Quality Assurance of Dynamic Multileaf Collimator for Intensity Modulated Radiation Therapy & Rapid Arc Treatment Using Portal Imaging and Films
Muhammed Anees K*
Department of Radiation Oncology, KIMS Cancer Center, Trivandrum, Kerala, India
- Corresponding Author:
- Muhammed Anees K
Department of Radiation Oncology
KIMS Cancer Center
Trivandrum, Kerala, India
Tel: +91 9746240625
E-mail: [email protected]
Received date: 07/02/2019; Accepted date: 09/05/2019; Published date: 16/05/2019
Visit for more related articles at Research & Reviews: Journal of Medical and Health Sciences
The most effective part of the linear accelerator is dynamic MLC, so the accuracy of positioning of the dMLC leaf is very
importance in the treatment technique such as Intensity Modulated Radiation Therapy (IMRT) and Rapid Arc, the radiation
transmission through MLC must controlled. To investigate different parameters of dynamic MLC (dMLC) for the commissioning of
IMRT and Rapid Arc such MLC transmission factor, Dosimetric leaf gap (DLG), leaf speed and positional accuracy using standard
test patters provided by vendor.
Materials And Methods
All the works were performed with dual energy (6 MV & 10 MV) linear accelerator provided by Clinac iX (varian medical
system) equipped with millennium-120 MLC. The EPID attached to the Linac is based on amorphous silicon type flat panel
detectors (a-Si 1000), As the part of MLC QA, Initially measured the MLC transmission factor by using 0.65 cc ionization chamber
at depth 10 cm in solid water phantom, The MLC transmission factor is the ratio of meter reading obtained for the closed MLC
field to the meter reading obtained for the open field. The mean reading of the MLC transmission factor of the two banks of MLC
was taken to be the MLC transmission factor .
Dosimetric leaf gap (DLG) computed from graph. Leaf transmission and leakage through the rounded leaf ends is known as
dosimetric leaf separation (DLS). The DLS is the quantity added to the leaf gap to compute the dose more accurately, especially
for small gaps. It is used by the leaf motion calculator as an offset value on leaf position In order to check positional accuracy,
MLC gap, Leaf speed and complex dynamic field, different dMLC test patterns provided by Varian are executed using EPID .
dMLC QA for IMRT using Amorphous silicon based EPID is attached to the exact arm of Clinac iX. A-Si1000 (Varian medical
systems) calibrated for hardware and dosimetric purpose for different energies and various dose rates. The active area of EPID
consists in a matrix of 1024 X 768 for 40 X 30 cm2 at source to detector distance (SDD) of 100 cm. The different QA test patters
for dMLC provided by Varian such as picket fence test, pyramid test, complex tests, Synchronized Segmented Stripes test, Non
Synchronized Segmented Stripes test, X Wedges Y Wedge, Continuous strip test were performed.
The MLC transmission factor is the ratio of meter reading obtained for the closed MLC field to the meter reading obtained for
the open field. The mean reading of the MLC transmission factor of the two banks of MLC was taken to be the MLC transmission
factor. The tabulated values were shown Table 1.
||MLC Transmission (%)
Table 1. MLC transmission.
The average leaf transmission was found to be 1.46%, 1.67% for 6 and 10 MV respectively; Dosimetric leaf gap obtained
from the graph is found to be 1.3 and 1.4 mm for 6 and 10 MV respectively Figures 1-4. Various dMLC tests patters for IMRT and
Rapid arc were measured using EPID and therapy verification films.
Figure 1. Measurement of DLG Clinac iX for 6 MV photon..
Figure 2.Measurement of DLG Clinac iX for 10 MV photon.
Figure 3.A) Picket Fence Test B) Synchronized Segmented Stripes Test.
The match lines of picket fence test found to be at -15.0 ± 0.1; -10.0 ± 0.1; -5.0 ± 0.1; 0.0 ± 0.1; 5.0 ± 0.1; 10.0 ± 0.1; 15.0
± 0.1 from the center of the field. The match lines of synchronized segmented stripes test appear at -12.0 ± 0.1 cm; -8.0 ± 0.1
cm; -4.0 ± 0.1 cm; 0.0 ± 0.1 cm; 4.0 ± 0.1 cm; 8.0 ± 0.1 cm; 12.0 ± 0.1 cm from the center of the field. The match lines of Non
Synchronized Segmented Stripes test found to be -4.0 ± 0.1 cm; -2.0 ± 0.1 cm; 0.0 ± 0.1 cm; 2.0 ± 0.1 cm and 4.0 ± 0.1 cm from
the center of the field. The match lines of X-wedge and Y wedge tests segments appeared as -4.0 ± 0.1 cm; -2.0 ± 0.1 cm; 0.0
± 0.1 cm; 2.0 ± 0.1 cm and 4.0 ± 0.1 cm from the center of the field the match line segments of pyramid test found to at -4.0 ±
0.1 cm; -3.0 ± 0.1 cm; -2.0 ± 0.1 cm; -1.0 ± 0.1 cm; 0.0 ± 0.1 cm; 1.0 ± 0.1 cm; 2.0 ± 0.1 cm; 3.0 ± 0.1 cm; 4.0 ± 0.1 cm from
the center of the field. All the match lines found to be less than 5 mm, so the QA result indicates that MLC opening is operating
properly. All results are found to be within the tolerance limit.
An initial attempt for commissioning of dMLC has been performed, and the dosimetric parameters of MLCs of such as MLC
transmission factor and dosimetric leaf gap (DLG) are used to be modeled in TPS algorithm. All the dynamic MLC test patterns
for IMRT and Rapid arc results are shown to be within acceptable limit. It can be concluded that the dosimetric properties of the
MLCs can be precisely controlled and hence can be used for IMRT and Rapid Arc techniques.
- Miles JH. Autism spectrum disorders-a genetics review. Genet Med. 2011;13:278-294.
- Williams JG, Higgins JP, Brayne CE. Systematic review of prevalence studies of autism spectrum disorders. Arch Dis Child. 2006;91:8-15.
- Wan Y, Hu Q, Li T, Jiang L, Du Y, Feng L. Prevalence of autism spectrum disorders among children in China: a systematic review. Shanghai Arch Psychiatry. 2013;25:70-80.
- Centers for Disease Control and Prevention. Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2010. MMWR Surveill Summ. 2014;63:1-21.
- Betancur C. Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res 2014;1380:42-77.
- Yu TW, Berry-Kravis E. Autism and fragile X syndrome. Semin Neurol. 2014;34:258-265.
- De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE, et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature. 2014;515:209-215.
- Levy D, Ronemus M, Yamrom B, Lee YH, Leotta A, Kendall J. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron. 2011;70:886-897.
- Li SO, Wang JL, Bjørklund G, Zhao WN, Yin CH. Serum copper and zinc levels in individuals with autism spectrum disorders. Neuroreport. 2014;25:1216-1220.
- Mundalil Vasu M, Anitha A, Thanseem I, Suzuki K, Yamada K, Takahashi T.Serum microRNA profiles in children with autism. Mol Autism. 2014;5: 40.
- West PR, Amaral DG, Bais P, Smith AM, Egnash L.A, Ross ME. Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children. PLoS One. 2014;9:e112445.
- Corbett BA, Kantor AB, Schulman H, Walker WL, Lit L. A proteomic study of serum from children with autism showing differential expression of apolipoproteins and complement proteins. Mol. Psychiatry. 2007;12:292-306.
- Gerald WH, Ronald JC. Glycomics hits the big time. Cell. 2010;143:672-676.
- Schachter H, Freeze, HH. Glycosylation diseases: quo vadis? Biochim Biophys Acta. 2009;925-930.
- Taniguchi N. Human disease glycomics/proteome initiative (HGPI). Mol Cell Proteomics. 2008;7:626-627.
- Haga Y, Uemura M, Baba S, Inamura K, Takeuchi K, Nonomura N, et al. Identification of multisialylated LacdiNAc structures as highly prostate cancer specific glycan signatures on PSA. Anal Chem. 2019;91:2247-2254.
- Qin Y, Chen Y, Yang J, Wu F, Zhao L, Yang F. Serum glycopattern and Maackia amurensis lectin-II binding glycoproteins in autism spectrum disorder. Sci Rep 2017;7: 46041.
- Kaji H, Yamauchi Y, Takahashi N, Isobe T.Mass spectrometric identification of N-linked glycopeptides using lectin-mediated affinity capture and glycosylation site-specific stable isotope tagging. Nat Protoc 2007;1:3019-3027.
- Sturiale L, Barone R, Palmigiano A, Ndosimao CN, Briones P, Adamowicz M. Multiplexed glycoproteomic analysis of glycosylation disorders by sequential yolk immunoglobulins immunoseparation and MALDI-TOF MS. Proteomics. 2008; 8: 3822-3832.
- Zhang H, Li XJ, Martin DB.Aebersold, R. Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat Biotechnol. 2003;21:660-666.
- Tian YA, Zhou, Elliott S, Aebersold R, Zhang H. Solid-phase extraction of N-linked glycopeptides. Nat Protoc. 2007;2:334-339.
- Lewandrowski U, Lohrig K, Zahedi R, Wolters D, Sickmann A. Glycosylation site analysis of human platelets by electrostatic repulsion hydrophilic interaction chromatography. Clin Proteomics. 2008;4:25-36.
- Zhang H, Guo T, Li X, Datta A, Park JE, Yang J. Simultaneous characterization of glyco- and phosphoproteomes of mouse brain membrane proteome with electrostatic repulsion hydrophilic interaction chromatography. Mol Cell Proteomics. 2010; 9: 635-647.
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. (2013). 5th edn. Arlington, VA.
- Yang G, Cui T, Wang Y, Sun S, Ma T, Wang T. Selective isolation and analysis of glycoprotein fractions and their glycomes from hepatocellular carcinoma sera. Proteomics. 2013; 13:1481-1498.
- Qin Y, Zhong Y, Yang G, Ma T, Jia L, Huang C. Profiling of concanavalin A-binding glycoproteins in human hepatic stellate cells activated with transforming growth factor-β 1. Molecules. 2014;19:19845-19867.
- Morelle W, Michalski JC. Analysis of protein glycosylation by mass spectrometry Nat Protoc. 2007;2:1585-1602.
- Ruhaak LR, Huhn C, Waterreus WJ, Boer AR, Neusüss C, Hokke CH. Hydrophilic interaction chromatography-based highthroughput sample preparation method for N-glycan analysis from total human plasma glycoproteins. Anal Chem.2008;80:6119-6126.
- Ceroni A, Maass K, Geyer H, Geyer R, Dell A, Haslam SM. GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans. J Proteome Res.2008:7:1650-1659.
- Chakraborty A, Dorsett KA, Trummell HQ, Yang ES, Oliver PG, Bonner JA. ST6Gal-I sialyltransferase promotes chemoresistance in pancreatic ductal adenocarcinoma by abrogating gemcitabine-mediated DNA damage. J Biol Chem. 2018;293:984-994.
- Go S, Veillon L, Ciampa MG, Mauri L, Sato C, Kitajima,K. Altered expression of ganglioside GM3 molecular species and a potential regulatory role during myoblast differentiation. J Biol Chem. 2017;292:7040-7051.
- Chowdhury SR, Ray U, Chatterjee BP, Roy SS. Targeted apoptosis in ovarian cancer cells through mitochondrial dysfunction in response to Sambucus nigra agglutinin. Cell Death Dis. 2017;8:e2762.
- Shah MH, Telang SD, Shah PM, Patel PS.Tissue and serum alpha 2-3- and alpha 2-6-linkage specific sialylation changes in oral carcinogenesis. Glycoconj J. 2008; 25:279-290.
- Llop E, Ferrer-Batallé M, Barrabés S, Guerrero PE, Ramírez M, Saldova R. Improvement of Prostate Cancer Diagnosis by Detecting PSA Glycosylation-Specific Changes. Theranostics. 2016;6: 1190-1204.
- Vajaria BN, Patel KR, Begum R, Patel PS. Sialylation: an Avenue to Target Cancer Cells. Pathol Onco Res. 2016;22:443-447.
- Sawhney H, Kumar C. Correlation of serum biomarkers (TSA & LSA) and epithelial dysplasia in early diagnosis of oral precancer and oral cancer. Cancer Biomark. 2011;10: 43-49.
- Zhang Z, Wuhrer M, Holst S. Serum sialylation changes in cancer. Glycoconj J. 2018;35:139-160.
- Kanehisa M, Furumichi, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45: D353-D361.
- Orr SL, Le D, Long JM, Sobieszczuk P, Ma B, Tian H. A phenotype survey of thirty-six mutant mouse strains with gene targeted defects in glycosyltransferases or glycan-binding proteins. Glycobiology. 2013:23:363-380
- Cai Y, Tang X, Chen X, Li X, Wang Y, Bao, X. Liver X receptor β regulates the development of the dentate gyrus and autistic-like behavior in the mouse. Proc Natl Acad Sci. 2018;115: E2725-E2733.
- Piras F, Schiff M, Chiapponi C, Bossù P, Mühlenhoff M, Caltagirone C, et al. Brain structure, cognition and negative symptoms in schizophrenia are associated with serum levels of polysialic acid-modified NCAM. Transl Psychiatry. 2015;5:e658.
- Li Y, Fu J, Ling Y, Yago T, McDaniel JM. Song J. Sialylation on O-glycans protects platelets from clearance by liver Kupffer cells. Proc Natl Acad Sci. 2017;114:8360-8365.
- Durand G, Feger J, Coignoux M, Agneray J, Pays M. Rapid estimation of small amounts of formaldehyde liberated during periodate oxidation of a sialoglycoprotein. Anal Biochem. 1974;61:232-236.
- Massamiri Y, Durand G, Richard A, Feger J, Agneraym J. Determination of erythrocyte surface sialic acid residues by a new colorimetric method. Anal biochem. 1979;97:346-351.
- Shubhakar A, Kozak RP, Reiding KR, Royle L, Spencer DI, et al. Automated High-Throughput Permethylation for Glycosylation Analysis of Biologics Using MALDI-TOF-MS. Anal Chem. 2016;88:8562-8569.
- Powell AK, Harvey DJ. Stabilization of sialic acids in Nlinked oligosaccharides and gangliosides for analysis by positive ion matrix-assisted laser desorption/ionization mass spectrometry. RCM. 1996;10:1027-1032.
- Reiding KR, Blank D, Kuijper DM, Deelder AM, Wuhrer M. High-throughput profiling of protein N-glycosylation by MALDI-TOF-MS employing linkage-specific sialic acid esterification. Anal Chem. 2014;86:5784-5793.
- Sekiya S, Wada Y, Tanaka K. Derivatization for stabilizing sialic acids in MALDI-MS. Anal Chem. 2005;77:4962-4968.
- Liu X, Qiu H, Lee RK, Chen W, Li J. Methylamidation for sialoglycomics by MALDI-MS: a facile derivatization strategy for both alpha2,3- and alpha2,6-linked sialic acids. Anal Chem. 2010;82:8300-8306.
- De Haan N, Reiding KR, Haberger M, Reusch D, Falck D, Wuhrer M. Linkage-specific sialic acid derivatization for MALDI-TOF-MS profiling of IgG glycopeptides. Anal Chem. 2015;87:8284-8291.