DIFFUSION-WEIGHTED MAGNETIC RESONANCE IMAGING IN PEDIATRIC NEURO-ONCOLOGY: ADVANCEMENTS AND APPLICATIONS

Main Article Content

Bikram Jeet Singh
R. P. Bansal
Feroz Shaheen

Keywords

Pediatric brain tumors, Diffusion-weighted imaging, Apparent diffusion coefficient, Tumor grading

Abstract

Background: Pediatric brain tumors constitute a significant cause of morbidity and mortality, requiring accurate diagnosis and grading for optimal treatment planning. Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) mapping have emerged as valuable tools in neuro-oncology, providing insights into tumor cellularity and aggressiveness.


Objective: This study aimed to evaluate the DWI characteristics of pediatric brain tumors and their correlation with tumor grade and classification.


Methods: A retrospective analysis of 66 pediatric patients who underwent preoperative DWI was conducted. Tumors were classified as high-grade (WHO grade ≥ III) or low-grade (WHO grade ≤ II) based on histopathology. DWI signal characteristics and ADC values were analyzed to assess their diagnostic accuracy in differentiating tumor grades. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Statistical analysis was performed using SPSS (Version 20), with a significance threshold of p < 0.05.


Results: Among 66 tumors, 18 (27.3%) were high-grade, and 48 (72.7%) were low-grade. Diffusion restriction was observed in 77.8% of high-grade tumors but in none of the low-grade tumors. DWI demonstrated a sensitivity of 77.8%, specificity of 100%, PPV of 100%, and NPV of 92.3% for identifying high-grade tumors.


Conclusion: DWI and ADC mapping provide valuable non-invasive biomarkers for differentiating pediatric brain tumor grades. Integrating DWI into routine imaging protocols may enhance diagnostic accuracy and guide treatment strategies in pediatric neuro-oncology.


 


 

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References

1. Panigrahy A, Blüml S. Neuroimaging of pediatric brain tumors: from basic to advanced magnetic resonance imaging (MRI). J Child Neurol. 2009;24(11):1343–65.
2. Kelly PJ, Daumas-Duport C, Kispert DB, Kall BA, Scheithauer BW, Illig JJ. Imaging-based stereotaxic serial biopsies in un treated intracranial glial neoplasms. J Neurosurg 1987; 66: 865–74.
3. Nardone V, Tini P, Biondi M, Sebaste L, Vanzi E, De Otto G et al. Prognostic Value of MR Imaging Texture Analysis in Brain Non-Small Cell Lung Cancer Oligo-Metastases Undergoing Stereotactic Irradiation. Cureus. 2016;8(4):e584. doi: 10.7759/cureus.584.
4. Peet AC, Arvanitis TN, Leach MO, Waldman AD. Functional imaging in adult and paediatric brain tumours. Nat Rev Clin Oncol. 2012 Dec;9(12):700-11.
5. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988 Aug;168(2):497-505.
6. Koh DM, Collins DJ. Diffusion-weighted MRI in the body: applications and challenges in oncology. Am J Roentgenol 2007;188:1622-35.
7. Gurney JG, Kadan-Lottick N. Brain and other central nervous system tumors: rates, trends, and epidemiology. Curr Opin Oncol. 2001;13(3):160–6.
8. Peet AC, Arvanitis TN, Leach MO, Waldman AD. Functional imaging in adult and paediatric brain tumours. Nat Rev Clin Oncol. 2012;9(12):700.
9. Stadnik TW, Demaerel P, Luypaert RR, Chaskis C, Van Rompaey KL, Michotte A, et al. Imaging tutorial: differential diagnosis of bright lesions on diffusion weighted MR images. Radiographics. 2003;23(1):e7–e.
10. Saunders DE, Thompson C, Gunny R, Jones R, Cox T, Chong WK. Magnetic resonance imaging protocols for paediatric neuroradiology. Pediatr Radiol. 2007 Aug; 37(8): 789-97.
11. Borja MJ, Plaza MJ, Altman N, Saigal G. Conventional and advanced MRI features of pediatric intracranial tumors: supratentorial tumors. Am J Roentgenol. 2013 May; 200(5): W483-503.
12. Walid MS and Troup EC. Cerebellar anaplastic astrocytoma in teenager with Ollier disease. Journal of Nuero-Oncology, 2009; 89: 59-62.
13. Byun WM, Shin SO, Chang Y, Lee SJ, Finsterbusch J, Frahm J. Diffusion-weighted MR imaging of metastatic disease of the spine: assessment of response to therapy. Am J Neuroradiol. 2002 Jun-Jul;23(6):906-12.
14. Chen CY, Li CW, Kuo YT, Jaw TS, Wu DK, Jao JC, Hsu JS, Liu GC. Early response of hepatocellular carcinoma to transcatheter arterial chemoembolization: choline levels and MR diffusion constants--initial experience. Radiology. 2006 May;239(2):448-56.
15. Provenzale JM, Mukundan S, Barboriak DP. Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. Radiology. 2006 Jun;239(3):632-49.
16. Humphries PD, Sebire NJ, Siegel MJ, Olsen ØE. Tumors in pediatric patients at diffusion-weighted MR imaging: apparent diffusion coefficient and tumor cellularity. Radiology. 2007 Dec; 245(3): 848-54.
17. Klisch J, Husstedt H, Hennings S, vonVelthovenV, Pagenstecher A, Schumacher M. Supratentorial primitive neuroectodermal tumours: diffusion-weighted MRI. Neuroradiology 2000; 42: 393–98.
18. Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, Wakasa K, Yamada R. The role of diffusion-weighted imaging in patients with brain tumors. Am J Neuroradiol 2001; 22: 1081–88.
19. Bulakbasi N, Guvenc I, Onguru O, Erdogan E, Tayfun C, Ucoz T. The added value of the apparent diffusion coefficient calculation to magnetic resonance imaging in the differentiation and grading of malignant brain tumors. J Comput Assist Tomogr 2004; 28: 735–46.
20. Yamasaki F, Kurisu K, Satoh K, Arita K, Sugiyama K, Ohtaki M et al. Apparent diffusion coefficient of human brain tumors at MR imaging. Radiology 2005; 2(35): 985–91.
21. Fruehwald-Pallamar J, Puchner SB, Rossi A, Garre ML, Cama A, Koelblinger C et al. Magnetic resonance imaging spectrum of medulloblastoma. Neuroradiology. 2011 Jun; 53(6):387-96.
22. Prince MR and Chew FS. Ependymoma of the fourth ventricle. American Journal of Roentgenology, 1991; 157: 1278-82.
23. Koeller KK and Rushing EJ. From the archives of the AFIP: pilocytic astrocytoma: radiologic-pathologic correlation. Radiographics. 2004 Nov-Dec;24(6):1693-708.
24. Docampo JR, González N, SPIVACOW M, Juarez R, Bruno C, Morales C. Pilocytic Astrocytoma. Typical and Atypical CT and MRI Imaging Findings. European Congress of Radiology-ECR 2012; 569: 1-37.
25. Lam S, Reddy GD, Lin Y, Jea A. Management of hydrocephalus in children with posterior fossa tumors. Surg Neurol Int. 2015 Jul 23;6(Suppl 11):S346-8.
26. Rumboldt Z, Camacho DL, Lake D, Welsh CT, Castillo M. Apparent diffusion coefficients for differentiation of cerebellar tumors in children. AJNR Am J Neuroradiol. 2006 Jun-Jul;27(6):1362-9.
27. Lobel U, Sedlacik J, Reddick WE, Kocak M, Ji Q, Broniscer A et al. Quantitative diffusion-weighted and dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging analysis of T2 hypointense lesion components in pediatric diffuse intrinsic pontine glioma. Am J Neuroradiol. 2011 Feb;32(2):315-22.
28. Beaman FD, Kransdorf MJ, Menke DM. Schwannoma: radiologic-pathologic correlation. Radiographics. 2004 Sep-Oct;24(5):1477-81.
29. Ahmed MAS, Dawoud MA, El-Saed HH and El-Gamal EE. Role of Magnetic Resonance Imaging (MRI) in the diagnosis of pediatric posterior fossa tumors. Med. J. Cairo Univ., 2018; 86(1): 325-31.