Evaluation of Diagnostic Accuracy of the Approved Tumor Mapping Protocol in Grading of Glial Tumors
AbstractObjective: Evaluation of Diagnostic Accuracy of the Approved Tumor Mapping Protocol in Grading of Glial Tumors.
Methods: This descriptive cross-sectional study was performed on patients aged 2 to 82 years with glial tumor. Patients were referred to the hospital for tumor mapping and underwent imaging with simultaneous methods of MRS and magnetic resonance (MR) perfusion and conventional MRI under the supervision of NIAG group. Then, the results of the second evaluation, including the ratios of the desired metabolites and the amount of blood flow, permeability of the target area were compared with the results of pathology. The results were analyzed by SPSS software version 24.
Results: In this study, 30 patients were included. Sensitivity, specificity, positive and negative predictive value for the determination of high-grade glioma with peripheral/internal rCBV were 100/100%, 100/93%, 93/100% and 100.100%, respectively. Sensitivity, specificity, positive and negative predictive value for the diagnosis of glioma by using peripheral/internal rCBV and thresholds of 2.65 and 1.06 were 100/100%, 93/100%, 93/100% and 100/100%, respectively. Sensitivity, specificity, positive predictive value and negative predictive value were determined for diagnosis of high-grade glioma tumor using Ch + Cr / NAA Cho / Cr and Cho / NAA ratios with detection threshold of 2.97 (93.3%), 3.5 (78.9%,100%, 100%, and 73.3%), and 2.1 (100%). Threshold values of 3.5, 2.1 and 2.97 were obtained using Cho / Cr, Ch + Cr / NAA and Cho / NAA, respectively, for the detection of high-grade gliomas. The combination of rCBV, Cho / Cr, Ch + Cr / NAA and Cho / NAA had sensitivity, specificity, positive and negative predictive value of 67.7%, 80%, 77% and 70.5%, respectively. Significant differences in rCBV and Cho / Cr, Cho / NAA and NAA / Cr ratios were observed between low- and high-grade gliomas (P <0.0001).
Conclusion: Preoperative grading of glioma based on routine MR imaging is often unreliable. As a result, measuring rCBV and Cho / Cr and Cho / NAA ratios independently and somewhat together can significantly improve the sensitivity and predictive values of preoperative glioma grading.
2- Amirkhah R, Naderi-Meshkin H, Mirahmadi M, Allahyari A, Sharifi H. Cancer Statistics in Iran: Towards Finding Priority for Prevention and Treatment. Cancer Press. 2017; 3(2): 27-38.
3-Porter KR, McCarthy BJ, Freels S, Kim Y, Davis FG. Prevalence estimates for primary brain tumors in the United States by age, gender, behavior, and histology. Neuro Oncol. 2010;12(6):520-7.
4-Inskip PD, Hoover RN, Devesa SS. Brain cancer incidence trends in relation to cellular telephone use in the United States. Neuro Oncol. 2010;12(11):1147-51.
5- Kaminogo M, Ishimaru H, Morikawa M, Ochi M, Ushijima R, Tani M, et al. Diagnostic potential of short echo time MR spectroscopy of gliomas with single-voxel and point-resolved spatially localised proton spectroscopy of brain. Neuroradiology. 2001;43(5):353-63.
6-Knopp EA, Cha S, Johnson G, Mazumdar A, Golfinos JG, Zagzag D, et al. Glial neoplasms: dynamic contrast-enhanced T2*-weighted MR imaging. Radiology. 1999; 211(3): 791-8.
7-Moller-Hartmann W, Herminghaus S, Krings T, Marquardt G, Lanfermann H, Pilatus U, et al. Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions. Neuroradiology. 2002;44(5):371-81.
8-Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y, Hirai T, et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. Journal of magnetic resonance imaging : JMRI. 1999; 9(1): 53-60.
9- Hlaihel C, Guilloton L, Guyotat J, Streichenberger N, Honnorat J, Cotton F. Predictive value of multimodality MRI using conventional, perfusion, and spectroscopy MR in anaplastic transformation of low-grade oligodendrogliomas. Journal of neuro-oncology. 2010; 97(1): 73-80.
10- Law M, Yang S, Wang H, Babb JS, Johnson G, Cha S, et al. Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. American Journal of Neuroradiology. 2003; 24(10): 1989-98.
11- Roy B, Gupta RK, Maudsley AA, Awasthi R, Sheriff S, Gu M, et al. Utility of multiparametric 3-T MRI for glioma characterization. Neuroradiology. 2013; 55(5): 603-13.
12- Barajas RF, Jr., Cha S. Benefits of dynamic susceptibility-weighted contrast-enhanced perfusion MRI for glioma diagnosis and therapy. CNS oncology. 2014;3(6):407-19.
13- Ohgaki H, Kleihues P. Epidemiology and etiology of gliomas. Acta neuropathologica. 2005;109(1):93-108.
14- Lev MH, Ozsunar Y, Henson JW, Rasheed AA, Barest GD, Harsh GR, et al. Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol. 2004; 25(2): 214-21.
15- Cha S, Knopp EA, Johnson G, Wetzel SG, Litt AW, Zagzag D. Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. Radiology. 2002; 223(1):11-29.
16- Zidan S, Tantawy HI, Makia MA. High grade gliomas: The role of dynamic contrast-enhanced susceptibility-weighted perfusion MRI and proton MR spectroscopic imaging in differentiating grade III from grade IV. The Egyptian Journal of Radiology Nuclear Medicine. 2016;47(4):1565-73.
17- Vallee A, Guillevin C, Wager M, Delwail V, Guillevin R, Vallée J-N. Added value of spectroscopy to perfusion MRI in the differential diagnostic performance of common malignant brain tumors. American Journal of Neuroradiology. 2018; 39(8): 1423-31.
18- Shimizu H, Kumabe T, Shirane R, Yoshimoto T. Correlation between choline level measured by proton MR spectroscopy and Ki-67 labeling index in gliomas. AJNR Am J Neuroradiol. 2000;21(4):659-65.
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