospectively included 150 patients (104 males, mean age: 46 years old) pathologically proved as adult diffuse gliomas and all diagnosis was based on the 2021 WHO central nervous system (CNS) classification. The pathological necrosis (Panecrosis) and gene mutation information were collected. All patients underwent conventional and DCE-MRI examinations and had been followed until May 31, 2021. The Imnecrosis was determined by two experienced neuroradiologists. DCE-MRI derived metric maps have been post-processed, and the mean value of each metric in the tumor parenchyma, peritumoral and contralateral area were recorded. RESULTS There was a strong degree of inter-observer agreement in defining Imnecrosis (Kappa = 0.668, p < 0.001) and a strong degree of agreement between Imnecrosis and Panecrosis (Kappa = 0.767, p < 0.001). Compared to low-grade gliomas, high-grade gliomas had more Imnecrosis (85.37%, p < 0.001), and Imnecrosis significantly increased with the grade of gliomas increasing. And Imnecrosis was significantly more identified in IDH-wildtype, 1p19q-non-codeletion, and CDKN2A/B-homozygous-deletion gliomas. Using multivariate Cox regression analysis, Imnecrosis was an independent and unfavorable prognosis factor (Hazard Ratio = 2.113, p = 0.046) in gliomas. Additionally, extravascular extracellular volume fraction (ve) in tumor parenchyma derived from DCE-MRI demonstrated the highest diagnostic efficiency in identifying Panecrosis and Imnecrosis with high specificity (83.3% and 91.9%, respectively). CONCLUSIONS Imnecrosis can provide supplementary evidence beyond Panecrosis in grading, predicting the genotype and prognosis of gliomas, and ve in tumor parenchyma can help to predict tumor necrosis with high specificity.
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Affiliation(s)
- Hui Ma
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Shanmei Zeng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Dingxiang Xie
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Wenting Zeng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Yingqian Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Liwei Mazu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Nengjin Zhu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Jianping Chu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
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Setyawan NH, Choridah L, Nugroho HA, Malueka RG, Dwianingsih EK. Beyond invasive biopsies: using VASARI MRI features to predict grade and molecular parameters in gliomas. Cancer Imaging 2024; 24:3. [PMID: 38167551 PMCID: PMC10759759 DOI: 10.1186/s40644-023-00638-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 11/22/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Gliomas present a significant economic burden and patient management challenge. The 2021 WHO classification incorporates molecular parameters, which guide treatment decisions. However, acquiring these molecular data involves invasive biopsies, prompting a need for non-invasive diagnostic methods. This study aims to assess the potential of Visually AcceSAble Rembrandt Images (VASARI) MRI features to predict glioma characteristics such as grade, IDH mutation, and MGMT methylation status. METHODS This study enrolled 107 glioma patients treated between 2017 and 2022, meeting specific criteria including the absence of prior chemotherapy/radiation therapy, and the presence of molecular and MRI data. Images were assessed using the 27 VASARI MRI features by two blinded radiologists. Pathological and molecular assessments were conducted according to WHO 2021 CNS Tumor classification. Cross-validation Least Absolute Shrinkage and Selection Operator (CV-LASSO) logistic regression was applied for statistical analysis to identify significant VASARI features in determining glioma grade, IDH mutation, and MGMT methylation status. RESULTS The study demonstrated substantial observer agreement in VASARI feature evaluation (inter- and intra-observer κ = 0.714 - 0.831 and 0.910, respectively). Patient imaging characteristics varied significantly with glioma grade, IDH mutation, and MGMT methylation. A predictive model was established using VASARI features for glioma grade prediction, exhibiting an AUC of 0.995 (95% CI = 0.986 - 0.998), 100% sensitivity, and 92.86% specificity. IDH mutation status was predicted with AUC 0.930 (95% CI = 0.882 - 0.977), and improved slightly to 0.933 with 'age-at-diagnosis' added. A model predicting MGMT methylation had a satisfactory performance (AUC 0.757, 95% CI = 0.645 - 0.868), improving to 0.791 when 'age-at-diagnosis' was added. CONCLUSIONS The T1/FLAIR ratio, enhancement quality, hemorrhage, and proportion enhancing predict glioma grade with excellent accuracy. The proportion enhancing, thickness of enhancing margin, and T1/FLAIR ratio are significant predictors for IDH mutation status. Lastly, MGMT methylation is related to the longest diameter of the lesion, edema crossing the midline, and the proportion of the non-enhancing lesion. VASARI MRI features offer non-invasive and accurate predictive models for glioma grade, IDH mutation, and MGMT methylation status, enhancing glioma patient management.
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Affiliation(s)
- Nurhuda Hendra Setyawan
- Department of Radiology, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Jl. Farmako, Kabupaten Sleman, Daerah Istimewa Yogyakarta, 55281, Indonesia.
| | - Lina Choridah
- Department of Radiology, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Jl. Farmako, Kabupaten Sleman, Daerah Istimewa Yogyakarta, 55281, Indonesia
| | - Hanung Adi Nugroho
- Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No.2, Kabupaten Sleman, Daerah Istimewa Yogyakarta, 55281, Indonesia
| | - Rusdy Ghazali Malueka
- Department of Neurology, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Jl. Farmako, Kabupaten Sleman, Daerah Istimewa Yogyakarta, 55281, Indonesia
| | - Ery Kus Dwianingsih
- Department of Anatomical Pathology, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Jl. Farmako, Kabupaten Sleman, Daerah Istimewa Yogyakarta, 55281, Indonesia
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Rathore S, Iftikhar MA, Chaddad A, Singh A, Gillani Z, Abdulkadir A. Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107812. [PMID: 37757566 DOI: 10.1016/j.cmpb.2023.107812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 05/14/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI), digital pathology imaging (PATH), demographics, and IDH mutation status predict overall survival (OS) in glioma. Identifying and characterizing predictive features in the different modalities may improve OS prediction accuracy. PURPOSE To evaluate the OS prediction accuracy of combinations of prognostic markers in glioma patients. MATERIALS AND METHODS Multi-contrast MRI, comprising T1-weighted, T1-weighted post-contrast, T2-weighted, T2 fluid-attenuated-inversion-recovery, and pathology images from glioma patients (n = 160) were retrospectively collected (1983-2008) from TCGA alongside age and sex. Phenotypic profiling of tumors was performed by quantifying the radiographic and histopathologic descriptors extracted from the delineated region-of-interest in MRI and PATH images. A Cox proportional hazard model was trained with the MRI and PATH features, IDH mutation status, and basic demographic variables (age and sex) to predict OS. The performance was evaluated in a split-train-test configuration using the concordance-index, computed between the predicted risk score and observed OS. RESULTS The average age of patients was 51.2years (women: n = 77, age-range=18-84years; men: n = 83, age-range=21-80years). The median OS of the participants was 494.5 (range,3-4752), 481 (range,7-4752), and 524.5 days (range,3-2869), respectively, in complete dataset, training, and test datasets. The addition of MRI or PATH features improved prediction of OS when compared to models based on age, sex, and mutation status alone or their combination (p < 0.001). The full multi-omics model integrated MRI, PATH, clinical, and genetic profiles and predicted the OS best (c-index= 0.87). CONCLUSION The combination of imaging, genetic, and clinical profiles leads to a more accurate prognosis than the clinical and/or mutation status.
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Affiliation(s)
- Saima Rathore
- AVID Radiopharmaceuticals, Philadelphia, PA, USA; Eli Lilly and Company, Indianapolis, IN, USA.
| | | | - Ahmad Chaddad
- School of Artificial Intelligence, GUET, Guilin, China
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Zeeshan Gillani
- Comsats University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Ahmed Abdulkadir
- Center for Research in Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Center for Artificial Intelligence, Zurich University of Applied Sciences, Winterthur, ZH, Switzerland
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Priambada D, Thohar Arifin M, Saputro A, Muzakka A, Karlowee V, Sadhana U, Bakhtiar Y, Prihastomo KT, Risdianto A, Brotoarianto HK, Andar E, Muttaqin Z. Immunohistochemical Expression of IDH1, ATRX, Ki67, GFAP, and Prognosis in Indonesian Glioma Patients. Int J Gen Med 2023; 16:393-403. [PMID: 36756391 PMCID: PMC9900491 DOI: 10.2147/ijgm.s397550] [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: 12/01/2022] [Accepted: 01/07/2023] [Indexed: 02/04/2023] Open
Abstract
Background The current World Health Organization (WHO) 2021 classification of human glioma is based on key molecular biomarkers to define neoplastic entities. This review further delineates mutant IDH (isocitrate dehydrogenase) from wild-type IDH disease, a necessity given the large survival gap between mutant IDH and wild-type IDH tumors. In Indonesia, there are currently few reports on the distribution and significance of these mutations. Therefore, this research aims to determine the relationship between IDH mutations, as well as clinicopathological and prognostic factors in patients with gliomas. Other immunohistochemical markers including ATRX (alpha-thalassemia/mental retardation, X-linked), Ki67 and GFAP (glial fibrillary acidic protein) expression were also evaluated. Methods Forty-two glioma samples were collected from patients who underwent surgery at Dr. Kariadi General Hospital in Semarang, Central Java, Indonesia. Fresh and paraffin-embedded, formalin-fixed tissue samples were removed and sectioned for hematoxylin and eosin staining, immunohistochemistry, and IDH analysis of mutation. Medical records were used to collect clinicopathological and survival data. Results IDH1 mutations were discovered in 32 (76,1%) patients, and those with IDH1 mutation had longer overall survival when corresponded to patients with IDH1-wild-type. Lower expression of Ki67 was discovered to be very associated with a better prognosis. Conclusion IDH1 mutations status showed a significant relationship with prognosis in patients with glioma. Meanwhile, other markers (ATRX, Ki67, and GFAP) did not correlate with the prognosis.
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Affiliation(s)
- Dody Priambada
- Department of Neurosurgery, Faculty of Medicine, Diponegoro University/Dr. Kariadi Hospital, Semarang, Indonesia
| | - Muhamad Thohar Arifin
- Department of Neurosurgery, Faculty of Medicine, Diponegoro University/Dr. Kariadi Hospital, Semarang, Indonesia,Correspondence: Muhamad Thohar Arifin, Department of Neurosurgery, Faculty of Medicine, Diponegoro University/Dr. Kariadi Hospital, Dr. Soetomo Street Number 16-18, Semarang, Indonesia, Tel +62 813 2586 1628, Email
| | - Abdi Saputro
- Department of Neurosurgery, Faculty of Medicine, Diponegoro University/Dr. Kariadi Hospital, Semarang, Indonesia
| | - Azka Muzakka
- Department of Neurosurgery, Faculty of Medicine, Diponegoro University/Dr. Kariadi Hospital, Semarang, Indonesia
| | - Vega Karlowee
- Department of Clinical Pathology, Faculty of Medicine, Diponegoro University, Dr. Kariadi Hospital, Semarang, Indonesia
| | - Udadi Sadhana
- Department of Clinical Pathology, Faculty of Medicine, Diponegoro University, Dr. Kariadi Hospital, Semarang, Indonesia
| | - Yuriz Bakhtiar
- Department of Neurosurgery, Faculty of Medicine, Diponegoro University/Dr. Kariadi Hospital, Semarang, Indonesia
| | - Krisna Tsaniadi Prihastomo
- Department of Neurosurgery, Faculty of Medicine, Diponegoro University/Dr. Kariadi Hospital, Semarang, Indonesia
| | - Ajid Risdianto
- Department of Neurosurgery, Faculty of Medicine, Diponegoro University/Dr. Kariadi Hospital, Semarang, Indonesia
| | - Happy Kurnia Brotoarianto
- Department of Neurosurgery, Faculty of Medicine, Diponegoro University/Dr. Kariadi Hospital, Semarang, Indonesia
| | - Erie Andar
- Department of Neurosurgery, Faculty of Medicine, Diponegoro University/Dr. Kariadi Hospital, Semarang, Indonesia
| | - Zainal Muttaqin
- Department of Neurosurgery, Faculty of Medicine, Diponegoro University/Dr. Kariadi Hospital, Semarang, Indonesia
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Kumar M, Nanga RPR, Verma G, Wilson N, Brisset JC, Nath K, Chawla S. Emerging MR Imaging and Spectroscopic Methods to Study Brain Tumor Metabolism. Front Neurol 2022; 13:789355. [PMID: 35370872 PMCID: PMC8967433 DOI: 10.3389/fneur.2022.789355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Proton magnetic resonance spectroscopy (1H-MRS) provides a non-invasive biochemical profile of brain tumors. The conventional 1H-MRS methods present a few challenges mainly related to limited spatial coverage and low spatial and spectral resolutions. In the recent past, the advent and development of more sophisticated metabolic imaging and spectroscopic sequences have revolutionized the field of neuro-oncologic metabolomics. In this review article, we will briefly describe the scientific premises of three-dimensional echoplanar spectroscopic imaging (3D-EPSI), two-dimensional correlation spectroscopy (2D-COSY), and chemical exchange saturation technique (CEST) MRI techniques. Several published studies have shown how these emerging techniques can significantly impact the management of patients with glioma by determining histologic grades, molecular profiles, planning treatment strategies, and assessing the therapeutic responses. The purpose of this review article is to summarize the potential clinical applications of these techniques in studying brain tumor metabolism.
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Affiliation(s)
- Manoj Kumar
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Ravi Prakash Reddy Nanga
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
| | - Gaurav Verma
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Neil Wilson
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
| | | | - Kavindra Nath
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
- *Correspondence: Sanjeev Chawla
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Tang PLY, Méndez Romero A, Jaspers JPM, Warnert EAH. The potential of advanced MR techniques for precision radiotherapy of glioblastoma. MAGMA (NEW YORK, N.Y.) 2022; 35:127-143. [PMID: 35129718 PMCID: PMC8901515 DOI: 10.1007/s10334-021-00997-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 06/14/2023]
Abstract
As microscopic tumour infiltration of glioblastomas is not visible on conventional magnetic resonance (MR) imaging, an isotropic expansion of 1-2 cm around the visible tumour is applied to define the clinical target volume for radiotherapy. An opportunity to visualize microscopic infiltration arises with advanced MR imaging. In this review, various advanced MR biomarkers are explored that could improve target volume delineation for radiotherapy of glioblastomas. Various physiological processes in glioblastomas can be visualized with different advanced MR techniques. Combining maps of oxygen metabolism (CMRO2), relative cerebral blood volume (rCBV), vessel size imaging (VSI), and apparent diffusion coefficient (ADC) or amide proton transfer (APT) can provide early information on tumour infiltration and high-risk regions of future recurrence. Oxygen consumption is increased 6 months prior to tumour progression being visible on conventional MR imaging. However, presence of the Warburg effect, marking a switch from an infiltrative to a proliferative phenotype, could result in CMRO2 to appear unaltered in high-risk regions. Including information on biomarkers representing angiogenesis (rCBV and VSI) and hypercellularity (ADC) or protein concentration (APT) can omit misinterpretation due to the Warburg effect. Future research should evaluate these biomarkers in radiotherapy planning to explore the potential of advanced MR techniques to personalize target volume delineation with the aim to improve local tumour control and/or reduce radiation-induced toxicity.
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Affiliation(s)
- Patrick L Y Tang
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands.
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
| | - Alejandra Méndez Romero
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Jaap P M Jaspers
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Esther A H Warnert
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
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Xie Y, Li S, Shen N, Gan T, Zhang S, Liu WV, Zhu W. Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging. Front Neurosci 2021; 15:783361. [PMID: 34880724 PMCID: PMC8645648 DOI: 10.3389/fnins.2021.783361] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives: To compare the efficacy of parameters from multiple diffusion magnetic resonance imaging (dMRI) for prediction of isocitrate dehydrogenase 1 (IDH1) genotype and assessment of cell proliferation in gliomas. Methods: Ninety-one patients with glioma underwent diffusion weighted imaging (DWI), multi-b-value DWI, and diffusion kurtosis imaging (DKI)/neurite orientation dispersion and density imaging (NODDI) on 3.0T MRI. Each parameter was compared between IDH1-mutant and IDH1 wild-type groups by Mann-Whitney U test in lower-grade gliomas (LrGGs) and glioblastomas (GBMs), respectively. Further, performance of each parameter was compared for glioma grading under the same IDH1 genotype. Spearman correlation coefficient between Ki-67 labeling index (LI) and each parameter was calculated. Results: The diagnostic performance was better achieved with apparent diffusion coefficient (ADC), slow ADC (D), fast ADC (D∗), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), mean diffusivity (MD), mean kurtosis (MK), and intracellular volume fraction (ICVF) for distinguishing IDH1 genotypes in LrGGs, with statistically insignificant AUC values from 0.750 to 0.817. In GBMs, no difference between the two groups was found. For IDH1-mutant group, all parameters, except for fractional anisotropy (FA) and D∗, significantly discriminated LrGGs from GBMs (P < 0.05). However, for IDH1 wild-type group, only ADC statistically discriminated the two (P = 0.048). In addition, MK has maximal correlation coefficient (r = 0.567, P < 0.001) with Ki-67 LI. Conclusion: dMRI-derived parameters are promising biomarkers for predicting IDH1 genotype in LrGGs, and MK has shown great potential in assessing glioma cell proliferation.
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Affiliation(s)
- Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shihui Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Nanxi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tongjia Gan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weiyin Vivian Liu
- Magnetic Resonance Research, General Electric Healthcare, Beijing, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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