Journal of Isfahan Medical School

Journal of Isfahan Medical School

The Apparent Diffusion Coefficient Changes after Radiation Therapy in Patients with Astrocytoma Cancer and Its Relationship with Tumor Volume-Dose

Document Type : Original Article (s)

Authors
Alireza Nasiri 1
Mohammad Reza Salamat 2
1 MSc Student of Medical Physics, Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
2 Associate Professor, Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
10.48305/jims.v40.i681.0571
Abstract
Background: Among the effective and non-invasive factors in predicting and evaluating the treatment outcome of cancerous tumors including cerebral astrocytoma is the apparent diffusion coefficient (ADC) resulting from diffusion weight imaging (DW-MRI). The aim of this study was to investigate the correlation of dose per unit volume of treatment target with ADC changes after radiation therapy.
Methods: In this prospective, cross -sectional study, 40 patients with astrocytoma tumor were investigated. From DW-MRI images, each patient performed once before surgery and again 30 to 45 days after radiation therapy to investigate the relationship between. Statistical findings from data analysis showed that the amount of apparent diffusion coefficient after radiation therapy increased significantly and the amount of these changes was directly related to the dose reached per unit volume of treatment.
Findings: Performing statistical analysis on the resulting data showed that there is a significant increase in the amount of Apparent diffusion coefficient after radiation therapy, that the amount of these changes is directly related to the Planning target volume and the amount of emission changes in Apparent diffusion coefficient due to receiving radiation in the tumor area. It can be determined by radiation therapy.
Conclusion: The results of this research show the advantage of using diffusion-weighted magnetic resonance images in the treatment design of patients with astrocytoma tumors and selecting the optimal Planning target volume. Also, with the possibility of calculating the secondary apparent diffusion coefficient in the tumor area, which indicates the tumor response and the patient's survival rate, the treatment result can be predicted with appropriate accuracy.
Keywords
Apparent Diffusion Coefficient
Astrocytoma
Diffusion Magnetic Resonance Imaging
Radiotherapy
Tumor Burden
  1. McGuire S. World cancer report 2014. Geneva, Switzerland: World Health Organization, international agency for research on cancer, WHO Press, 2015. Adv Nutr 2016; 7(2): 418-9.
  2. Yuan C, Yao Q, Cheng L, Zhang C, Ma L, Guan J,
    et al. Prognostic factors and nomogram prediction of survival probability in primary spinal cord astrocytoma patients. J Neurosurg Spine 2021; 35(5): 651-62.
  3. Hashimoto S, Inaji M, Nariai T, Kobayashi D, Sanjo N, Yokota T, et al. Usefulness of [11C] methionine PET in the differentiation of tumefactive multiple sclerosis from high grade astrocytoma. Neurol Med Chir (Tokyo) 2019; 59(5): 176-83.
  4. Dutta R, Sharma MC, Suri V, Sarkar C, Garg A, Srivastava A, et al. Granular cell astrocytoma: a diagnostic conundrum. World Neurosurg 2020; 143: 209-13.
  5. Röhrich M, Loktev A, Wefers AK, Altmann A, Paech D, Adeberg S, et al. IDH-wildtype glioblastomas and grade III/IV IDH-mutant gliomas show elevated tracer uptake in fibroblast activation protein-specific PET/CT. Eur J Nucl Med Mol Imaging 2019; 46(12): 2569-80.
  6. Rajalingam B, Priya R, Bhavani R. Comparative analysis of hybrid fusion algorithms using neurocysticercosis, neoplastic, Alzheimer's, and astrocytoma disease affected multimodality medical images. In: Gandhi TK, Bhattacharyya S, De S, Konar D, Dey S, editord. Advanced machine vision paradigms for medical image analysis. London, UK: Elsevier; 2021. p. 131-67.
  7. Dong F, Li Q, Xu D, Xiu W, Zeng Q, Zhu X, et al. Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features. Eur Radiol 2019; 29(8): 3968-75.
  8. Ogunlade J, Wiginton 4th JG, Elia C, Odell T, Rao SC. Primary spinal astrocytomas: a literature review. Cureus 2019; 11(7): e5247.
  9. Ramaglia A, Tortora D, Mankad K, Lequin M, Severino M, D’Arco F, et al. Role of diffusion weighted imaging for differentiating cerebral pilocytic astrocytoma and ganglioglioma BRAF V600E-mutant from wild type. Neuroradiology 2020; 62(1): 71-80.
  10. Ho CY, Supakul N, Patel PU, Seit V, Groswald M, Cardinal J, et al. Differentiation of pilocytic and pilomyxoid astrocytomas using dynamic susceptibility contrast perfusion and diffusion weighted imaging. Neuroradiology 2020; 62(1): 81-8.
  11. White NS, McDonald C, Farid N, Kuperman J, Karow D, Schenker-Ahmed NM, et al. Erratum: Diffusion-weighted imaging in cancer: Physical foundations and applications of restriction spectrum imaging. Cancer Rese 2014; 74(17): 4638-52.
  12. Dury RJ, Lourdusamy A, Macarthur DC, Peet AC,
    Auer DP, Grundy RG, et al. Meta-analysis of apparent diffusion coefficient in pediatric medulloblastoma, ependymoma, and pilocytic astrocytoma. J Magn Reson Imaging 2022; 56(1): 147-57.
  13. Qin JB, Zhang H, Wang XC, Tan Y, Wu XF. Combination value of diffusion-weighted imaging and dynamic susceptibility contrast-enhanced MRI in astrocytoma grading and correlation with GFAP, Topoisomerase IIα and MGMT. Oncol Lett 2019; 18(3): 2763-70.
  14. Phuttharak W, Wannasarnmetha M, Wara-Asawapati S, Yuthawong S. Diffusion MRI in evaluation of pediatric posterior fossa tumors. Asian Pac J Cancer Prev 2021; 22(4): 1129-36.
  15. Iima M, Partridge SC, Le Bihan D. Six DWI questions you always wanted to know but were afraid to ask: clinical relevance for breast diffusion MRI. Eur Radiol 2020; 30(5): 2561-70.
  16. Dorrius MD, Dijkstra H, Oudkerk M, Sijens PE. Effect of b value and pre-admission of contrast on diagnostic accuracy of 1.5-T breast DWI: a systematic review and meta-analysis. Eur Radiol 2014; 24(11): 2835-47.
  17. Khan F. Khan's lectures: Handbook of the physics of radiation therapy. Baltimore, Maryland: Williams and Wilkins; 2009. p. 36-53.
  18. Podgorsak EB. Review of radiation oncology physics: a handbook for teachers and students. Vienna, Austria: IAE Agency; 2003. p. 13.
  19. Hamilton KR, Lee SS, Urquhart JC, Jonker BP. A systematic review of outcome in intramedullary ependymoma and astrocytoma. J Clin Neurosci 2019; 63: 168-75.
  20. Kapoor M, Gupta V. Astrocytoma. StatPearls [Internet]: StatPearls Publishing; 2021.
  21. Zeng L, Soliman H. Adult pilocytic astrocytoma. In: Halasz LM, Lo SS, Chang EL, Sahgal A, editors. Intracranial and spinal radiotherapy. Heidelberg, Germany: Springer; 2021. p. 89-94.
  22. Lin MH, Yang M, Dougherty J, Tasson A, Zhang Y, Mohamad O, et al. Radiation therapy for pediatric brain tumors using robotic radiation delivery system and intensity modulated proton therapy. Pract Radiat Oncol 2020; 10(3): e173-82.
  23. Mahmood F, Johannesen HH, Geertsen P, Hansen RH. Ultra-early apparent diffusion coefficient change indicates irradiation and predicts radiotherapy outcome in brain metastases. Acta Oncol 2017; 56(11): 1651-3.
  24. Maiya VM, Chundru S, Bhargav J, Swamy S, Shivalingappa SS, Rao RM, et al. Correlation of magnetic resonance imaging apparent diffusion coeffecient values with treatment outcome in high grade glioma patients undergoing concurrent chemoradiation. Int J Radiat Oncol Biol Phys 2018; 102(3): e300.
  25. Jakubovic R, Zhou S, Heyn C, Soliman H, Zhang L,
    Aviv R, et al. The predictive capacity of apparent diffusion coefficient (ADC) in response assessment of brain metastases following radiation. Clin Exp Metastasis 2016; 33(3): 277-84.
  26. Li C, Gan Y, Chen H, Chen Y, Deng Y, Zhan W,
    et al. Advanced multimodal imaging in differentiating glioma recurrence from post-radiotherapy changes. Int Rev Neurobiol 2020; 151: 281-97.
  27. Hein PA, Eskey CJ, Dunn JF, Hug EB. Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. AJNR Am J Neuroradiol 2004; 25(2): 201-9.
  28. Mardor Y, Roth Y, Ocherashvilli A, Spiegelmann R, Tichler T, Daniels D, et al. Pretreatment prediction of brain tumors response to radiation therapy using high b-value diffusion-weighted MRI. Neoplasia 2004; 6(2): 136-42.
  29. Wu CC, Jain R, Radmanesh A, Poisson LM, Guo WY, Zagzag D, et al. Predicting genotype and survival in glioma using standard clinical MR imaging apparent diffusion coefficient images: a pilot study from the cancer genome atlas. AJNR Am J Neuroradiol 2018; 39(10): 1814-20.
  30. Ravn S, Holmberg M, Sørensen P, Frøkjær JB, Carl J. Differences in supratentorial white matter diffusion after radiotherapy–new biomarker of normal brain tissue damage? Acta Oncologica. 2013; 52(7):
    1314-9.
Journal of Isfahan Medical School
Volume 40, Issue 681
1st Week, October
Autumn 2022
Pages 571-577

  • Receive Date 28 September 2022