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Abdelgawad MS, Kayed MH, Reda MIS, Abdelzaher E, Farhoud AH, Elsebaie N. Contribution of advanced neuro-imaging (MR diffusion, perfusion and proton spectroscopy) in differentiation between low grade gliomas GII and MR morphologically similar non neoplastic lesions. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00695-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Non-neoplastic brain lesions can be misdiagnosed as low-grade gliomas. Conventional magnetic resonance (MR) imaging may be non-specific. Additional imaging modalities such as spectroscopy (MRS), perfusion and diffusion imaging aid in diagnosis of such lesions. However, contradictory and overlapping results are still present. Hence, our purpose was to evaluate the role of advanced neuro-imaging in differentiation between low-grade gliomas (WHO grade II) and MR morphologically similar non-neoplastic lesions and to prove which modality has the most accurate results in differentiation.
Results
All patients were classified into two main groups: patients with low-grade glioma (n = 12; mean age, 38.8 ± 16; 8 males) and patients with non-neoplastic lesions (n = 27; mean age, 36.6 ± 15; 19 males) based on the histopathological and clinical–radiological diagnosis. Using ROC curve analysis, a threshold value of 0.93 for rCBV (AUC = 0.875, PPV = 92%, NPV = 71.4%) and a threshold value of 2.5 for Cho/NAA (AUC = 0.829, PPV = 92%, NPV = 71.4%) had 85.2% sensitivity and 83.3% specificity for predicting neoplastic lesions. The area under the curve (AUC) of ROC analysis was good for relative cerebral blood volume (rCBV) and Cho/NAA ratios (> 0.80) and fair for Cho/Cr and NAA/Cr ratios (0.70–0.80). When the rCBV measurements were combined with MRS ratios, significant improvement was observed in the area under the curve (AUC) (0.969) with improved diagnostic accuracy (89.7%) and sensitivity (88.9%).
Conclusions
Evaluation of rCBV and metabolite ratios at MRS, particularly Cho/NAA ratio, may be helpful in differentiating low-grade gliomas from non-neoplastic lesions. The combination of dynamic susceptibility contrast (DSC) perfusion and MRS can significantly improve the diagnostic accuracy and can help avoiding the need for an invasive biopsy.
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Wang Q, Pérez-Carrillo GJG, Ponisio MR, LaMontagne P, Dahiya S, Marcus DS, Milchenko M, Shimony J, Liu J, Chen G, Salter A, Massoumzadeh P, Miller-Thomas MM, Rich KM, McConathy J, Benzinger TLS, Wang Y. Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation. PLoS One 2019; 14:e0225093. [PMID: 31725772 PMCID: PMC6855653 DOI: 10.1371/journal.pone.0225093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 10/29/2019] [Indexed: 12/05/2022] Open
Abstract
Objectives Primary brain tumors are composed of tumor cells, neural/glial tissues, edema, and vasculature tissue. Conventional MRI has a limited ability to evaluate heterogeneous tumor pathologies. We developed a novel diffusion MRI-based method—Heterogeneity Diffusion Imaging (HDI)—to simultaneously detect and characterize multiple tumor pathologies and capillary blood perfusion using a single diffusion MRI scan. Methods Seven adult patients with primary brain tumors underwent standard-of-care MRI protocols and HDI protocol before planned surgical resection and/or stereotactic biopsy. Twelve tumor sampling sites were identified using a neuronavigational system and recorded for imaging data quantification. Metrics from both protocols were compared between World Health Organization (WHO) II and III tumor groups. Cerebral blood volume (CBV) derived from dynamic susceptibility contrast (DSC) perfusion imaging was also compared with the HDI-derived perfusion fraction. Results The conventional apparent diffusion coefficient did not identify differences between WHO II and III tumor groups. HDI-derived slow hindered diffusion fraction was significantly elevated in the WHO III group as compared with the WHO II group. There was a non-significantly increasing trend of HDI-derived tumor cellularity fraction in the WHO III group, and both HDI-derived perfusion fraction and DSC-derived CBV were found to be significantly higher in the WHO III group. Both HDI-derived perfusion fraction and slow hindered diffusion fraction strongly correlated with DSC-derived CBV. Neither HDI-derived cellularity fraction nor HDI-derived fast hindered diffusion fraction correlated with DSC-derived CBV. Conclusions Conventional apparent diffusion coefficient, which measures averaged pathology properties of brain tumors, has compromised accuracy and specificity. HDI holds great promise to accurately separate and quantify the tumor cell fraction, the tumor cell packing density, edema, and capillary blood perfusion, thereby leading to an improved microenvironment characterization of primary brain tumors. Larger studies will further establish HDI’s clinical value and use for facilitating biopsy planning, treatment evaluation, and noninvasive tumor grading.
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Affiliation(s)
- Qing Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | | | - Maria Rosana Ponisio
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Sonika Dahiya
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Daniel S. Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Mikhail Milchenko
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Joshua Shimony
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Jingxia Liu
- Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Gengsheng Chen
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Amber Salter
- Department of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Parinaz Massoumzadeh
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Michelle M. Miller-Thomas
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Keith M. Rich
- Department of Neurosurgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Jonathan McConathy
- Department of Radiology, Division of Molecular Imaging and Therapeutics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Tammie L. S. Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Yong Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
- Department of Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, Missouri, United States of America
- * E-mail:
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