Abstract
In the present research, we have developed a model-based crisp logic function statistical classifier decision support system supplemented with treatment planning systems for radiation oncologists in the treatment of glioblastoma multiforme (GBM). This system is based on Monte Carlo radiation transport simulation and it recreates visualization of treatment environments on mathematical anthropomorphic brain (MAB) phantoms. Energy deposition within tumour tissue and normal tissues are graded by quality audit factors which ensure planned dose delivery to tumour site thereby minimising damages to healthy tissues. The proposed novel methodology predicts tumour growth response to radiation therapy from a patient-specific medicine quality audit perspective. Validation of the study was achieved by recreating thirty-eight patient-specific mathematical anthropomorphic brain phantoms of treatment environments by taking into consideration density variation and composition of brain tissues. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost-effective, nor patient-specific customized and is often less accurate. The above-highlighted drawbacks can be overcome by using open-source Electron Gamma Shower (EGSnrc) software and clinical case reports for MAB phantom synthesis which would result in accurate dosimetry with due consideration to the time factors. Considerable dose deviations occur at the tumour site for environments with intraventricular glioblastoma, haematoma, abscess, trapped air and cranial flaps leading to quality factors with a lower logic value of 0. Logic value of 1 depicts higher dose deposition within healthy tissues and also leptomeninges for majority of the environments which results in radiation-induced laceration.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and material
CT scan data, Peptide atlas protein sequence data, PubChem, Medical case reports, Biochemistry assays of tumours, IAEA atomic and molecular data for radiotherapy research, NIST physical Measurements laboratory ESTAR program for stopping power range of electrons and EGSnrc open source radiation transport software.
Code availability
Self developed C and FORTRAN codes used for EGSnrc open source software and NIST data.
References
Adeeb N, Mortazavi MM, Deep A, Griessenauer CJ, Watanabe K, Shoja MM, Loukas M, Tubbs RS (2012) The pia mater: a comprehensive review of literature. Childs Nervous Syst 29(1):1803–1810. https://doi.org/10.1007/s00381-013-2044-5
Agazaryan N, Solberg TD, DeMarco JJ (2002) Patient specific quality assurance for the delivery of intensity modulated radiotherapy. J Appl Clin Med Phys 4(1):40–50. https://doi.org/10.1120/jacmp.v4i1.2540
Akpati H, Kim CS, Kim B, Park T, Meek A (2008) Unified dosimetry index (UDI): a figure of merit for ranking treatment plans. J Appl Clin Med Phys 9(3):99–108. https://doi.org/10.1120/jacmp.v9i3.2803
Alfonso JCL, Herrero MA, Núñez L (2015) A dose-volume histogram-based decision-support system for dosimetric comparison of radiotherapy treatment plans. Radiat Oncol 10(1):1–9. https://doi.org/10.1186/s13014-015-0569-3
Al-Mohammed HI (2011) Patient specification quality assurance for Glioblastoma Multiforme brain tumours treated with intensity modulated radiation therapy. Int J Med Sci 8(6):461–466. https://doi.org/10.7150/ijms.8.461
Barber TW, Brockway JA, Lawrence S, Higgins, (1970) The density of tissues in and about the head. Acta Neurol Scand 46(1):85–92. https://doi.org/10.1111/j.1600-0404.1970.tb05606.x
Beilla S, Chauveau N, Laprie A, Bardiès M, Franceries X (2016) Which impact of tumour density variations on absorbed dose in external radiotherapy. Physica Med. https://doi.org/10.1016/j.ejmp.2016.07.147
Berger J (1994) Roentgen: radiation therapy and case-based reasoning: proceedings of the tenth conference on artificial intelligence for application. IEEE Xplore 1(1):171–177. https://doi.org/10.1109/CAIA.1994.323677
Berger MJ, Coursey JS, Zucker MA, Chang J (2017) Stopping-power & range tables for electrons, protons, and helium ions. NIST Stand Ref Database. https://doi.org/10.18434/T4NC7P
Boldrini L, Bibault J-E, Masciocchi C, Shen Y, Bittner M-I (2019) Deep learning: a review for the radiation oncologist. Front Oncol. https://doi.org/10.3389/fonc.2019.00977
Cai Y, Wu J, Li Z, Long Q (2016) Mathematical modelling of a brain tumour initiation and early development: a coupled model of glioblastoma growth, pre-existing vessel co-option, angiogenesis and blood perfusion. PLoS ONE. https://doi.org/10.1371/journal.pone.0150296
Canelas HM, Aisen J, De Jorge FB, Anghinah A (1969) Biochemistry of the normal dura mater of the human brain. Neuro Psiquiatria 27(2):85–88. https://doi.org/10.1590/S0004-282X1969000200001
Chaplain MAJ (1996) Avascular Growth, angiogenesis and vascular growth in solid tumours: the mathematical modelling of the stages of tumour development. Math Comput Model 23(6):47–87. https://doi.org/10.1016/0895-7177(96)00019-2
Chen J, Kerr GD, Cullings HM (2012) A comparison of organ doses between mathematical and voxel phantoms with the Dso2 photon fluences. Radiat Prot Dosimetry 149(1):49–55. https://doi.org/10.1093/rpd/ncr485
Chen Xing Wu, Lin G-S, Lin Z-X, Zhang J-D, Liu S-Y, Zhou C-F (2015) Peritumoral oedema shown by MRI predicts poor clinical outcome in glioblastoma. World J Surg Oncol 13(97):2–9. https://doi.org/10.1186/s12957-015-0496-7
Cloft HJ and Kallmes DF (2004) Aneurysm packing with Hydro Coil embolic system versus platinum coil: clinical experience. Am J Neuroradiol 25(1):60–62. www.ajnr.org/content/25/1/60.abstract.
Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, Holmes CJ, Evans AC (1998) Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 17(3):463–468. https://doi.org/10.1109/42.712135
Colombo MC, Giverso C, Faggiano E, Boffano C, Acerbi F, Ciarletta P (2015) Towards the personalized treatment of Glioblastoma: integrating patient-specific clinical data in a continuous mechanical model. PLoS One 10(7):1–23. https://doi.org/10.1371/journal.pone.0132887
Conegero CI, Chopard RP (2003) Tri dimensional architecture of the collagen element in the arachnoid granulations in humans. Arq Neuropsiquiatr 61(3A):561–565. https://doi.org/10.1590/S0004-282X2003000400007
Coy H, Hsieh K, Willie Wu, Nagarajan MB, Young JR, Douek ML, Brown MS, Scalzo F, Raman SS (2019) Deep learning and radiomics: the utility of Google TensorFlow™ inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT. Abdominal Radiol 44(6):2009–2020. https://doi.org/10.1007/s00261-019-01929-0
Daphne A, Haas-Kogan, Yount G, Haas M, Dan Levi BS, Kogan SS, Hu L, Vidair C, Deen DF, Dewey WC, Israel MA (1996) p53-Dependent G1 arrest and p53-independent apoptosis influences the radiobiologic response of glioblastoma. Int J Radiat Oncol Biol Phys 36(1):95–103. https://doi.org/10.1016/S0360-3016(96)00244-1
Dempsey MF, Condon BR and Hadley DM (2005) Measurement of tumour size in recurrent malignant glioma: 1D, 2D or 3D? Am J Neuroradiol 26(4):770–776. http://www.ajnr.org/content/26/4/770.
Enderling H, Chaplain MAJ, Hahnfeldt P (2010) Quantitative modelling of tumour dynamics and radiotherapy. Acta Biotheor 58(4):341–353. https://doi.org/10.1007/s10441-010-9111-z
Gilroy J (2000) Basic neurology, 3rd edn. McGraw-Hill, New York
Gimsa J, Habel B, Scheriber U, Van Rienen U, Strauss U, Gimsa U (2005) Choosing electrodes for deep brain simulations—electrochemical considerations. J Neurosci Methods 142(2):251–265. https://doi.org/10.1016/j.jneumeth.2004.09.001
Greenberg MS (2001) Handbook of neurosurgery, 5th edn. Thieme, New York
Grochowski C, Blicharska E, Krukow P, Jonak K, Maciejewski M, Jonak DSK, Flieger J, Maciejewsk R (2019) Analysis of trace elements in human brain: its aim, methods, and concentration levels. Front Chem 7(115):1–14. https://doi.org/10.3389/fchem.2019.00115
Hana Y, Shin EH, Lim C, Kang S-K, Park SH, Lah J-E, Suh T-S, Yoon M, Lee SB, Cho SH, Ibbott GS, Sang Gyu Ju, Ahn YC (2008) Dosimetry in an IMRT phantom designed for a remote monitoring program. Med Phys 35(6):2519–2527. https://doi.org/10.1118/1.2903440
Hathout L, Patel V, Wen P (2016) A 3-dimensional DTI MRI-based model of GBM growth and response to radiation therapy. Int J Oncol 49(3):1081–1087. https://doi.org/10.3892/ijo.2016.3595
Höhne J, Brawanski A, Gassner HG, Schebesch K-M (2013) Feasibility of the custom-made titanium cranioplasty CRANIOTOP. Surg Neurol Int 4(1):88. https://doi.org/10.4103/2152-7806.114811
Hori H, Moretti G, Rebora A, Crovato F (1972) The thickness of human scalp: normal and bald. J Investig Dermatol 58(6):396–399. https://doi.org/10.1111/1523-1747.ep12540633
Jain NL, Khan MG, Drzymala RE, Emami BE, Purdy JA (1993) Objective evaluation of 3D treatment plans: a decision-analytic tool incorporating treatment preferences for radiation oncologists. Int J Radiat Oncol Biol Phys 26(2):321–333. https://doi.org/10.1016/0360-3016(93)90213-F
Jiang X, Wells A, Brufsky A, Neapolitan R (2019) A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis. PLoS ONE. https://doi.org/10.1371/journal.pone.0213292
Juran JM, Blanton Godfrey A, Hoogstoel RE, Schilling EG (1998) Juran’s quality handbook, 5th edn. McGraw-Hill, New York
Kang J, Schwartz RS, Flickinger JC, Beriwal S (2015) Machine learning approaches for predicting radiation therapy outcomes: a clinician’s perspective. Int J Radiat Oncol Biol Phys 93(5):1127–1135. https://doi.org/10.1016/j.ijrobp.2015.07.2286
Kawrakow I, Rogers RDWO, Mainegra-Hing E, Tessier F, Walters BRB (2021) The EGSnrc code system: monte carlo simulation of electron and photon transport: ionizing radiation standards, National Research Council Canada: Ottawa Canada. NRCC Report PRIS 701 1(1):1–312. https://doi.org/10.4224/40001303
Kerklaan JP, Lycklama a Nijeholt GJ, Wiggenraad RGJ, Berghuis B, Postma TJ, Taphoorn MJB (2010) SMART syndrome a late reversible complication after radiation therapy or brain tumours. J Neurol 258(6):1098–1104. https://doi.org/10.1007/s00415-010-5892-x
Kim YJ, Lee SK, Cho MK, Kim Y-J (2008) Intraventricular glioblastoma multiforme with previous history of intracerebral haemorrhage: a case report. J Korea Neurosurg Soc 44(6):405–408. https://doi.org/10.3340/jkns.2008.44.6.405
Kutcher GJ, Coia L, Gillin M, Hanson WF, Leibel S, Morton RJ, Palta JR, Purdy JA, Reinstein LE, Svensson GK, Weller M, Wingfield L (1994) Comprehensive QA for radiation oncology: report of AAPM radiation therapy committee task group 40. Med Phys 21(4):581–618. https://doi.org/10.1118/1.597316
Lima JY, Leech M (2016) Use of auto-segmentation in the delineation of target volumes and organs at risk in head and neck. Acta Oncol 55(7):799–806. https://doi.org/10.3109/0284186X.2016.1173723
Lin T, Li R, Tang X, Dy JG, Jiang SB (2009) Marker less gating for lung cancer radiotherapy based on machine learning techniques. Phys Med Biol 54(6):1555–1563. https://doi.org/10.1088/0031-9155/54/6/010
Lumniczky K, Szatmári T, Sáfrány G (2017) Ionizing radiation-induced immune and inflammatory reactions in the Brain. Front Immunol 8(517):1–13. https://doi.org/10.3389/fimmu.2017.00517
Mc Gale EHF, Pye F, Stonier C, Hutchinson C, Aber GM (1977) Studies of the inter-relationship between cerebrospinal fluid and plasma amino acid concentrations in normal individuals. J Neuro Chem 29(2):291–297. https://doi.org/10.1111/j.1471-4159.1977.tb09621.x
Mehta S, Gajjar SR, Padgett KR, Asher D, Stoyanova R, Ford JC, Mellon EA (2018) Daily tracking of Glioblastoma resection cavity, cerebral oedema and tumour volume with MRI- Guided radiation therapy. Cureus. https://doi.org/10.7759/cureus.2346
Murty OP, Mahinda HAM (2009) Variability in thickness of human skull bones and sternum-an autopsy experience. J Forensic Med Toxicol 26(2):26–31
Noble RJ, Ng JST, Barani IJ, McGuinness C (2015) TH-AB-304-09: A patient-specific heterogeneous radiobiological model for Glioblastoma Multiforme. Int J Med Phys Res Pract 42(6):3702–3703. https://doi.org/10.1118/1.4926124
Ottosson RO, Engstrom PE, Sjostorm D, Behrens CF, Karlsson A, Knoos T, Ceberg C (2009) The feasibility of using Pareto fronts for comparison of treatment planning systems and delivery techniques. Acta Oncol 48(1):233–237. https://doi.org/10.1080/02841860802251559
Parekh VS, Jacobs MA (2019) Deep learning and radiomics in precision medicine. Expert Rev Precis Med Drug Dev 4(2):59–72. https://doi.org/10.1080/23808993.2019.1585805
Pareto V (2014) Manual of political economy, Crirtical and variorum edition. Oxford University Press
Patone H, Barker J, Roberge D (2006) Effects of neurosurgical titanium mesh on radiation dose. Med Dosim 31(4):298–301. https://doi.org/10.1016/j.meddos.2006.05.001
Popple RA, Griffith HR, Sawrie SM, Fiveash JB, Brezovich IA (2005) Implementation of Talairach atlas based automated brain segmentation for radiation therapy dosimetry. Technol Cancer Res Treat 5(1):15–21. https://doi.org/10.1177/153303460600500103
Richard SA, Ye Y, Li H, Ma L, You C (2018) Glioblastoma multiforme subterfuge as acute cerebral haemorrhage. Neurol Int 10(7558):35–40. https://doi.org/10.4081/ni.2018.7558
Rockne R, Alvord EC Jr, Rockhill JK, Swanson KR (2009) A mathematical model for brain tumour response to radiation therapy. J Math Biol 58(1):561–578. https://doi.org/10.1007/s00285-008-0219-6
Roniotis A, Marias K, Sakkalis V, Manikis GC, Zervakis M (2012) Simulating radiotherapy Effect in high-grade Glioma by using diffusive modelling and brain atlases. J Biomed Biotechnol 2012(1):1–9. https://doi.org/10.1155/2012/715812
Saboori P, Sadegh A (2015) Histology and morphology of the brain subarachnoid trabeculae. Anatomy Res Int 1:1–9. https://doi.org/10.1155/2015/279814
Sajjad J, Perumal CK, Bermingham N, Marks C, Keohane C (2015) Unusual brain stone: heavily calcified primary neoplasm with some features suggestive of angiocentric glioma. J Neurosurg 123(5):1256–1260. https://doi.org/10.3171/2014.11.JNS131158
Schiavon G, Ruggiero A, Schoffski P, van der Holt B, Bekers DJ, Eechoute K, Vandecaveye V, Krestin GP, Verweij J, Sleijfer S, Mathijssen RHJ (2012) Tumour volume as an alternative response measurement for Imatinib treated GIST patients. PLoS ONE 7(11):1–11. https://doi.org/10.1371/journal.pone.0048372
Schulthesiss TE, Orton CG (1985) Models in radiotherapy: definition of decision criteria. Med Phys 12(2):183–187. https://doi.org/10.1118/1.595707
Stamatakos GS, Antipas VP, Uzunoglu NK, Dale RG (2006) A four-dimensional computer simulation model of the in vivo response to radiotherapy of glioblastoma multiforme: studies on the effect of clonogenic cell density. Br J Radiol 79(941):389–400. https://doi.org/10.1259/bjr/30604050
Thompson L, Dias HG, Campos TPR (2013) Dosimetry in brain tumour phantom at 15 MV 3D conformal radiation therapy. Radiat Oncol 8(168):2–14. https://doi.org/10.1186/1748-717X-8-168
Toh CH, Wei K-C, Chang C-N, Hsu P-W, Wong H-F, Ng S-H, Castillo M, Lin C-P (2012) Differentiation of pyrogenic abscesses from necrotic glioblastomas with use of susceptibility weighted imaging. Am J Neuroradiol 33(8):1534–1538. https://doi.org/10.3174/ajnr.A2986
Tol JP, Delaney AR, Dahele M, Slotman BJ, Verbakel WFAR (2014) Evaluation of a knowledge-based planning solution for head and neck cancer. Int J Radiat Oncol Biol Phys 91(3):612–620. https://doi.org/10.1016/j.ijrobp.2014.11.014
Valdes G, Simone CB, Chen J, Lin A, Yom SS, Pattison AJ, Carpenter CM, Solberg TD (2017) Clinical decision support of radiotherapy treatment planning: a data-driven machine learning strategy for patient-specific dosimetric decision making. Radiother Oncol 125(3):392–397. https://doi.org/10.1016/j.radonc.2017.10.014
Wael MA, El-Ghani A (2018) Cranioplasty with polymethyl methacrylate implant: solutions of pitfalls. Egypt J Neurosurg 33(7):1–4. https://doi.org/10.1186/s41984-018-0002-y
Weller RO, Sharp MM, Christodoulides M, Carare RO, Møllgård K (2018) The meninges as barriers and facilitators for the movement of fluid, cells and pathogens related to the rodent and human CNS. Acta Neuropathol 135(1):363–385. https://doi.org/10.1007/s00401-018-1809-z
Wieser HG, Rudolph U, Blau N, Boison D, Imhof H-G, Bernays R, Yonekawa Y (2003) Amino acid composition of brain cysts: levels of excitatory amino acids in cyst fluid fail to predict seizures. Epilepsy Res 55(3):191–199. https://doi.org/10.1016/S0920-1211(03)00106-2
Wollemann M (1972) Biochemistry of brain tumours. In: Lajtha A (ed) Handbook of neurochemistry, 1st edn. Springer, New York, pp 503–542
Wood S, Krishnamurthy N, Santini T, Raval SB, Farhat N, Holmes JA, Ibrahim TS (2017) Design and fabrication of a realistic anthropomorphic heterogeneous head phantom for MR purposes. PLoS ONE. https://doi.org/10.1371/journal.pone.0183168
Ziyal IM, Ece K, Bilginer B, Tezel GG, Zcan OEO (2004) A glioma with an arteriovenous malformation: an association or a different entity? Acta Neurochir 146:83–86. https://doi.org/10.1007/s00701-003-0161-8
Acknowledgements
Thanks to Regional cancer center, Thiruvananthapuram, India and Institute of Medical Sciences – BHU, Varanasi, India for providing moral support.
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
Developing mathematical models based on collected CT scan data and works of literature. Material/tissue of brain part synthesised using NIST data for extracting excitation energy and density correction files. Material /tissues recreated on Monte Carlo EGSnrc platform from the above-compiled outputs and NIST codes. Program coding in EGSnrc performed for irradiation each MAB phantom with 6 MV photon beams. Complied outputs in the format of scattered and total doses to generate a Pareto charts for classification.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Appendices
Appendix 1: PDD deviations in each brain layer/tissue/material for treatment environments GBM 1 to GBM 38
Appendix 2: Collision, Radiative and Total stopping powers of brain layers/tissues within the MAB Phantom
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Praveen Kumar, C., Aggarwal, L.M., Bhasi, S. et al. A Monte Carlo simulation-based decision support system for radiation oncologists in the treatment of glioblastoma multiforme. Radiat Environ Biophys 63, 215–262 (2024). https://doi.org/10.1007/s00411-024-01065-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00411-024-01065-4