Radiomic Study for Objectification of Diagnostics and Complex Treatment of Glioblastoma

Introduction. Glioblastoma is a neuroepithelial malignant brain tumour of predominantly astrocytic origin with an aggressive course and an extremely unfavorable prognosis. Since the median of overall survival with glioblastoma is 14.6 months after complex treatment that includes a combination of surgical treatment, radiation therapy and chemotherapy, the development a personalized approach in the diagnosis and treatment of glioblastomas is appeared to be urgent.

Materials and methods. MRIs of a patient undergoing chemoradiotherapy for glioblastoma G4 were performed on the following MRI scanners: Philips Ingenia 1.5T and Philips Ingenia Ambient 1.5T. The analysis of MR-images was carried out using the Matlab 2021 apps.

Results and discussion. MR-images were analyzed before and after surgery, and after a course of chemoradiotherapy. The statistical characteristics of the local brightness distribution of the lesion image, which are described by statistical texture parameters, were analyzed as informative features of the lesion area on the images. Initial confirmation of the ability to objectify diagnosis and treatment using the above statistical parameters of T2 MR images of lesion area has been obtained.

Conclusion. The aim of further research in this area is to use radiomic study for planning and monitoring the treatment of high-grade gliomas, estimate disease outcomes, and analyze the response to complex treatments in a predictive way.

1. Yakovlenko Yu.G. Glioblastoma: the current state of the problem. Medical Herald of the South of Russia. 2019;10(4):28–35 (In Russ.). DOI: 10.21886/2219-8075-2019-10-4-28-35

2. Zolotova S.V., Khokhlova E.V., Belyashova A.S., Nikolaeva A.A., Starovoytov D.V., Igoshina E.N., et al. Investigation of the metabolic features of primary glioblastomas by Tc-MIBI SPECT/CT and evaluation of their effect on disease prognosis. Zhurnal Voprosy Neirokhirurgii Imeni N.N. Burdenko. 2019;83(2):17–26 (In Russ.). DOI: 10.17116/neiro20198302117

3. Beig N., Bera K., Prasanna P., Antunes J., Correa R., Singh S., et al. Radiogenomic-based survival risk stratification of tumor habitat on Gd-T1w MRI is associated with biological processes in glioblastoma. Clin Cancer Res. 2020;26(8):1866–76. DOI: 10.1158/1078-0432.ccr-19-2556

4. Ostrom Q.T., Gittleman H., Truitt G., Boscia A., Kruchko C., Barnholtz-Sloan J.S. CBTRUS Statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2011–2015. Neuro Oncol. 2018;20(suppl_4):iv1–86. DOI: 10.1093/neuonc/noy131

5. Incoronato M., Aiello M., Infante T., Cavaliere C., Grimaldi A., Mirabelli P., et al. Radiogenomic analysis of oncological data: a technical survey. Int J Mol Sci. 2017;24(3):14–21. DOI: 10.3390/ijms18040805

6. Zanfardino M., Franzese M., Pane K., Cavaliere C., Monti S., Esposito G., et al. Bringing radiomics into a multi-omics framework for a comprehensive genotype–phenotype characterization of oncological diseases. J Transl Med. 2019;34(3):26–38. DOI: 10.1186/s12967-019-2073-2

7. Mazurowski M.A., Clark K., Czarnek N.M., Shamsesfandabadi P., Peters K.B., Saha A. Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multiinstitutional study with The Cancer Genome Atlas data. J Neurooncol. 2017;133(1):27–35. DOI: 10.1007/s11060-017-2420-1

8. Boxerman J.L., Quarles C.C., Hu L.S., Erickson B., J., Gerstner E.R., Smits M., et al. Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas. Neuro Oncol. 2020;22(9):1262–75. DOI: 10.1093/neuonc/noaa141

9. Shenouda G., Souhami L., Petrecca K., Owen S., Panet-Raymond V., Guiot M.-C., et al. A phase 2 trial of neoadjuvant temozolomide followed by hypofractionated accelerated radiation therapy with concurrent and adjuvant temozolomide for patients with glioblastoma. Int J Radiat Oncol Biol Phys. 2017;97(3):487–94. DOI: 10.1016/j.ijrobp.2016.11.006

10. Oh S., Yeom J., Cho H.J., Kim J., Yoon S.-J., Kim H., et al. Integrated pharmaco-proteogenomics defines two subgroups in isocitrate dehydrogenase wild-type glioblastoma with prognostic and therapeutic opportunities. Nat Commun. 2020;11(1):3288. DOI: 10.1038/s41467-020-17139-y

11. Moradmand H., Aghamiri S.M.R., Ghaderi R. Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma. J Appl Clin Med Phys. 2020;21(1):179–90. DOI: 10.1002/acm2.12795

12. Lotan E., Jain R., Razavian N., Fatterpekar G.M., Lui Y.W. State of the art: machine learning applications in glioma imaging. Am J Roentgenol. 2019;212(1):26–37. DOI: 10.2214/ajr.18.20218

13. Akbari H., Bakas S., Pisapia J.M., Nasrallah M.P., Rozycki M., Martinez-Lage M., et al. In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro Oncol. 2018;20(8):1068–79. DOI: 10.1093/neuonc/noy033

14. Kickingereder P., Neuberger U., Bonekamp D., Piechotta P.L., Götz M., Wicket A., et al. Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro Oncol. 2018;0(6):848–57. DOI: 10.1093/neuonc/nox188

15. Bae S., Choi Y.S., Ahn S.S., Chang J.H., Kang S.-G., Kim E.H., et al. Radiomic MRI phenotyping of glioblastoma: Improving survival prediction. Radiology. 2018;289(3):797–806. DOI: 10.1148/radiol.2018180200

16. Rathore S., Mohan S., Bakas S., Sako C., Badve C., Pati S., et al. Multi-institutional noninvasive in vivo characterization of IDH, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk). Neuro Oncol Adv. 2020;2(suppl_4):iv22–34. DOI: 10.1093/noajnl/vdaa128

17. Rathore S., Akbari H., Doshi J. Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning. J Med Imaging. 2018;5(02):1. DOI: 10.1117/1.jmi.5.2.021219

18. Guo G., Sun Y., Hong R., Xiong J., Lu Y., Liu Y., et al. IKBKE enhances TMZchemoresistance through up regulations of MGMT expression in glioblastoma. Clin Trans Oncol. 2019;22(8):1252–62. DOI: 10.1007/s12094-019-02251-3

19. Strauss S.B., Meng A., Ebani E.J., Chiang G.C. Imaging glioblastoma posttreatment: Progression, pseudoprogression, pseudoresponse, radiation necrosis. Neuroimaging Clin. 2021;31(1):103–20. DOI: 10.1016/j.nic.2020.09.010.

20. Gonzales R., Woods R. Digital Image Processing. Moscow: Technosfera; 2012. 1104 p. (In Russ.).