The influence of tissue composition uncertainty on dose distributions in brachytherapy

Published:February 07, 2018DOI:


      Background and purpose

      Model-based dose calculation algorithms (MBDCAs) have evolved from serving as a research tool into clinical practice in brachytherapy. This study investigates primary sources of tissue elemental compositions used as input to MBDCAs and the impact of their variability on MBDCA-based dosimetry.

      Materials and methods

      Relevant studies were retrieved through PubMed. Minimum dose delivered to 90% of the target (D90), minimum dose delivered to the hottest specified volume for organs at risk (OAR) and mass energy-absorption coefficients ( μ en / ρ ) generated by using EGSnrc “g” user-code were compared to assess the impact of compositional variability.


      Elemental composition for hydrogen, carbon, oxygen and nitrogen are derived from the gross contents of fats, proteins and carbohydrates for any given tissue, the compositions of which are taken from literature dating back to 1940–1950. Heavier elements are derived from studies performed in the 1950–1960. Variability in elemental composition impacts greatly D90 for target tissues and doses to OAR for brachytherapy with low energy sources and less for 192Ir-based brachytherapy. Discrepancies in μ en / ρ are also indicative of dose differences.


      Updated elemental compositions are needed to optimize MBDCA-based dosimetry. Until then, tissue compositions based on gross simplifications in early studies will dominate the uncertainties in tissue heterogeneity.


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        • Nath R.
        • Anderson L.L.
        • Luxton G.
        • et al.
        Dosimetry of interstitial brachytherapy sources: recommendations of the AAPM Radiation Therapy Committee Task Group No. 43.
        Med Phys. 1995; 22: 209-234
        • Rivard M.J.
        • Coursey B.M.
        • DeWerd L.A.
        • et al.
        Update of AAPM Task Group No. 43 Report: a revised AAPM protocol for brachytherapy dose calculations.
        Med Phys. 2004; 31: 633-674
        • Hedtjärn H.
        • Carlsson G.A.
        • Williamson J.F.
        Accelerated Monte Carlo based dose calculations for brachytherapy planning using correlated sampling.
        Phys Med Biol. 2002; 47: 351-376
        • Ahnesjö A.
        Collapsed cone convolution of radiant energy for photon dose calculation in heterogeneous media.
        Med Phys. 1989; 16: 577-592
        • Mikell J.K.
        • Mourtada F.
        Dosimetric impact of an 192Ir brachytherapy source cable length modeled using a grid-based Boltzmann transport equation solver.
        Med Phys. 2010; 37: 4733-4743
        • Vassiliev O.N.
        • Wareing T.A.
        • McGhee J.
        • et al.
        Validation of a new grid-based Boltzmann equation solver for dose calculation in radiotherapy with photon beams.
        Phys Med Biol. 2010; 55: 581-598
        • Rogers D.W.O.
        Fifty years of Monte Carlo simulations for medical physics.
        Phys Med Biol. 2006; 51: R287-R301
        • Beaulieu L.
        • Carlsson Tedgren Å.
        • Carrier J.-F.
        • et al.
        Report of the Task Group 186 on model-based dose calculation methods in brachytherapy beyond the TG-43 formalism: current status and recommendations for clinical implementation.
        Med Phys. 2012; 39: 6208-6236
        • Enger S.A.
        • Landry G.
        • D’Amours M.
        • et al.
        Layered mass geometry: a novel technique to overlay seeds and applicators onto patient geometry in Geant4 brachytherapy simulations.
        Phys Med Biol. 2012; 57: 6269-6277
        • Fonseca G.P.
        • Landry G.
        • White S.
        • et al.
        The use of tetrahedral mesh geometries in Monte Carlo simulation of applicator based brachytherapy dose distributions.
        Phys Med Biol. 2014; 59: 5921-5935
        • ICRU 46
        Photon, electron, proton and neutron interaction data for body tissues.
        International Commission on Radiation Units and Measurements, Bethesda, MD, USA1992
        • Woodard H.Q.
        • White D.R.
        The composition of body tissues.
        Br J Radiol. 1986; 59: 1209-1218
        • International Commission on Radiological Protection
        Task Group on Reference Man. International Commission on Radiological Protection No. 23: report of the Task Group on Reference Man: a report prepared by a task group of Committee 2 of the International Commission on Radiological Protection, adopted by the Commission in October, 19.
        Pergamon Press, Oxford, New York1975
        • Rivard M.J.
        • Beaulieu L.
        • Mourtada F.
        Enhancements to commissioning techniques and quality assurance of brachytherapy treatment planning systems that use model-based dose calculation algorithms.
        Med Phys. 2010; 37: 2645-2658
        • Kawrakow I.
        Accurate condensed history Monte Carlo simulation of electron transport. I. EGSnrc, the new EGS4 version.
        Med Phys. 2000; 27: 485-498
      1. Berger M, Hubbell J. XCOM: photon cross sections on a personal computer. Natl Bur Stand Washington, DC (USA) Cent Radiat Res; 1987:1–28

      2. NIST: X-ray mass attenuation coefficients – Table 4. Available at: [accessed 02.10.17].

        • Furstoss C.
        • Reniers B.
        • Bertrand M.J.
        • et al.
        Monte Carlo study of LDR seed dosimetry with an application in a clinical brachytherapy breast implant.
        Med Phys. 2009; 36: 1848-1858
        • Afsharpour H.
        • Pignol J.-P.
        • Keller B.
        • et al.
        Influence of breast composition and interseed attenuation in dose calculations for post-implant assessment of permanent breast 103Pd seed implant.
        Phys Med Biol. 2010; 55: 4547-4561
        • Landry G.
        • Reniers B.
        • Murrer L.
        • et al.
        Sensitivity of low energy brachytherapy Monte Carlo dose calculations to uncertainties in human tissue composition.
        Med Phys. 2010; 37: 5188-5198
        • White A.
        • Landry G.
        • Fonseca P.
        • et al.
        Comparison of TG-43 and TG-186 in breast irradiation using a low energy electronic brachytherapy source.
        Med Phys. 2014; 41 (061701.1–061701.12)
        • Zourari K.
        • Major T.
        • Herein A.
        • et al.
        A retrospective dosimetric comparison of TG43 and a commercially available MBDCA for an APBI brachytherapy patient cohort.
        Phys Med. 2015; : 669-676
        • Ma Y.
        • Lacroix F.
        • Lavallée M.C.
        • Beaulieu L.
        Validation of the Oncentra Brachy Advanced Collapsed cone Engine for a commercial 192Ir source using heterogeneous geometries.
        Brachytherapy. 2015; 14: 939-952
        • Hofbauer J.
        • Kirisits C.
        • Resch A.
        • et al.
        Impact of heterogeneity-corrected dose calculation using a grid-based Boltzmann solver on breast and cervix cancer brachytherapy.
        J Contemp Brachytherapy. 2016; 2: 143-149
        • Berger D.
        • Kauer-Dorner D.
        • Seitz W.
        • Pötter R.
        • Kirisits C.
        Concepts for critical organ dosimetry in three-dimensional image-based breast brachytherapy.
        Brachytherapy. 2008; 7: 320-326
      3. Varian Medical Systems Inc. BrachyVision-Acuros algorithm reference guide. Palo Alto, CA; West Sussex, United Kingdom; 2009.

        • Miksys N.
        • Cygler J.E.
        • Caudrelier J.M.
        • Thomson R.M.
        Patient-specific Monte Carlo dose calculations for 103Pd breast brachytherapy.
        Phys Med Biol. 2016; 61: 1-25
        • Hammerstein R.G.
        • Miller D.W.
        • White D.R.
        • et al.
        Absorbed radiation dose in mammography.
        Radiology. 1979; 130: 485-491
        • Chibani O.
        • Williamson J.F.
        MCPI (c): a sub-minute Monte Carlo dose calculation engine for prostate implants.
        Med Phys. 2005; 32: 3688-3698
        • Carrier J.-F.
        • Beaulieu L.
        • Therriault-Proulx F.
        • Roy R.
        Impact of interseed attenuation and tissue composition for permanent prostate implants.
        Med Phys. 2006; 33: 595-604
        • Hanada T.
        • Yorozu A.
        • Ohashi T.
        • et al.
        The effect of tissue composition of the prostate on the dose calculation for 125I brachytherapy.
        Kitasato Med J. 2011; 41: 136-144
        • White S.A.
        • Landry G.
        • van Gils F.
        • Verhaegen F.
        • Reniers B.
        Influence of trace elements in human tissue in low-energy photon brachytherapy dosimetry.
        Phys Med Biol. 2012; 57: 3585-3596
        • Mason J.
        • Al-Qaisieh B.
        • Bownes P.
        • Henry A.
        • Thwaites D.
        Investigation of interseed attenuation and tissue composition effects in 125I seed implant prostate brachytherapy.
        Brachytherapy. 2014; 13: 603-610
        • Pope D.J.
        • Cutajar D.L.
        • George S.P.
        • et al.
        The investigation of prostatic calcifications using mu-PIXE analysis and their dosimetric effect in low dose rate brachytherapy treatments using Geant4.
        Phys Med Biol. 2015; 60: 4335-4353
        • Hsu T.H.
        • Lin S.-Y.
        • Lin C.-C.
        • Cheng W.-T.
        Preliminary feasibility study of FTIR microscopic mapping system for the rapid detection of the composited components of prostatic calculi.
        Urol Res. 2011; 39: 165-170
        • Collins Fekete C.A.
        • Plamondon M.
        • Martin A.G.
        • et al.
        Calcifications in low-dose rate prostate seed brachytherapy treatment: post-planning dosimetry and predictive factors.
        Radiother Oncol. 2015; 114: 339-344
        • Miksys N.
        • Vigneault E.
        • Martin A.G.
        • Beaulieu L.
        • Thomson R.M.
        Large-scale retrospective Monte Carlo dosimetric study for permanent implant prostate brachytherapy.
        Int J Radiat Oncol Biol Phys. 2017; 97: 606-615
        • Poon E.
        • Williamson J.F.
        • Vuong T.
        • Verhaegen F.
        Patient-specific Monte Carlo dose calculations for high-dose-rate endorectal brachytherapy with shielded intracavitary applicator.
        Int J Radiat Oncol Biol Phys. 2008; 72: 1259-1266
        • Sutherland J.G.H.
        • Furutani K.M.
        • Garces Y.I.
        • Thomson R.M.
        Model-based dose calculations for 125I lung brachytherapy.
        Med Phys. 2012; 39: 4365-4377
        • Sutherland J.G.H.
        • Furutani K.M.
        • Thomson R.M.
        A Monte Carlo investigation of lung brachytherapy treatment planning.
        Phys Med Biol. 2013; 58: 4763-4780
        • Sutherland J.G.H.
        • Furutani K.M.
        • Thomson R.M.
        Monte Carlo calculated doses to treatment volumes and organs at risk for permanent implant lung brachytherapy.
        Phys Med Biol. 2013; 58: 7061-7080
        • Siebert F.A.
        • Wolf S.
        • Kóvacs G.
        Head and neck 192Ir HDR-brachytherapy dosimetry using a grid-based Boltzmann solver.
        J Contemp Brachytherapy. 2013; 5: 232-235
        • Chibani O.
        • Ma C.-M.
        HDRMC, an accelerated Monte Carlo dose calculator for high dose rate brachytherapy with CT-compatible applicators.
        Med Phys. 2014; 41 (051712.1–051712.7)
        • Hadad K.
        • Zohrevand M.
        • Faghihi R.
        • Sedighi Pashaki A.
        Accuracy evaluation of Oncentra™ TPS in HDR brachytherapy of nasopharynx cancer using EGSnrc Monte Carlo code.
        J Biomed Phys Eng. 2015; 5: 25-30
        • Kawrakow I.
        • Matthias F.
        • Friedrich K.
        3D electron dose calculation using a Voxel based Monte Carlo algorithm (VMC).
        Med Phys. 1996; 23: 445-457
        • Peppa V.
        • Pappas E.
        • Major T.
        • et al.
        On the impact of improved dosimetric accuracy on head and neck high dose rate brachytherapy.
        Radiother Oncol. 2016; 120: 92-97
        • Pantelis E.
        • Peppa V.
        • Lahanas V.
        • Pappas E.
        • Papagiannis P.
        BrachyGuide: a brachytherapy-dedicated DICOM RT viewer and interface to Monte Carlo simulation software.
        J Appl Clin Med Phys. 2015; 16: 208-218
        • Hawk P.
        Practical physiological chemistry.
        12th ed. Blakiston Co., Philadelphia; Toronto1947
        • Kokatnur M.G.
        • Oalmann M.C.
        • Johnson W.D.
        • Malcom G.T.
        • Strong J.P.
        Fatty acid composition of human adipose tissue from two anatomical sites in a biracial community.
        Am J Clin Nutr. 1979; 32: 2198-2205
        • Oliveira A.F.
        • Cunha D.A.
        • Ladriere L.
        • et al.
        In vitro use of free fatty acids bound to albumin: a comparison of protocols.
        Biotechniques. 2015; 58: 228-233
        • Fruton J.
        General biochemistry.
        2nd ed. Wiley, New York1958
      4. Tipton I, Steiner R, Foland W, Mueller J, Stanley M. Spectrographic analysis of the tissues from autopsies of twenty-four instantaneous deaths. Oak Ridge, TN; 1954.

      5. Tipton I, Cook M, Steiner R, et al. Spectrographic analysis of tissues for trace elements. Progress report for July 1, 1955 through December 31, 1955. Oak Ridge, TN; 1956.

      6. Tipton I, Cook M, Steiner R, et al. Spectrographic analysis of normal tissue from Miami, Florida. Oak Ridge, TN; 1957.

      7. Tipton I, Cook M, Steiner R, et al. Spectrographic analysis of normal human tissue from Dallas, Texas. Oak Ridge, TN; 1957.

      8. Tipton I, Cook M, Steiner R, et al. Spectrographic analysis of normal human tissue from Baltimore, Maryland. Oak Ridge, TN; 1957.

      9. Tipton I, Cook M, Foland J, et al. Spectrographic analysis of normal tissue from Seattle and Tacoma, Wahinston. Oak Ridge, TN; 1958.

      10. Tipton I, Cook M, Steiner R, et al. Methods of collection, preperation and spectrographic analysis of human tissues. Oak Ridge, TN; 1957.

        • Ahrens L.
        Spectrochemical analysis: arranged particularly for the D.C. arc analysis of minerals, rocks and soils and applicable also to ceramic materials, refractories, slag, biological ash and powders. Wavelength tables of sensitive lines.
        Addison-Wesley Press, Cambridge, MA1950
        • Kwiatek W.M.
        • Banaś A.
        • Gajda M.
        • et al.
        Cancerous tissues analyzed by SRIXE.
        J Alloys Compd. 2005; 401: 173-177
        • Sutherland J.G.H.
        • Thomson R.M.
        • Rogers D.W.O.
        Changes in dose with segmentation of breast tissues in Monte Carlo calculations for low-energy brachytherapy.
        Med Phys. 2011; 38: 4858-4865
        • Demetri-Lewis A.
        • Slanetz P.J.
        • Eisenberg R.L.
        Breast calcifications: the focal group.
        Am J Roentgenol. 2012; 198: 325-343
        • Geramoutsos I.
        • Gyftopoulos K.
        • Perimenis P.
        • et al.
        Clinical correlation of prostatic lithiasis with chronic pelvic pain syndromes in young adults.
        Eur Urol. 2004; 45: 333-338
        • Suh J.H.
        • Gardner J.M.
        • Kee K.H.
        • et al.
        Calcifications in prostate and ejaculatory system: a study on 298 consecutive whole mount sections of prostate from radical prostatectomy or cystoprostatectomy specimens.
        Ann Diagn Pathol. 2008; 12: 165-170
        • Yaffe M.J.
        • Boone J.M.
        • Packard N.
        • et al.
        The myth of the 50–50 breast.
        Med Phys. 2009; 36: 5437-5443
        • Palmer A.K.
        • Kirkland J.L.
        Aging and adipose tissue: potential interventions for diabetes and regenerative medicine.
        Exp Gerontol. 2016; 86: 97-105
        • DeSantis C.
        • Ma J.
        • Bryan L.
        • Jemal A.
        Breast cancer statistics, 2013.
        CA Cancer J Clin. 2014; 64: 52-62
        • Leitzmann M.
        • Rohrmann S.
        Risk factors for the onset of prostatic cancer: age, location, and behavioral correlates.
        Clin Epidemiol. 2012; 1: 1-11
        • Finley D.S.
        • Calvert V.S.
        • Inokuchi J.
        • et al.
        Periprostatic adipose tissue as a modulator of prostate cancer aggressiveness.
        J Urol. 2009; 182: 1621-1627
        • Hoyland K.
        Post-radical prostatectomy incontinence: etiology and prevention.
        Rev Urol. 2014; 16: 181-188
        • Junuzovic D.
        • Hasanbegovic M.
        • Omerbegovic D.
        Erectile dysfunction as a complication after treatment of prostate cancer.
        Mater Socio Medica. 2011; 23: 230-231
        • Enger S.A.
        • Lundqvist H.
        • D’Amours M.
        • Beaulieu L.
        Exploring 57Co as a new isotope for brachytherapy applications.
        Med Phys. 2012; 39: 2342-2345
        • Oliveira S.M.
        • Teixeira N.J.
        • Fernandes L.
        • Teles P.
        • Vaz P.
        Dosimetric effect of tissue heterogeneity for 125I prostate implants.
        Rep Pract Oncol Radiother. 2014; 19: 392-398
        • Valentin J.
        Basic anatomical and physiological data for use in radiological protection: reference values.
        Ann ICRP. 2002; 32: 1-277
        • Johansson S.A.E.
        • Campbell J.L.
        • Malmqvist K.G.
        Particle induced X-ray emission spectrometry (PIXE).
        Wiley, 1995
        • Dimitriou P.
        • Becker H.-W.
        • Bogdanović-Radović I.
        • et al.
        Development of a reference database for particle-induced gamma-ray emission spectroscopy.
        Nucl Instrum Methods Phys Res B. 2016; 371: 33-36
        • Corliss W.R.
        Neutron activation analysis.
        2nd ed. U.S. Atomic Energy Commission, Division of Technical Information, Oak Ridge, TN1964
        • Sparkman O.D.
        • Penton Z.
        • Kitson F.G.
        Gas Chromatography and mass spectrometry: a practical guide.
        Academic Press, Burlington2011
        • Stuart B.H.
        Infrared spectroscopy: fundamentals and applications.
        John Wiley, Chichester, England/Hoboken, NJ2004
        • Hinshaw J.
        The thermal conductivity detector.
        LCGC North Am. 2006; : 24
        • Gauglitz G.
        • Moore D.S.
        Handbook of spectroscopy.
        2nd ed. Wiley-VCH, 2014
        • Steyermark A.
        Quantitative organic microanalysis.
        Academic Press, New York1961
        • Gower R.P.
        • Rhodes I.P.
        A review of techniques in the Lassaigne sodium-fusion.
        J Chem Educ. 1969; 46: 606-607
      11. PerkinElmer Inc. Award-winning results – 2400 series II CHNS/O elemental analysis {Brochure}; 2011.

        • Landry G.
        • Reniers B.
        • Granton P.V.
        • et al.
        Extracting atomic numbers and electron densities from a dual source dual energy CT scanner: experiments and a simulation model.
        Radiother Oncol. 2011; 100: 375-379
        • Bazalova M.
        • Carrier J.-F.
        • Beaulieu L.
        • Verhaegen F.
        Dual-energy CT-based material extraction for tissue segmentation in Monte Carlo dose calculations.
        Phys Med Biol. 2008; 53: 2439-2456
        • Leng S.
        • Yu L.
        • Wang J.
        • et al.
        Noise reduction in spectral CT: reducing dose and breaking the trade-off between image noise and energy bin selection.
        Med Phys. 2011; 38: 4946-4957
        • McCollough C.H.
        • Leng S.
        • Yu L.
        • Fletcher J.G.
        Dual-and multi-energy CT: principles, technical approaches, and clinical applications.
        Radiology. 2015; 276: 637-653
        • Newhauser W.D.
        • Zhang R.
        The physics of proton therapy.
        Phys Med Biol. 2015; 60: 155-209
        • White D.R.
        • Woodard H.Q.
        • Hammond S.M.
        Average soft-tissue and bone models for use in radiation dosimetry.
        Br J Radiol. 1987; 60: 907-913