What should we know about photon dose calculation algorithms used for radiotherapy? Their impact on dose distribution and medical decisions based on TCP/NTCP

Abdulhamid Chaikh, Tamizhanban Kumar, Jacques Balosso


The dose calculation algorithms, integrated in a radiotherapy treatment planning system, use different approximations to swiftly compute the dose distributions. Any biological effect is somehow related to the dose delivered to the tissues. Thus, the optimization of treatment planning in radiation oncology requires, as a basis, the most accurate dose calculation to carry out the best possible prediction of the Normal Tissue Complication Probability (NTCP), as well as Tumor Control Probability (TCP). Presently, a number of bio-mathematical models exist to estimate TCP and NTCP from a physical calculated dose using the differential dose volume histogram (dDVH). The purpose of this review is to highlight the link between any change of algorithms and possible significant changes of DVH metrics, TCP, NTCP and even more of estimated Quality-adjusted life years (QALY) based on predicted NTCP. The former algorithms, such as pencil beam convolution (PBC) algorithm with 1D or 3D density correction methods, overestimated the TCP while underestimating NTCP for lung cancer. The magnitude of error depends on the algorithms, the radiobiological models and their assumed radiobiological parameters setting. The over/under estimation of radiotherapy outcomes can reach up to 50% relatively. Presently, the anisotropic analytical algorithm (AAA), collapsed cone convolution algorithm (CCC), Acuros-XB or Monte Carlo are the most recommended algorithms to consistently estimate the TCP/ NTCP outcomes and QALY score, to rank and compare radiotherapy plans, to make a useful medical decision regarding the best plan. This paper points out also that the values of the NTCP radiobiological parameters should be adjusted to each dose calculation algorithm to provide the most accurate estimates. 


Dose calculation algorithm, Radiobiological models, Medical decision

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International commission on radiation units and measurements (ICRU). Determination of absorbed dose in a patient irradiated by beams of X or gamma rays in radiotherapy procedures. ICRU Report 24. 1976.

Ahnesjo A, Aspradakis MM. Dose calculations for external photon beams in radiotherapy. Phys Med Biol. 1999;44:R99–155.

Fdhila MT, Flandin IG, Balosso J, et al. Quantitative evaluation of the impact of heterogeneity correction on left breast cancer radiotherapy performed with respiratory gating. Int J Cancer Ther Oncol. 2016:4(1):417.

Ojala J. The accuracy of the acuros XB algorithm in external beam radiotherapy– A comprehensive review. Int J Cancer Ther Oncol. 2014;2:020417.

Rana S. Clinical dosimetric impact of acuros XB and analytical anisotropic algorithm (AAA) on real lung cancer treatment plans: review. Int J Cancer Ther Oncol. 2014;2:02019.

Rana S, Pokharel S. Dose-to-medium vs. dose-to-water: Dosimetric evaluation of dose reporting modes in acuros XB for prostate, lung and breast cancer. Int J Cancer Ther Oncol. 2014;2:020421.

Lu L. Dose calculation algorithms in external beam photon radiation therapy. Int J Cancer Ther Oncol. 2013;1(2):01025.

Chaikh A, Balosso J. Statistical evaluation of dosimetric changes between modified Batho's density correction method and anisotropic analytical algorithm for clinical practice. Int J Cancer Ther Oncol. 2016;4(2):4217.

Mavroidis P. Clinical implementation of radiobiological measures in treatment planning. Why has it taken so long? Int J CancerTher Oncol. 2013;1(1):01019.

Karlsson M , Ahnesjö A, Georg D, et al. Independent dose calculations: Concepts and models. ESTRO Booklet 10. 2010.

Batho HF. Lung corrections in cobalt 60 beam therapy. J Can Assoc Radiol. 1964;15:79–83.

Sontag MR, Cunningham JR. Corrections to absorbed dose calculations for tissue inhomogeneities. Med Phys. 1977;4 431–6.

Webb S, Fox RA. Verification by Monte Carlo methods of a power law tissue-air ratio algorithm for inhomogeneity corrections in photon beam dose calculations. Phys Med Biol. 1980;25:225–40.

Cassell KJ, Hobday PA, Parker RP. The implementation of a generalized Batho inhomogeneity correction for radiotherapy planning with direct use of CT numbers. Phys Med Biol. 1981;26:825–33.

El-Khatib E, Battista JJ. Improved lung dose calculation using tissue-maximum ratios in the Batho correction. Med Phys. 1984;11:279–86.

Thomas SJ. A modified power-law formula for inhomogeneity corrections in beams of high-energy x-rays. Med Phys. 1991;18:719–23.

Sontag MR, Cunningham JR. Clinical application of a CT based treatment planning system. Comput Tomogr. 1978;2:117–30.

Sontag MR, Cunningham JR .The equivalent tissue-air ratio method for making absorbed dose calculations in a heterogeneous medium. Radiology. 1978;129:787–94.

Wong E, Van Dyk J, Zhu Y. Lateral electron transport in FFT photon dose calculations. Med Phys. 1997;24(12):1992–2000.

Ahnesjo A. Collapsed cone convolution of radiant energy for photon dose calculation in heterogeneous media. Med Phys. 1989;16:577–92.

Gustafsson A, Lind BK, Brahme A. A generalized pencil beam algorithm for optimization of radiation therapy. Med Phys. 1994;21(3): 343–56.

Ahnesjo A, Saxner M, Trepp A. A pencil beam model for photon dose calculation. Med Phys. 1992;192:63–73.

Hurkmans C, et al. Limitations of a pencil beam approach to photon dose calculations in the head and neck region. Radiother Oncol. 1995;37:74–80.

Knoos T, et al. Limitations of a pencil beam approach to photon dose calculations in lung tissue. Phys Med Biol. 1995;40:1411–20.

Lewis RD, Ryde S, Seaby W, et al. Use of Monte Carlo computations in benchmarking radiotherapy treatment planning system algorithms. Phys Med Biol. 2000;45:1755-64.

Chaikh A, Balosso J. Should the dose prescription be readjusted when using tissues density corrections algorithms for radiation oncology? J Case Rep Onc Ther. 2014;1(1):01018.

Knöös T, Wieslander E, Cozzi L, et al. Comparison of dose calculation algorithms for treatment planning in external photon beam therapy for clinical situations. Phys Med Biol. 2006;51:5785-807.

Ojala JJ, Kapanen MK, Hyödynmaa SJ, et al. Performance of dose calculation algorithms from three generations in lung SBRT: comparison with full Monte Carlo-based dose distributions. J Appl Clin Med Phys. 2014:15(2):4-18.

Zaider M, Minerbo GN. Tumour control probability: A formulation applicable to any temporal protocol of dose delivery. Phys Med Biol. 2000;45(2):279–93.

Zaider M, Hanin L. Tumor control probability in radiation treatment. Med Phys. 2011;38(2): 574-83.

Bentzen, SM, Tucker SL. Quantifying the position and steepness of radiation dose response curves. Int J Radiat Biol. 1997; 71(5):531–42.

Tucker SL, Taylor JM. Improved models of tumour cure. Int J Radiat Biol. 1996;70(5): 539–53.

Martel M K, et al. Estimation of tumor control probability model parameters from 3-D dose distributions of non-small cell lung cancer patients. Lung Cancer. 1999;24:31–7.

Fenwick JD, et al. Escalation and intensification of radiotherapy for stage III non-small cell lung cancer: Opportunities for treatment improvement. Clin Oncol. 2009;21(4):343–60.

Nahum AE, Sanchez-Nieto B. Tumour control probability modelling: Basic principles and applications in treatment planning. Phys Med. 2001;17:13–23.

Webb S, Nahum AE. A model for calculating tumour control probability in radiotherapy including the effects of inhomogeneous distributions of dose and clonogenic cell density. Phys Med Biol. 1993;38:653–66.

Niemierko A. A unified model of tissue response to radiation. Med Phys. 1999:1100.

Niemierko A. Reporting and analyzing dose distributions: A concept of equivalent uniform dose. Med Phys. 1997;24(1):103–10.

Niemierko A. A generalized concept of equivalent uniform dose (EUD). Med Phys. 2009;26:1101.

Lyman JT. Complication probability as assessed from dose-volume histograms. Radiat Res. 1985;8:S13–9.

Kutcher GJ, Burman C. Calculation of complication probability factors for non-uniform normal tissue irradiation: the effective volume method. Int J Radiat Oncol Biol Phys. 1989;16;1623–30.

Mohan R, Mageras GS, Baldwin B, et al. Clinically relevant optimization of 3-D conformal treatments. Med Phys. 1992;19:933–44.

Deasy JO. Comments on the use of the Lyman–Kutcher–Burmanmodel to describe tissue response to nonuniform irradiation. Int J Radiat Oncol Biol Phys. 2000;47:1458–60.

Källman P, Ågren A, Brahme A. Tumour and normal tissue responses to fractionated non-uniform dose delivery. Int J Radiat Biol. 1992;62(2):249–62.

Allen Li X, et al. The use and QA of biologically related models for treatment planning. Report of AAPM task group 166 of the therapy physics committee. 2012.

Agren A, Brahme A, Turesson I. Optimization of uncomplicated control for head and neck tumors. Int J Radiat Oncol Biol Phys. 1990;19:1077–85.

Langer M, Morrill SS, Lane R. A test of the claim that plan rankings are determined by relative complication and tumor-control probabilities. Int J Radiat Oncol Biol Phys. 1998;41(2):451-7.

Brenner DJ, et al. A more robust biologically based ranking criterion for treatment plans. Int J Radiation Oncology Biol Phys. 1999;43(3):697–700.

Chaikh A, Balosso J. A decision protocol to propose proton versus photon radiotherapy: In silico comparison. Radiother Oncol. 2016:119(1),EP-2077: S979.

Chaikh A, Giraud JY, Balosso J. A method to quantify and assess the dosimetric and clinical impact resulting from the heterogeneity correction in radiotherapy for lung cancer. Int J Cancer Ther Oncol. 2014;2(1): 020110.

Chaikh A, Giraud JY, Marguet M, et al. A decision tool to determine the prescribed dose after change in the dose calculation algorithm. Int J Cancer Ther Oncol. 2014;2(4):020414.

Chaikh A, Balosso J, Giraud JY. A 3D quantitative evaluation for assessing the changes of treatment planning system and irradiation techniques in radiotherapy. Int J Cancer Ther Oncol. 2014;2(3):02033.

Chaikh A, Balosso J, Giraud JY. Clinical comparison of pencil beam convolution and Clarkson algorithms for dose calculation. Journal of Cancer Therapy. 2013;4:1485-9.

Chaikh A, Balosso J, Giraud JY. Clinical comparison of density correction methods associated with pencil beam convolution algorithm for clinical situations. Medical Physics International Journal. 2014;2(1):49-53.

Chaikh A, Balosso J. The use of radiobiological TCP and NTCP models to validate the dose calculation algorithm and readjust the prescribed dose. Radiother Oncol. 2016;118: S24.

Nielsen TB, Wieslander E, Fogliata A, et al. Influence of dose calculation algorithms on the predicted dose distribution and NTCP values for NSCLC patients. Med Phys. 2011;38(5):2412-8.

Engelsman M, et al. Impact of simple tissue inhomogeneity correction algorithms on conformal radiotherapy of lung tumours. Radiother Oncol. 2001;60(3):299–309.

Brink C, et al. Sensitivity of NTCP parameter values against a change of dose calculation algorithm. Med Phys. 2007;34(9):3580-6.

Lax I, Panettieri V, Wennberg B, et al. Dose distributions in SBRT of lung tumors: Comparison between two different treatment planning algorithms and monte carlo simulation including breathing motions. Acta Oncol. 2006;45:978–88.

Rye Aarup L, Nahum A, Zacharatou C, et al. The effect of different lung densities on the accuracy of various radiotherapy dose calculation methods: Implications for tumour coverage. Radiotherapy and oncology. 2009; (91):405–14.

Chetty IJ, Devpura S, Liu D, et al. Correlation of dose computed using different algorithms with local control following stereotactic ablative radiotherapy (SABR)-based treatment of non-small-cell lung cancer. Radiother Oncol. 2013;109:498-504.

Liu MB, Eclov NC, Trakul N, et al. Clinical impact of dose overestimation by effective path length calculation in stereotactic ablative radiation therapy of lung tumors. Pract Radiat Oncol. 2013;3:294-300.

Chandrasekaran M, Panettieri V, Baker C, et al. The clinical impact of differences in photon dose algorithms: 3DCRT and SBRT of lung tumors. Radiother Oncol. 2011;99:S427–8.

Chaikh A, Balosso J. Assessing the shift of radiobiological metrics in lung radiotherapy plans using 2D gamma index. Transl Lung Cancer Res. 2016;5(3).

Chaikh A, Balosso J. NTCP variability in radiotherapy of lung cancer when changing the radiobiologic models and the photon dose calculation algorithms. J Cancer and Clin Oncol. 2016; 2(1).

Chaikh A, Balosso J. Quantitative comparison of radiotherapy plans using 2D gamma maps and computed tomography. Quant Imaging Med Surg. 2016;6(3):243-9.

American Association of Physicists in Medicine (AAPM) report 85 tissue inhomogeneity corrections for MV photon beams. USA: Report of Task Group No. 65 of AAPM. 2004.

Burman C, Kutcher GJ, Emami B, et al: Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys. 1991;21:123–35.

Emami B, Lyman J, Brown A, et al: Tolerance of normal tissue to therapeutic irradiation. Int J Radiat Oncol Biol Phys. 1991;21:109–12.

Seppenwoolde Y, Lebesque JV, De Jaeger K, et al. Comparing different NTCP models that predict the incidence of radiation pneumonitis. Int J Radiat Oncol Biol Phys. 2003;55:724–35.

Kwa SL, Lebesgue JV, Theuws JCM, et al. Radiation pneumonitis as a function of mean dose: an analysis of pooled data of 540 patients. Int J Radiat Oncol Biol Phys. 1998;42:1–9.

De Jaeger K, Hoogeman MS, Engelsman M, et al. Incorporating an improved dose-calculation algorithm in conformal radiotherapy of lung cancer: Re-evaluation of dose in normal lung tissue. Radiother Oncol. 2003; 69:1–10.

Emma H, Anna B. Influence of different dose calculation algorithms on the estimate of NTCP for lung complications. Journal of applied clinical medical physics. 2013;14(5):127-39.

M Hartmann, Schneider U. Integration of second cancer risk calculations in a radiotherapy treatment planning system. Journal of Physics: Conference Series 489. 2014;012049.

Schneider U, Kaser-Hotz B. Radiation risk estimates after radiotherapy: Application of the organ equivalent dose concept to plateau dose-response relationships. Radiat Environ Biophys. 2005;44(3):235-9.

Schneider U, et al. Estimation of radiation induced cancer from 3D-dose distributions: Concept of organ equivalent dose. IntJ Radiat Oncol Biol Phys. 2005;61:1510-5.

Dasu A, Toma-Dasu I, Olofsson J, et al. The use of risk estimation models for the induction of secondary cancers following radiotherapy. Acta Oncol. 2005;44(4):339-47.

UNSCEAR. Report to the general assembly: Sources and effects of ionizing radiation. Annex I-Epidemiological evaluation of radiation-induced cancer. 2000.

Liang X, et al. Radiobiological impact of dose calculation algorithms on biologically optimized IMRT lung stereotactic body radiation therapy plans. Radiat Oncol. 2016;11:10.

Padmanaban, et al. Comparison of Acuros (AXB) and Anisotropic Analytical Algorithm (AAA) for dose calculation in treatment of oesophageal cancer: Effects on modeling tumor control probability. Radiation Oncology. 2014;9:286.

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DOI: http://dx.doi.org/10.14319/ijcto.44.18

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