ORIGINAL_ARTICLE
Effects of Mobile Phone Radiation on Surface Tension and Volume Flow Rate of Human Blood groups
Introduction: The great use of electrical appliances in different life applications is one of the most obvious concerns because of its possible health drawbacks. These investigation reports results of electromagnetic field effect emitted from mobile phones on some biophysical parameters of human blood belonging to blood groups A, B, AB & O collected from the normal persons. The parameters observed are surface tension, volume flow rate of blood. This article displays a comparative data of the above parameters for control group and test group.
Materials and Methods: The blood samples were collected from healthy persons and stored in heparin as anticoagulant. The test samples were exposed with mobile phone up to 1 hour with the interval of 15 min. The parameters such as surface tension and volume flow rate of normal and irradiated blood samples were measured using capillary viscometer, developed at Biophysics Laboratory, Nizam College, Osmania University, Hyderabad, India.
Results: It is interesting to note that surface tension of blood, irrespective of blood group, is increased significantly, when blood exposed to radiation produced by mobile phone. Volume flow rate decreases significantly in A, B and AB blood groups, and increases in blood group O, when blood exposed to radiation produced by mobile phone.
Conclusion: Mobile phone radiation has significant effect on surface tension and volume flow rate of human blood of different blood groups.
https://ijmp.mums.ac.ir/article_8863_855f655a20104773b20a0c1950855ad4.pdf
2017-09-01
122
127
10.22038/ijmp.2017.22639.1217
Human Blood Group
Electromagnetic Field
Mobile Phone
Surface tension
Volume Flow
somayeh
Arian Rad
somayeharianrad@ymail.com
1
Biophysics Research Laboratory, Department of Physics, Nizam College (Autonomous),
Osmania University, Hyderabad – 500 001, India
LEAD_AUTHOR
adeel
ahmad
dr_adeelahmad@yahoo.com
2
2. Biophysics Research Laboratory, Department of Physics, Nizam College (Autonomous), Osmania University, Hyderabad – 500 001, India.
AUTHOR
World Health Organization. [homepage on the Internet].Electromagnetic fields and public health mobile phones[update 2014, October] Available from: http://www.who.int/mediacentre/factsheets/fs193/en/
1
Bernadette F. Rodak, George A. Fritsma, Kathryn Doig. Hematology: Clinical Principles and Applications[book on the internet].Elsevier Health Sciences; 2007. Available from: https://books.google.co.in/books/about/Hematology.html?id=6sfacydDNsUC&redir_esc=y
2
World health organization. [homepage on the Internet].Electromagnetic fields and public health exposure to extremely low frequency fields, [update 2007, Jun] Available from: http://www.who.int/peh-emf/publications/facts/fs322/en/
3
SCENIHR. 2015. Scientific Committee on Emerging and Newly Identified Health Risks: Potential health effects of exposure to electromagnetic fields (EMF).
4
Hirose H, Suhara T, Kaji N, et al. Mobile phone base station radiation does not affect neoplastic transformation in BALB/3T3 cells. Bioelectromagnetics. 2008 January; 29(1):55–64.
5
Zook BC, Simmens SJ. The effects of pulsed 860 MHz radiofrequency radiation on the promotion of neurogenic tumors in rats. Radiation Research. 2006 May; 165(5):608–615.
6
Ahlbom A, Green A, Kheifets L, et al. Epidemiology of health effects of radiofrequency exposure. Environmental Health Perspectives. 2004 December; 112(17):1741–1754.
7
Mariam S and Nawal A, Effects of exposure to electromagnetic field on some hematological parameters in mice. J. Med. Chem.2012 June; 2(2): 30-42.
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Braune S, Wrocklage C, Raczek J, Gailus T and Ludking C, Resting blood pressure in Chease during exposures to a radio frequency electromagnetic field, Lancet. 1998 June 20; 351(9119): 1857-1858.
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Scheffecter K, Lu H, Norman K, Van Nood N, Munoz F and Beaudet A, Spontaneous Skin Ulceration and Defective T Cell Function in Cd 18 Null Mice, J. Expt. Med.2007; 188(1) :119-131.
10
Karel M, Jan M and Hand T, Biological Effects of Electromagnetic Waves and Their Mechanism, 1971. http://www.totalitaer.de/Waffen/englelectmagfieldlife.htm
11
Mercola Take Control of Your Health [homepage on the Internet].How Cell phone Radiation Affects Your Cells, [update 2008 March 18] Available from: http://articles.mercola.com/sites/articles/archive/2008/03/18/how-cellphone-radiation-affects-your-cells.aspx
12
Somayeh Arian Rad, Kaleem Ahmed Jaleeli, Adeel Ahmad. Influence of electromagnetic radiation produced by mobile phone on viscosity of human blood. International Journal of Science, Environment.2015 December 2; 4(6):1469 – 1475
13
Fatemeh Zakieh Tohidi, Reza Fardid, Somayeh Arian Rad, Mohadeseh Tohidi, Mohammad Hosein Bahrayni Toosi, Tayebeh Kianosh. The Effect of Cell phone Radiation on Hematological Blood Cell Factors In BALB/C Mice. Iranian Journal of Medical Physics.2016 April 30; 13(1): 58-64.
14
.Somayeh Arian Rad, Adeel Ahmad. A Study on Effects of Electromagnetic Radiation Produced by Mobile Phone on Size and Shape of Red Blood Cells of Human Blood Using Laser Diffraction Technique. International Journal of Scientific Engineering and Technology. 2017 March 1; 6(3): 126-127.
15
Somayeh Arian Rad, Adeel Ahmad. Effect of mobile phone radiation on tympanic temperature. International journal of engineering sciences & research technology.2017 April 15; 6(4): 92-95.
16
Mohammed Gulam Ahamad, Abdulaziz Almazyad, Adeel Ahmad, Sayeed Ahmad.Analysis of Human Blood and Plasma Viscosity in Excel. The Indian Journal of Bio Research. 2009; 74(1) : 74-81.
17
N. Manohar Reddy1, D.Kothandan, S. Chandra Lingam and Adeel Ahmad. Hemorheological parameters of Blood of patients suffering from pulmonary Tuberculosis.Pelagia Research Library Eur. J. Exp. Biol. 2012; 2 (1): 135-141.
18
Abu Bakr El-Bediwi & Mohamed Saad . Influence of Electromagnetic Radiation Produced by Mobile Phone on Some Biophysical Blood Properties in Rats. Cell Biochem Biophys. 2013;65(3): 297-300.
19
L. Tazrout & K. Talea. Effects of Chronic Exposure to Electromagnetic Waves at 930 MHz Frequency on OxidativeStress in Plasma and Some Organs of Wistar Rats, Indian Journal of Research. 2015;(3):157-163.
20
ORIGINAL_ARTICLE
Assessment of Patient Radiation Dose in Interventional Procedures at Shahid Madani Heart Center in Khorramabad, Iran
Introduction: Coronary angiography is the most common angiographic procedure for diagnosis and treatment of the heart diseases. Herein, we aimed to evaluate the entrance surface dose (ESD), dose area product (DAP), as well as cancer risk in interventional cardiology procedures. Materials and Methods: This study was conducted during July-December 2015 at Shahid Madani Heart Center in Khorramabad, Iran. A total of 225 adult patients including 122 females and 103 males regardless of the risk factors for coronary diseases were participated. Of them, 199 and 26 patients underwent diagnostic coronary angiography (CA) and percutaneous transluminal coronary angioplasty (PTCA), respectively. Each patient underwent CA or PTCA separately. All the procedures were carried out using Siemens angiography system with the pulsed fluoroscopy of 10-30 pulses/s and cine frame rate of 15 frames/s. DAP, ESD, fluoroscopy time (FT), as well as the number of sequences and frames per sequence were collected for each 199 CA and 26 PTCA procedures. Results: The median values of DAP were 19.77±14.88 and 57.11±33.36 Gy.cm2 in CA and PTCA, respectively. In addition, the median values of ESD were 323.12±245.39 and 1145.22±594.42 mGy in CA and PTCA, respectively. FTs were 114.59±74.33 s in CA and 424.15±292.93 s in PTCA. Conclusion: The average patient dose and cancer risk estimates in both CA and PTCA were consistent with the reference levels. However, in agreement with other interventional procedures, dose levels in the interventional cardiology are influenced by staff and clinical protocols, as well as the type of equipment.
https://ijmp.mums.ac.ir/article_8836_080f675bc4f0eef2341285f991db8d40.pdf
2017-09-01
128
134
10.22038/ijmp.2017.22500.1212
Coronary Angiography Entrance Surface Dose
Dose Area Product
Effective Dose
Radiation risk
Mehrdad
Gholami
mhrgh@yahoo.com
1
Department of Medical Physics & Radiation Sciences, Lorestan University of Medical Sciences, Khorram Abad, Iran
LEAD_AUTHOR
Somayeh
gharloghi
s.gharloghi@yahoo.com
2
Department of Medical physics and Radiation Sciences, School of Para Medicine, Lorestan University of Medical Sciences, Khorramabad, Iran
AUTHOR
Soodabeh
Zare
soodabeh.zare@yahoo.com
3
Department of Public Health, Lorestan University of Medical Sciences, Khorramabad, Iran
AUTHOR
Arezoo
Saki
arezoosaki71@yahoo.com
4
Student Research Committee, Lorestan University of Medical Sciences, Khorramabad, Iran
AUTHOR
Zahra
Piri
zahra_piri74@yahoo.com
5
Student Research Committee, Lorestan University of Medical Sciences, Khorramabad, Iran
AUTHOR
Majid
Mousavi
mosavy_majid@yahoo.com
6
Angiography and Angioplasty section, Shahid Madani Heart Center, Lorestan University of Medical Sciences, Khorramabad, Iran
AUTHOR
Gray B, Klimis H, Inam S, Ariyathna N, Kumar S, Bailey B, et al. Radiation Exposure During Cardiac Catheterisation is Similar for Both Femoral and Radial Approaches. Heart, Lung and Circulation. 2015; 24: 264–9. DOI: 10.1016/j.hlc.2014.09.022.
1
Gerber T.C, Gibbons R.J. Weighing the Risks and Benefits of Cardiac Imaging with Ionizing Radiation. J A C C: Cardiovascular Imaging. 2010; 3(5): 528 – 35. DOI: 10.1016/j.jcmg.2010.03.003.
2
Bor D, Sancak T, Olgar T, Elcim Y, Adanali A, Sanlidilek U ,et al. Comparison of effective doses obtained from dose–area product and air kerma measurements in interventional radiology. Br J Radiol. 2004; 77(916): 315–22. DOI: 10.1259/bjr/29942833.
3
Tavakoli M.B, Monsef S, Hashemi M, Emami H. Assessment of patients skin dose undergoing coronary angiography and Percutaneous Transluminal Coronary Angioplasty (PTCA). Iran J Radiat Res. 2010; 8 (3): 155-60.
4
Stratis A.I, Anthopoulos P.L, Gavaliatsis I.P, Ifantis G.P, Salahas A.I, Antonellis I.P, et al. Patient Dose in Cardiac Radiology. Hellenic J Cardiol. 2009; 50(1):17-25.
5
Giordano C, D’Ercole L, Gobbi R, Bocchiola M, Passerini F. Coronary angiography and percutaneous transluminal coronary angioplasty procedures: Evaluation of patients’ maximum skin dose using Gafchromic films and a comparison of local levels with reference levels proposed in the literature. Physica Medica. 2010; 26: 224-32. DOI: 10.1016/j.ejmp.2010.01.001.
6
Dogan Bor, Turan Olğar, Türkay Toklu, Ayça C¸ağlan, Elif önal, Renato Padovani. Patient doses and dosimetric evaluations in interventional cardiology. Physica Medica. 2009; 25(1):31-42. DOI: 10.1016/j.ejmp.2008.03.002.
7
Castles AV, ul Haq MA, Barlis P, Ponnuthurai FA, Lim CC, Mehta N, et al.. Radiation Exposure with the Radial Approach for Diagnostic Coronary Angiography in a Centre Previously Performing Purely the Femoral Approach. Heart Lung and Circulation. 2014; 23: 751–7. DOI: 10.1016/j.hlc.2014.02.019.
8
Gray B, Klimis H, Inam Sh, Ariyathna N, Kumar Sh, Bailey B, et al. Radiation Exposure during cardiac catheterization is similar for both femoral and radial approaches. Heart Lung and Circulation. 2016; 24: 264–9. DOI: 10.1016/j.hlc.2014.09.022.
9
Wrixon A D. New ICRP Recommendations. J Radiol Prot. 2008; 28(2):161-8.
10
Chida K, Saito H, Otani H, Kohzuki M, Takahashi Sh, Yamada Sh, et al. Relationship Between Fluoroscopic Time, Dose–Area Product, Body Weight, and Maximum Radiation Skin Dose in Cardiac Interventional Procedures. A J R. 2006; 186: 774–8. DOI: 10.2214/AJR.04.1653.
11
Sources and effects of ionizing radiation. (UNSCEAR)United Nations. New York ;2010, 1. 53 p
12
Smith I.R, and Rivers J.T. Measures of Radiation Exposure in Cardiac Imaging and the Impact of Case Complexity. Heart Lung and Circulation. 2008; 17: 224–31. DOI: 10.1016/j.hlc.2007.10.004.
13
Khelassi-Toutaoui N, Toutaoui A, Merad A, Sakhri-Brahimi Z, Baggoura B, Mansouri B. Assessment of radiation protection of patients and staff in interventional procedures in four algerian hospitals. Radiat Prot Dosimetry. 2016 ;168(1):55-60. DOI: 10.1093/rpd/ncv001.
14
Bahreyni Toossi MT, Baradaran SF, Gholoobi A, Nademi H.. Evaluation of Maximum Patient Skin Dose Arising from Interventional Cardiology Using Thermoluminescence Dosimeter in Mashhad, Iran. Iranian Journal of Medical Physics. 2013; 10(3): 87-94. DOI: 10.22038/ijmp.2013.2176.
15
Padovani R, Vano E, Trianni A, Bokou C, Bosmans H, Bor D, et al. Reference levels at European level for cardiac interventional procedures. Radiat Prot Dosimetry. 2008; 129: 104-7. DOI: 10.1093/rpd/ncn039.
16
Bar O, Maccia C, Pages P. A multicenter survey of patient exposure to ionising radiation during interventional cardiology procedures in France. EuroIntervention. 2008;3(5):593-9. DOI: 10.4244/EIJV3I5A107.
17
ORIGINAL_ARTICLE
Finite Element Analysis of Tissue Conductivity during High-frequency and Low-voltage Irreversible Electroporation
Introduction: Irreversible electroporation (IRE) is a process in which the membrane of the cancer cells are irreversibly damaged with the use of high-intensity electric pulses, which in turn leads to cell death. The IRE is a non-thermal way to ablate the cancer cells. This process relies on the distribution of the electric field, which affects the pulse amplitude, width, and electrical conductivity of the tissues. The present study aimed to investigate the relationship of the pulse width and intensity with the conductivity changes during the IRE using simulation.
Materials and Methods: For the purpose of the study, the COMSOL 5 software was utilized to predict the conductivity changes during the IRE. We used 4,000 bipolar and monopolar pulses with the frequency of 5 kHz and 1 Hz, width of 100 µs, and electric fields of low and high intensity. Subsequently, we built three-dimensional numerical models for the liver tissue.
Results: The results of our study revealed that the conductivity of tissue increased during the application of electrical pulses. Additionally, the conductivity changes increased with the elevation of the electric field intensity.
Conclusion: As the finding of this study indicated, the IRE with high-frequency and low electric field intensity could change the tissue conductivity. Therefore, the IRE was recommended to be applied with high frequency and low voltage.
https://ijmp.mums.ac.ir/article_8801_d681bfb9c64a680b4e84d27e77f879a0.pdf
2017-09-01
135
140
10.22038/ijmp.2017.22116.1208
High frequency
Irreversible
Electroporation
Low voltage
Electric Conductivity
amir
khorasani
amir69k@yahoo.com
1
Dept. of Medical Physics, School of Medical Sciences, Tarbiat Modares University, Tehran, Iran
AUTHOR
Seyed Mohamad
Firoozabadi
pourmir@modares.ac.ir
2
Department of Medical Physics, School of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
LEAD_AUTHOR
zeinab
shankayi
z.shankayi@gmail.com
3
Dept. of Medical Physics, School of Medical Sciences, Tarbiat Modares University, Tehran, Iran
AUTHOR
Neumann E, Schaefer-Ridder M, Wang Y, Hofschneider PH. Gene transfer into mouse lyoma cells by electroporation in high electric fields. EMBO J. 1982;1(7):841.
1
DeBruin KA, Krassowska W. Modeling electroporation in a single cell. I. Effects of fielstrength and rest potential. Biophys. J. 1999 Sep 30;77(3):1213-24. DOI: 10.1016/S0006-3495(99)76973-0.
2
Adeyanju O, Al-Angari H, Sahakian A. The optimization of needle electrode number and placement for IRE of hepatocellular carcinoma. Radiol Oncol. 2012 Jun 1;46(2):126-35. DOI: 10.2478/v10019-012-0026-y.
3
Miklavčič D, Beravs K, Šemrov D, Čemažar M, Demšar F, Serša G. The importance of electric field distribution for effective in vivo electroporation of tissues. Biophys. J. 1998 May 31;74(5):2152-8. DOI: 10.1016/S0006-3495(98)77924-X.
4
Lu DS, Kee ST, Lee EW. IRE: ready for prime time? Tech Vasc Interv Radiol. 2013;16(4):277-86. DOI: 10.1053/j.tvir.2013.08.010.
5
Mir LM. Therapeutic perspectives of in vivo cell electropermeabilization. Bioelectrochemistry. 2001 Jan 1;53(1):1-0. DOI: 10.1016/S0302-4598(00)00112-4.
6
Arena CB, Sano MB, Rossmeisl JH, Caldwell JL, Garcia PA, Rylander MN, et al. High-frequency IRE (H-FIRE) for non-thermal ablation without muscle contraction. Biomed Eng Online. 2011;10(1):102. DOI: 10.1186/1475-925X-10-102.
7
Reilly JP, Freeman VT, Larkin WD. Sensory effects of transient electrical stimulation-evaluation with a neuroelectric model. IEEE Trans. Biomed. Eng. 1985 Dec(12):1001-11. DOI: 10.1109/TBME.1985.325509.
8
Miklavcic D, Pucihar G, Pavlovec M, Ribaric S, Mali M, Macek-Lebar A, et al. The effect of high frequency electric pulses on muscle contractions and antitumor efficiency in vivo for a potential use in clinical electrochemotherapy. Bioelectrochemistry. 2005;65(2):121-8. DOI: 10.1016/j.bioelechem.2004.07.004.
9
Corovic S, Zupanic A, Miklavcic D. Numerical modeling and optimization of electric field distribution in subcutaneous tumor treated with electrochemotherapy using needle electrodes. IEEE Trans. Plasma Sci. 2008 Aug;36(4):1665-72. DOI: 10.1109/TPS.2008.2000996.
10
Dunki-Jacobs EM, Philips P, Martin RC. Evaluation of resistance as a measure of successful tumor ablation during IRE of the pancreas. J. Am. Coll. Surg. 2014 Feb 28;218(2):179-87. DOI: 10.1016/j.jamcollsurg.2013.10.013.
11
Moisescu MG, Radu M, Kovacs E, Mir LM, Savopol T. Changes of cell electrical parameters induced by electroporation. A dielectrophoresis study. Biochim. Biophys. Acta. 2013 Feb 28;1828(2):365-72. DOI: 10.1016/j.bbamem.2012.08.030.
12
Kranjc M, Bajd F, Serša I, Miklavčič D. Magnetic resonance electrical impedance tomography for measuring electrical conductivity during electroporation. Physiol Meas. 2014 May 20;35(6):985.
13
Ivorra A, Rubinsky B. In vivo electrical impedance measurements during and after electroporation of rat liver. Bioelectrochemistry. 2007 May 31;70(2):287-95. DOI: 10.1016/j.bioelechem.2006.10.005.
14
Pavlin M, Kandušer M, Reberšek M, Pucihar G, Hart FX, Magjarevićcacute R, et al. Effect of cell electroporation on the conductivity of a cell suspension. Biophys. J. 2005 Jun 30;88(6):4378-90. DOI: 10.1529/biophysj.104.048975.
15
Cukjati D, Batiuskaite D, André F, Miklavčič D, Mir LM. Real time electroporation control for accurate and safe in vivo non-viral gene therapy. Bioelectrochemistry. 2007 May 31;70(2):501-7. DOI: 10.1016/j.bioelechem.2006.11.001.
16
Glahder J, Norrild B, Persson MB, Persson BR. Transfection of HeLa‐cells with pEGFP plasmid by impedance power‐assisted electroporation. Biotechnol. Bioeng. 2005 Nov 5;92(3):267-76. DOI: 10.1002/bit.20426.
17
Garcia PA, Rossmeisl Jr JH, Neal II RE, Ellis TL, Olson JD, Henao-Guerrero N, et al. Intracranial nonthermal IRE: in vivo analysis. J. Membr. Biol. 2010 Jul 1;236(1):127-36. DOI: 10.1007/s00s232-010-9284-z.
18
Sano MB, Neal RE, Garcia PA, Gerber D, Robertson J, Davalos RV. Towards the creation of decellularized organ constructs using IRE and active mechanical perfusion. Biomed Eng Online. 2010 Dec 10;9(1):83. DOI: 10.1186/1475-925X-9-83.
19
Garcia PA, Davalos RV, Miklavcic D. A numerical investigation of the electric and thermal cell kill distributions in electroporation-based therapies in tissue. PloS one. 2014 Aug 12;9(8):e103083. DOI: 10.1371/journal.pone.0103083.
20
Shankayi Z, Firoozabadi SM, Hassan ZS. Optimization of Electric Pulse Amplitude and Frequency In Vitro for Low Voltage and High Frequency Electrochemotherapy. The Journal of membrane biology. 2014 Feb 1;247(2):147-54. DOI: 10.1007/s00232-013-9617-9.
21
Shankayi Z, Saraf Hassan Z. Comparison of low voltage amplitude electrochemotherapy with 1 Hz and 5 kHz frequency in volume reduction of mouse mammary tumor in Balb/c Mice. Koomesh. 2012 Jun 15;13(4):486-90.
22
Shankayi Z, Firoozabadi SM, Saraf HZ. The Endothelial Permeability Increased by Low Voltage and High Frequency Electroporation. Journal of biomedical physics & engineering. 2013 Sep;3(3):87.
23
Bilska AO, DeBruin KA, Krassowska W. Theoretical modeling of the effects of shock duration, frequency, and strength on the degree of electroporation. Bioelectrochemistry. 2000 Jun 30;51(2):133-43. DOI: 10.1016/S0302-4598(00)00066-0.
24
ORIGINAL_ARTICLE
Decision Support System for Age-Related Macular Degeneration Using Convolutional Neural Networks
Introduction: Age-related macular degeneration (AMD) is one of the major causes of visual loss among the elderly. It causes degeneration of cells in the macula. Early diagnosis can be helpful in preventing blindness. Drusen are the initial symptoms of AMD. Since drusen have a wide variety, locating them in screening images is difficult and time-consuming. An automated digital fundus photography-based screening system help overcome such drawbacks. The main objective of this study was to suggest a novel method to classify AMD and normal retinal fundus images. Materials and Methods: The suggested system was developed using convolutional neural networks. Several methods were adopted for increasing data such as horizontal reflection, random crop, as well as transfer and combination of such methods. The suggested system was evaluated using images obtained from STARE database and a local dataset. Results: The local dataset contained 3195 images (2070 images of AMD suspects and 1125 images of healthy retina) and the STARE dataset comprised of 201 images (105 images of AMD suspects and 96 images of healthy retina). According to the results, the accuracies of the local and standard datasets were 0.95 and 0.81, respectively. Conclusion: Diagnosis and screening of AMD is a time-consuming task for specialists. To overcome this limitation, we attempted to design an intelligent decision support system for the diagnosis of AMD fundus using retina images. The proposed system is an important step toward providing a reliable tool for supervising patients. Early diagnosis of AMD can lead to timely access to treatment.
https://ijmp.mums.ac.ir/article_8531_56c7ad097eb7b1d9fcc4160dbc1af202.pdf
2017-09-01
141
148
10.22038/ijmp.2017.21597.1205
Age-Related Macular-Degeneration Convolutional Neural
Networks Drusen Fundus Photography
Mostafa
Langarizadeh
langarizadeh2001@yahoo.com
1
Department of Health Information Management, School of Health Management and Information Science, Iran University of Medical Sciences, Tehran, Iran.
AUTHOR
Banafshe
Maghsoudi
maghsoudi.banafshe@gmail.com
2
Department of Health Information Management, School of Health Management and Information Science, Iran University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
Naveed
Nilforushan
naveednil@yahoo.com
3
Iran University of Medical Sciences / Rassoul Akram Hospital, Department Of Ophthalmology, Glaucoma division
AUTHOR
Facts about age-related macular degeneration. [Internet]. 2015 [cited 2015 28 October]; Available from:https://nei.nih.gov/health/maculardegen/armd_facts.
1
Gheorghe A, Mahdi L,Musat O. Age-related macular degeneration. Romanian Journal of Ophthalmolog. 2015;59(2):74-7.
2
Jong D,Paulus T. Age-related macular degeneration. New England Journal of Medicine. 2006;355(14):1474-85.
3
Editore S. Age-related macular degeneration (armd): SICS; 2014
4
Hijazi MHA, Coenen F,Zheng Y. Data mining techniques for the screening of age-related macular degeneration. Knowledge-Based Systems. 2012;29:83-92.
5
Freund DE, Bressler N,Burlina P. Automated detection of drusen in the macula. Biomedical Imaging: From Nano to Macro, 2009 ISBI'09 IEEE International Symposium on; 2009: IEEE.
6
Ravudu M, Jain V,Kunda MMR. Review of image processing techniques for automatic detection of eye diseases. Sensing Technology (ICST), 2012 Sixth International Conference on; 2012: IEEE.
7
Liu H, Xu Y, Wong DW, Laude A, Lim TH,Liu J. Achiko-d350: A dataset for early amd detection and drusen segmentation. 2014.
8
Langarizadeh M, Mahmud R, Ramli A, Napis S, Beikzadeh M,Rahman WWA. Effects of enhancement methods on diagnostic quality of digital mammogram images. Iranian Journal of Cancer Prevention. 2012;3(1):36-41.
9
Karami M, Fatehi M, Torabi M, Langarizadeh M, Rahimi A,Safdari R. Enhance hospital performance from intellectual capital to business intelligence. Radiol Manage. 2013;35(6):30-5.
10
Riazi H, Larijani B, Langarizadeh M,Shahmoradi L. Managing diabetes mellitus using information technology: A systematic review. Journal of Diabetes & Metabolic Disorders. 2015;14(1):1.
11
Safdari R, Kadivar M, Langarizadeh M, Nejad AF,Kermani F. Developing a fuzzy expert system to predict the risk of neonatal death. Acta Informatica Medica. 2016;24(1):34.
12
Bayani A, Langarizadeh M, Shahmoradi L, Radmard AR,Nejad AF. Quality improvement of liver ultrasound images using fuzzy techniques. Acta Informatica Medica. 2016;24(6):380.
13
Langarizadeh M. Detection of masses and microcalcifications in digital mammogram images using fuzzy logic. Asian Biomed. 2016:345.
14
Khalid S, Pakistan MA,Ikram MU. Review of image processing techniques for detection of agerelated macoular degeneration. Proceedings of the 2015 International Conference on Operations Excellence and Service Engineering; 2015; Orlando, Florida, USA.
15
Langarizadeh M,Mahmud R. Breast density classification using histogram-based features. Iranian Journal of Medical Informatics. 2012;1(1).
16
Acharya UR, Mookiah MRK, Koh JE, Tan JH, Noronha K, Bhandary SV, et al. Novel risk index for the identification of age-related macular degeneration using radon transform and dwt features. Computers in biology and medicine. 2016;73:131-40.
17
Cheng J, Wong DWK, Cheng X, Liu J, Tan NM, Bhargava M, et al. Early age-related macular degeneration detection by focal biologically inspired feature. 2012 19th IEEE International Conference on Image Processing; 2012: IEEE.
18
Mookiah MRK, Acharya UR, Koh JE, Chandran V, Chua CK, Tan JH, et al. Automated diagnosis of age-related macular degeneration using greyscale features from digital fundus images. Computers in biology and medicine. 2014;53:55-64.
19
Mookiah MRK, Acharya UR, Koh JE, Chua CK, Tan JH, Chandran V, et al. Decision support system for age-related macular degeneration using discrete wavelet transform. Medical & biological engineering & computing. 2014;52(9):781-96.
20
Phan TV, Seoud L, Chakor H,Cheriet F. Automatic screening and grading of age-related macular degeneration from texture analysis of fundus images. Journal of Ophthalmology. 2016;2016:11.
21
Waseem S, Akram MU,Ahmed BA. Drusen detection from colored fundus images for diagnosis of age related macular degeneration. 7th International Conference on Information and Automation for Sustainability; 2014 22-24 Dec. 2014.
22
Garnier M, Hurtut T, Tahar HB,Cheriet F. Automatic multiresolution age-related macular degeneration detection from fundus images. SPIE Medical Imaging; 2014: International Society for Optics and Photonics.
23
Prasath AR,Ramya M. Detection of macular drusen based on texture descriptors. Research Journal of Information Technology. 2015;7(1):70-9.
24
Mookiah MRK, Acharya UR, Fujita H, Koh JE, Tan JH, Chua CK, et al. Automated detection of age-related macular degeneration using empirical mode decomposition. Knowledge-Based Systems. 2015;89:654-68.
25
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59
ORIGINAL_ARTICLE
Evaluation of Effect of Different Computed Tomography Scanning Protocols on Hounsfield Unit and Its Impact on Dose Calculation by Treatment Planning System
Introduction: In radiotherapy treatment planning system (TPS), basic input is the data from computed tomography (CT) scan, which takes into account the effect of inhomogeneities in dose calculations. Measurement of CT numbers may be affected by scanner-specific parameters. Therefore, it is important to verify the effect of different CT scanning protocols on Hounsfield unit (HU) and its impact on dose calculation. This study was carried out to analyse the effect of different tube voltages on HU for various tissue substitutes in phantom and their dosimetric impact on dose calculation in TPS due to variation in HU–relative electron density (RED) calibration curves. Materials and Methods: HU for different density materials was obtained from CT images of the phantom acquired at various tube voltages. HU-RED calibration curves were drawn from CT images with various tissue substitutes acquired at different tube voltages used to quantify the error in dose calculation for different algorithms. Doses were calculated on CT images acquired at 120 kVp and by applying CT number to RED curve obtained from 80, 100, 120, and 140 kVp voltages. Results: No significant variation was observed in HU of different density materials for various kVp values. Doses calculated with applying different HU-RED calibration curves were well within 1%. Conclusion: Variation in doses calculated by algorithms with various HU-RED calibration curves was found to be well within 1%. Therefore, it can be concluded that clinical practice of using the standard HU-RED calibration curve by a 120 kVp CT acquisition technique is viable.
https://ijmp.mums.ac.ir/article_8663_92b5bf323e151091d3b5d88e95fc34cb.pdf
2017-09-01
149
154
10.22038/ijmp.2017.21942.1207
Computed Tomography Hounsfield Units
Phantom
Treatment Planning System
Mamta
Mahur
mamtamahur@gmail.com
1
Department of Radiotherapy, Delhi State Cancer Institute(s), Dilshad Garden, Delhi – 110095, India
AUTHOR
Om Prakash
Gurjar
ominbarc@gmail.com
2
Roentgen-SAIMS Radiation Oncology Centre, Sri Aurobindo Institute of Medical Sciences
LEAD_AUTHOR
RK
GROVER
rosindoreresearch@gmail.com
3
Department of Radiotherapy, Delhi State Cancer Institute(s), Dilshad Garden, Delhi – 110095, India
AUTHOR
PS
Negi
prit2negi@yahoo.co.in
4
Department of Radiotherapy, Delhi State Cancer Institute(s), Dilshad Garden, Delhi – 110095, India
AUTHOR
Richa
Sharma
richa035@gmail.com
5
Department of Radiotherapy, Delhi State Cancer Institute(s), Dilshad Garden, Delhi – 110095, India
AUTHOR
Anshu
Singh
anshu.rajput25@gmail.com
6
Department of Radiotherapy, Delhi State Cancer Institute(s), Dilshad Garden, Delhi – 110095, India
AUTHOR
Munendra
Singh
drmunendra1974phy@gmail.com
7
Department of Physics, School of Basic Sciences & Research, Sharda University, Greater Noida, Uttar Pradesh – 201306, India
AUTHOR
Watanabe Y. Derivation of linear attenuation coefficients from CT numbers for low energy photons. Phys Med Biol. 1999; 44:2201-11.
1
Das IJ, Cheng CW, Cao M, Johnstone PAS. Computed tomography imaging parameters for inhomogeneities correction in radiation treatment planning. J Med Phys. 2016;41:3-11. DOI:10.4103/0971-6203.177277.
2
Roa AMA, Andersen HK, Martinsen AC. CT image quality over time: comparison of image quality for six different CT scanners over a six-year period. J Appl Clin Med Phys. 2015;16: 4972. DOI: 10.1120/jacmp.v16i2.4972.
3
Constantinou C, Harrington JC, DeWerd LA. An electron density calibration phantom for CT-based treatment planning computers. Med Phys. 1992;19:325–7. DOI: 10.1118/1.596862.
4
Guan H, Yin FF, Kim JH. Accuracy of inhomogeneity correction in photon radiotherapy from CT scans with different settings. Phys Med Biol. 2002;47:223–31.
5
Moyers MF, Miller DW, Siebers JV, Galindo R, Sun S, Sardesai S. Water equivalence of various materials for 155 to 250 MeV protons. Med Phys.1992;19: 829.
6
Nobah A, Moftah B, Tomic N, Devic A. Influence of electron density spatial distribution and x-ray beam quality during CT simulation on dose calculation accuracy. J Appl Clin Med Phys. 2011;12: 3432. DOI: 10.1120/jacmp.v12i3.3432.
7
Goodenough DJ. Catphan 500 and 600 manual. Greenwich, NY: The Phantom Laboratory; 2012.
8
Sande EP, Martinsen AC, Hole EO, Olerud HM. Inter phantom and inter scanner variations for Hounsfield units-establishment of reference values for HU in a commercial QA phantom. Phys Med Biol. 2010;55: 5123-35. DOI: 10.1088/0031-9155/55/17/015.
9
Cozzi L, Fogliata A, Buffa F, Bieri S. Dosimetric impact of computed tomography calibration on a commercial treatment planning system for external radiation therapy. Radiother Oncol. 1998;48: 335–8. DOI: 10.1016/S0167-8140(98)00072-3.
10
Sharma DS, Sharma SD, Sanu KK, Saju S, Deshpande DD, Kannan S , Performance evaluation of a dedicated computed tomography scanner used for virtual simulation using inhouse fabricated CT phantoms. J Med Phys 2006;31:28–35.
11
Cropp RJ, Seslija P, Tso D, Thakur Yogesh. Scanner and kVp dependence of measured CT numbers in the ACR CT phantom. J Appl Clin Med Phys 2013;14:4417.
12
Maria AR, Anderson HK, Martinsen AC. CT image quality over time: comparison of image quality for six different CT scanners over a six-year period. J Appl Clin Med Phys 2015;16:4972.
13
Gulliksurd K, Stokke C, Martinsen AC. How to measure CT image quality: Variations in CT-numbers, uniformity and low contrast resolution for a CT quality assurance phantom. Phys Med 2014;30:521-6.
14
ORIGINAL_ARTICLE
Dose Evaluation for Common Digital Radiographic Examinations in Selected Hospitals in Pahang Malaysia
Introduction: In digital radiography, radiographers tend to increase exposure factors to acquire an acceptable image quality thereby increasing radiation dose to patients. Regarding this, the present study aimed to re-evaluate the exposure parameters and to ascertain the entrance surface dose (ESD) and effective dose (ED) of posterior-anterior (PA) chest, abdomen, and anterior-posterior (AP) lumbosacral spine radiography.
Materials and Methods: This study was conducted on 180 physically able patients with age of 20-60 years and weight of 60-80 kg referred to Hospital Sultan Haji Ahmad Shah (HOSHAS) and Hospital Tengku Ampuan Afzan (HTAA).Image acquisition was performed using digital radiography. The ESD and ED were determined using CALDose_X 5.0 software.
Results: The ESD and ED for PA chest were 0.098 mGy and 0.012 mSv in HOSHAS, while in HTAA were 0.161 mGy and 0.021 mSv respectively. Regarding the abdomen, the ESD and ED were 2.57 mGy and 0.311 mSv in HOSHAS and 2.16 mGy and 0.262 mSv in HTAA respectively. For AP lumbosacral spine, the ESD and ED for HOSHAS were 2.65 mGy and 0.222 mSv, while in HTAA were 2.357 mGy and 0.201 mSv respectively.
Conclusion: The findings revealed the use of high kVp, automatic exposure control, correct focus image receptor distance, tight collimation and additional filter resulted in a lower ESD. The ESD and ED obtained in this study were comparable with those reported by other studies and lower than the values recommended by the United Nations Scientific Committee on the Effects of Atomic Radiation in 2008.
https://ijmp.mums.ac.ir/article_8835_e62fb201582efe42733fd9b00eb1b147.pdf
2017-09-01
155
161
10.22038/ijmp.2017.22744.1220
Digital Radiography
Radiation Dosage
Radiography Thoracic
Radiography Abdominal
Radiation Protection
Soo-Foon
Moey
moeysf@iium.edu.my
1
Department of Diagnostic Imaging and Radiotherapy, Kulliyyah (Faculty) of Allied Health Sciences, International Islamic University Malaysia, Kuantan Campus, 25200 Kuantan, Pahang, Malaysia
LEAD_AUTHOR
Zubir
Shazli
zubirshazli@gmail.com
2
Department of Diagnostic Imaging and Radiotherapy, Kulliyyah (Faculty) of Allied Health Sciences, International Islamic University Malaysia, Kuantan Campus, 25200 Kuantan, Pahang, Malaysia
AUTHOR
Inayatullah Shah
Sayed
inayatullah@iium.edu.my
3
Department of Diagnostic Imaging and Radiotherapy, Kulliyyah (Faculty) of Allied Health Sciences, International Islamic University Malaysia, Kuantan Campus, 25200 Kuantan, Pahang, Malaysia
AUTHOR
Osei EK, Darko J. A survey of organ equivalent and effective doses from diagnostic radiology procedures. ISRN radiology. 2012; 2013: 1-9. DOI: 10.5402/2013/204346.
1
Hoffman EA, Jiang R, Baumhauer H, Brooks MA, Carr JJ, Detrano R, et al. Reproducibility and validity of lung density measures from cardiac CT scans – the multi – Ethnic study of Atherosclerosis (MESA) lung study. Acad Radiol. 2009; 16(6): 689-99. DOI: 10.1016/j.acra.2008.12.024.
2
ICRP, Valentin J. Release of Patients after Therapy with Unsealed Radionuclides. ICRP Publication 94. Ann. ICRP 34 (2). Elsevier; 2004. DOI: 10.1097/00004032-200507000-00010.
3
Aliasgharzadeh A, Mihandoost E, Masoumbeigi M, Salimian M, Mohseni M. Measurement of entrance skin dose and calculation of effective dose for common diagnostic x-ray examination in Kashan, Iran. Global Journal of Health Science. 2015; 7(5): 202-7. DOI: 10.5539/gjhs.v7n5p202.
4
ICRP, 2002. Basic anatomical and physiological data for use in radiological protection: reference values. A report of age- and gender-related differences in the anatomical and physiological characteristics of reference individuals. ICRP Publication 89. Ann ICRP 32(3-4): 5-265.
5
Davies M, McCallum H, Whiter G, Brown J, Helm M. Patient dose audit in diagnostic radiography using custom designed software. Elsevier. 1997; 3(1): 17-25. DOI: 10.1016/S1078-8174(97)80021-1.
6
ICRP, 2007. The 2007 Recommendations of the International Commission on Radiological Protection. ICRP Publication 103. Ann. ICRP 37 (2-4).
7
Abdullah MHRO, Kandaiya S, Lim TH, Chumiran SH. Preliminary study on the trend of patient dose arising from diagnostic x-ray examination in Penang, Malaysia. Journal of Applied Sciences Research. 2010; 6(12): 2257-63.
8
European Commission. European guidelines on quality criteria for diagnostic radiographic images. Luxembourg: European Commission; 1996. EUR 16260 EN.
9
Hart D, Hillier M, Shrimpton P. (HPA CRCE-034) on Doses to patients from radiographic and fluoroscopic X-ray imaging procedures in the UK. Chilton: Health Protection Agency Centre for Radiation, Chemical and Environmental Hazards. 2010.
10
United Nations Scientific Committee on the effects of atomic radiation (Report to the general assembly Vol 11 with scientific annexes C, D, E) on the sources and effects of atomic radiation. New York: Off J UNSEAR. 2008.
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Compagnone G, Pgan L, Baleni MC, Calzolaio FL, Barozzi L, Bergamini C. Patient dose in digital projection radiography. Radiat Prot Dosimetry. 2008; 129(1-3): 135-7. DOI: 10.2349/biij.3.2.e26.
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Lanca L , Silva A. Digital radiography detector: A technical overview. Digital Imaging Systems for Plain Radiography. New York: Springer Science + Business Media; 2013. DOI: 10.1007/978-1-4614-5067-2_2.
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Bor D, Birgul O, Onal U, Olgar T. Investigation of grid performance using simple image quality tests. Journal of Medical Physics. 2016; 41(1): 21-8. DOI: 10.4103/0971-6203.177280.
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Suliman II, AbbasN, Habbani FI. Entrance surface doses to patients undergoing selected diagnostic x-ray examinations in Sudan. Radiation Protection Dosimetry. 2007; 123(2), 209-14. DOI: 10.1093/rpd/ncl137.
15
ORIGINAL_ARTICLE
Detection of Melanoma Skin Cancer by Elastic Scattering Spectra: A Proposed Classification Method
Introduction: There is a strong need for developing clinical technologies and instruments for prompt tissue assessment in a variety of oncological applications as smart methods. Elastic scattering spectroscopy (ESS) is a real-time, noninvasive, point-measurement, optical diagnostic technique for malignancy detection through changes at cellular and subcellular levels, especially important in early diagnosis of invasive skin cancer, melanoma. In fact, this preliminary study was conducted to provide a classification method for analyzing the ESS spectra. Elastic scattering spectra related to the normal skin and melanoma lesions, which were already confirmed pathologically, were provided as input from an ESS database. Materials and Methods: A program was developed in MATLAB based on singular value decomposition and K-means algorithm for classification. Results: Accuracy and sensitivity of the proposed classifying method for normal and melanoma spectra were 87.5% and 80%, respectively. Conclusion: This method can be helpful for classification of melanoma and normal spectra. However, a large body of data and modifications are required to achieve better sensitivity for clinical applications.
https://ijmp.mums.ac.ir/article_8630_7fe006cfca411ebe67cc81f3f320e500.pdf
2017-09-01
162
166
10.22038/ijmp.2017.21367.1203
classification
Early detection
Elastic Scattering Spectroscopy
Melanoma
Afshan
Shirkavand
ashirkavand@alumnus.tums.ac.ir
1
1.Medical physicist, Medical laser research center,ACECR 2. Laser &Plasma Research institute, Shahid Beheshti University
LEAD_AUTHOR
saeed
sarkar
sarkar@sina.tums.ac.ir
2
PhD, Professor of medical physics, Institute of advanced technologies in medicine (IAMT), Research Center for Science and Technology in Medicine (RCSTIM), Tehran University of Medical Sciences
AUTHOR
Leila
Ataie fashtami
ataiefash@gmail.com
3
assistant professor of dermatology, Medical Laser Research Center, Iranian Center for Medical Lasers (ICML), Academic Center for Education, Culture and Research (ACECR)
AUTHOR
Hanieh
Mohammadreza
hanieh1358@yahoo.com
4
MSc of medical physics, Institute of advanced technologies in medicine (IAMT), Research Center for Science and Technology in Medicine (RCSTIM), Tehran University of Medical Sciences,
AUTHOR
Siegel R, Naishadham D, Jemal A. Cancer statistics. 2013; CA Cancer J Clin. 2013 Jan; 63(1):11-30.
1
Murphy BW, Webster RJ, Turlach BA, Clay CJD, Heenan PJ., Sampson DD.toward the discrimination of early melanomafrom common and dysplastic nevus using fiber opticdiffuse reflectance spectroscopy. Journal of Biomedical Optics, 2005; 10:6, 0640201-9.
2
A'Amar OM, Ley RD, Bigio IJ. Comparison between ultraviolet-visible and nearinfraredelastic scattering spectroscopy of chemicallyinduced melanomas in an animal model. Journal of Biomedical Optics, 2004; 9(6), 1320–1326.
3
American Cancer Society. Cancer Facts & Figures 2016. Atlanta: American Cancer Society; 2016.
4
Garbe C, Peris K, Hauschild A, Saiag P, Middleton M, Bastholt L, et.al. Diagnosis and treatment of melanoma. European consensus-based interdisciplinary guideline e Update 2016. European Journal of Cancer, 2016; 63, 201-217.
5
Wang SQ, Setlow R, Berwick M, Polsky D, Marghoob AA, Kopf AW, Bart RS. Ultraviolet A and melanoma: A review. J Am Acad Dermatol, 2001; 44:837-46.
6
Day CL Jr, Mihm MC Jr, Lew RA, Kopf AW, Sober AJ, Fitzpatrick TB. Cutaneous Malignant Melanoma: Prognostic Guidelines for Physicians and Patients. CA-A cancer journal for clinician, 1982; 32: 2.
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Bigio IJ and Mourant JR. Ultraviolet and visible spectroscopies for tissuediagnostics: fluorescence spectroscopy and elastic-scattering spectroscopy. Phys. Med. Biol, 1997; 42803–814.
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9
Lovat LB, Bown SG. Elastic scattering spectroscopy for detection of dysplasiain Barrett’s esophagus”, gastrointestinal endoscopy clinicsof North America: optical biopsy, 14. Amsterdam: Elsevier, 2004; 507–17.
10
Mourant JR, Canpolat M, Brocker C .Light scattering from cells: thecontribution of the nucleus and the effects of proliferative status. J Biomed Opt, 2000; 5:131–7.
11
Zhu Y. Statistical aspects of elastic scattering spectroscopy with applications to cancer diagnosis. PhD thesis, Department of Statistical Science, National Medical Laser Centre, University College London, 2009.
12
Omar E, Current concepts and future of noninvasive procedures for diagnosing oral squamous cell carcinoma - a systematic review, Head & Face Medicine. 2015; 11:6.
13
Upile T, Jerjes W, Johal O, Lew-Gor S, Mahil J, Sudhoff H. A new tool to inform intra-operative decision making in skin cancer treatment: the non-invasive assessment of basal cell carcinoma of the skin using elastic scattering spectroscopy. Head Neck Oncol. 2012 Oct 31; 4(3):74.
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Mourant JR, Hielscher AH, Eick AA. Evidence of intrinsic differences inthe light scattering properties of tumorigenic and nontumorigenic cells. Cancer, 1998; 84:366–74.
15
Sharwani A, Jerjes W, Salih V, Swinson B, Bigio IJ, El-Maaytah M, Hopper C. Assessment of oral premalignancy using elastic scattering spectroscopy, Oral Oncology 42, 2006; 343–349.
16
Upile T, Jerjes W, Radhi H, Mahil J, Rao A, Hopper C. Elastic scattering spectroscopy in assessing skin lesions: An ‘‘in vivo’’ study. Photodiagnosis and Photodynamic Therapy 9, 2012; 132-141.
17
Dhar A, Johnson KS, Novelli MR, Bown SG, Bigio IJ, Lovat LB, Bloom SL. Elastic scattering spectroscopy for the diagnosis of colonic lesions: initial results of a novel optical biopsy technique. Gastrointestinal endoscopy, 2006; 63(2), 257-262.
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Vaswani N and Guo H, Correlated-PCA: Principal Components’ Analysis when Data and Noise are correlated, 30th Conference on Neural Information Processing Systems, NIPS 2016.
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Oblefias1WR., Soriano MN, Saloma CA. “SVD vs. PCA: Comparison of Performance in an Imaging Spectrometer. Science Diliman. 2004; 16:2, 74-78.
20
Golchin E, Maghooli K, Overview of Manifold Learning and Its Application in Medical Data set, International journal of Biomedical Engineering and Science (IJBES), Vol. 1, No. 2, July 2014.
21
Karamizadeh S, Abdullah SM, Manaf AA, Zamani M, Hooman A, An Overview of Principal Component Analysis, Journal of Signal and Information Processing, 2013, 4, 173-175.
22
Kalman D, A Singularly Valuable Decomposition: The SVD of a Matrix, The College Mathematics Journal 27 (1996), 2-23.
23
Petrıcek M, Components in Data Analysis, WDS'10 Proceedings of Contributed Papers, Part I, 82–87, 2010.
24
ORIGINAL_ARTICLE
Evaluation of Tumor Control and Normal Tissue Complication Probability in Head and Neck Cancers with Different Sources of Radiation: A Comparative Study
Introduction: The ultimate goal of radiation treatment planning is to yield a high tumor control probability (TCP) with a low normal tissue complication probability (NTCP). Historically dose volume histogram (DVH) with only volumetric dose distribution was utilized as a popular tool for plan evaluation hence present study aimed to compare the radiobiological effectiveness of the cobalt-60 (Co-60) gamma photon and 6MV X-rays of linear accelerators (Linac) in the radiotherapy of head and neck tumors. Materials and Methods: TCP and NTCP were calculated using DVH through the BIOPLAN software developed by Sanchez-Nieto and Nahum . The treatment planning was performed for all the patients using both treatment modalities (i.e., Co-60 and 6 MV Linac). The TCP was also manually calculated using a mathematical formula proposed by Brenner’s et al. Results: The average TCP calculated by the BIOPLAN for Co-60 and 6 MV X-rays were 44.6% and 60.8%, respectively. Furthermore, the average NTCPs obtained for the organ at risk, namely optic nerve, for Co-60 and 6 MV X-ray were 0.24 % and 0.03 %, respectively. Regarding the spinal cord, the average NTCPs for Co-60 gamma photon and 6 MV X-ray of Linac were 0.05 % and 0.002%, respectively. Conclusion: As the findings of the present study indicated, Co-60 unit could provide comparable TCP along with minimal NTCP, compared to the high-cost technologies of Linac. The design of treatment plans based on the radiobiological parameters facilitated the judicious choice of physical parameters for the achievement of high TCP and low NTCP.
https://ijmp.mums.ac.ir/article_8659_60a11c8aeac6838975efee03fc19575e.pdf
2017-09-01
167
172
10.22038/ijmp.2017.21731.1206
Tumor control probability
Normal tissue complication probability
Dose Volume Histogram
Anoop
Srivastava
anoopsrivastava78@gmail.com
1
Department of Radiation Oncology,
Dr. Ram Manohar Lohia Institute of Medical Sciences,
Vibhuti Khand Gomti Nagar, Lucknow -226010
India
LEAD_AUTHOR
MADHUP
RASTOGI
drmadhup1@gmail.com
2
Department of Radiation Oncology, Dr. Ram Manohar Lohia Institute of Medical Sciences, Vibhuti Khand Gomti Nagar, Lucknow -226010
AUTHOR
SURENDRA
MISHRA
mishrasp05@gmail.com
3
Department of Radiation Oncology, Dr. Ram Manohar Lohia Institute of Medical Sciences, Vibhuti Khand Gomti Nagar, Lucknow -226010
AUTHOR
Deasy JO, Mayo CS, Orton CG.. Treatment planning evaluation and optimization should be biologicaly and not dose /volume based. Med. Phys. 2015; 42(6):2753-6. DOI: 10.1118/1.4916670.
1
Sanchez-Nieto B, Nahum AE.. Bioplan software for the biological evaluation of radiotherapy treatment plans. Medical Dosimetry. 2000; 25(2),:71-6. DOI: 10.1016/S0958-3947(00)00031-5.
2
Brenner DJ. Dose, volume, and tumor-control predictions in radiotherapy. Int. J. Radiat Oncol Biol. Phys. 1993; 26, (1): 171-9. DOI: 10.1016/0360-3016(93)90189-3.
3
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