

ORIGINAL ARTICLE 



Year : 2017  Volume
: 42
 Issue : 4  Page : 222233 

A unified dose response relationship to predict high dose fractionation response in the lung cancer stereotactic body radiation therapy
Than S Kehwar^{1}, Kashmiri L Chopra^{2}, Durg V Rai^{2}
^{1} Department of Radiation Oncology, Eastern Virginia Medical School, Sentara Obici Hospital, Suffolk, VA 23434, USA ^{2} Department of Biomedical Engineering, Shobhit University, Saharanpur, Uttar Pradesh, India
Date of Submission  08Mar2017 
Date of Decision  18Aug2017 
Date of Acceptance  18Aug2017 
Date of Web Publication  5Dec2017 
Correspondence Address: Than S Kehwar 1123 W Powderhorn Road, Mechanicsburg, PA 17050 USA
Source of Support: None, Conflict of Interest: None  Check 
DOI: 10.4103/jmp.JMP_36_17
Abstract   
Aim: This study is designed to investigate the superiority and applicability of the model among the linearquadratic (LQ), linearquadraticlinear (LQL) and universalsurvivalcurve (USC) models by fitting published radiation cell survival data of lung cancer cell lines. Materials and Method: The radiation cell survival data for small cell (SC) and nonsmall cell (NSC) lung cancer cell lines were obtained from published reports, and were used to determine the LQ and cell survival curve parameters, which ultimately were used in the curve fitting of the LQ, LQL and USC models. Results: The results of this study demonstrate that the LQL(D_{tmt}) model, compared with the LQ and USC models, provides best fit with smooth and gradual transition to the linear portion of the curve at transition dose D_{tmt}, where the LQ model loses its validity, and the LQL(D_{t2α/β}) and USC(Dtmt) models do not transition smoothly to the linear portion of the survival curve. Conclusion: The LQL(D_{tmt}) model is able to fit wide variety of cell survival data over a very wide dose range, and retains the strength of the LQ model in the lowdose range.
Keywords: Linear quadratic linear model, linear quadratic model, lung cancer, stereotactic body radiation therapy, universal survival curve model
How to cite this article: Kehwar TS, Chopra KL, Rai DV. A unified dose response relationship to predict high dose fractionation response in the lung cancer stereotactic body radiation therapy. J Med Phys 2017;42:22233 
How to cite this URL: Kehwar TS, Chopra KL, Rai DV. A unified dose response relationship to predict high dose fractionation response in the lung cancer stereotactic body radiation therapy. J Med Phys [serial online] 2017 [cited 2023 Mar 24];42:22233. Available from: https://www.jmp.org.in/text.asp?2017/42/4/222/219929 
Introduction   
In a standard external beam radiation therapy treatment scheme, dose per fraction between 1.8 Gy to 2.0 Gy is delivered over a period of 5–8 weeks, with 5 fractions/week, which is based on the early experience of radiotherapy treatment and has been justified by accepted radiobiological models.^{[1],[2],[3],[4],[5]} On the other hand, hypofractionation treatments are delivered to relieve symptoms of the disease, such as pain, by delivering higher doses in few fractions.^{[6],[7],[8]} Therefore, daily fraction size is larger than that used in standard fractionation, which is commonly more than 3 Gy. Large doses delivered in palliative cases would very unlikely cause late complications as compared to the survival time of the patient.^{[9],[10],[11]}
Conventionally, single large dose fractions are used by the neurosurgeons to treat brain lesions and it is an important treatment option for patients with benign and metastatic brain lesions.^{[12],[13],[14]} Similar to stereotactic radiosurgery (SRS), stereotactic body radiation therapy (SBRT) is a radiation therapy technique in which large dose of radiation is delivered in one or few fractions with high conformity to an extracranial body targets.^{[15],[16],[17],[18]}
Use of large dose fractions in SRS and SBRT defied conventional radiobiology of oxygenenhanced sensitivity of the tumor cells.^{[19]} The large volume tumors comprise of hypoxic core that is more radioresistant due to poor vascular supply to the core.^{[20]}
Fractionation schemes are widely used in radiation therapy to exploit the effect of reoxygenation of the tumor cells and sublethal damage repair of normal tissues during interfraction interval, which expands the therapeutic window.^{[1],[21],[22]} The radiobiological phenomenon of these fractionation schedules can adequately be explained by the linear quadratic (LQ) model ^{[23],[24],[25]} while it fails to do so for SRS and SBRT due to inherent drawbacks in the formulation.^{[25],[26],[27]}
In this article, we attempt to investigate the validity and applicability of the LQ, LQlinear (LQL), and universal survival curve (USC) models for lung SBRT using published cell survival data of different cell lines of the lung cancers, and a unified LQL model is derived to predict high dose response for lung SBRT cases.
Methods And Materials   
The linear quadratic model
The LQ model is the most popular method used for fitting experimental results derived from in vitro and in vivo radiation survival experiments of clonogenic cells of mammalian and human origin, irradiated to different dosefractionation schemes.^{[3],[23]} Based on the findings of the fits and their agreement with the LQ model predictions, it is widely used in the clinics to interpret tumor and normal tissue response.^{[24],[25],[26],[27]}
When ionizing radiation interacts with cells, it causes radiationinduced DNA damage, which can be divided into two categories, namely, (1) irreparable lethal damage and (2) reparable sublethal damage.^{[25],[26],[27],[28]} When single dose of radiation is delivered, the survival fraction (S) is given by
or
where D is the single fraction dose, a and b are the coefficients of lethal and sublethal damages, respectively, and α/β is the tissuespecific parameter that implies the dose at which the components of lethal damage and sublethal damage, i.e. aD and bD ^{2}, of the LQ survival equation, intersect.
In the formulation of the equation (1) it is assumed that the doublestranded DNA breaks are sufficient enough to cause cell death. The doublestranded breaks can be achieved by single– hit or by two separate hits. The single– hit aberrations correspond to the lethal damage and two separate hits aberrations correspond to the sublethal damage, and are represented by aD and bD ^{2}, respectively.
The LQ model has been extensively used in clinics over the last three decades, and its validity range appears to extend up to 6 Gy per fraction.^{[29]} Beyond the validity range, i.e. at doses more than 6 Gy per fraction which are used in SRS and SBRT treatments. The doseresponse curves of the LQ model keep on bending and are inconsistent with the in vitro survival curves that are straight on the semilogarithmic plot at high doses.^{[30],[31],[32]} Hence, the LQ model is a low dose approximation and cannot be applied to interpret dose– response for higher dose fractionation schedules. At lower single fraction dose, the biologically effective dose (BED) may be given by
Almost a century of the research on the radiobiological basis of radiation therapy revealed 4 Rs that are critical in determining the net effect of radiation therapy on tumor cells: (1) Repair of sublethal damage, (2) Repopulation of cells after irradiation, (3) Redistribution of cells within the cell cycle, and (4) Reoxygenation of the surviving cells during or after irradiation.^{[21],[22]} The effects of these 4 Rs are exploited in the design of various fractionated treatment schedules. The BED for such a schedule can be written by
where n is the number fractions and d is the dose per fraction.
Within the validity range of the LQ model, the comparison of fractionated treatment schedule is done by calculating the equivalent dose in 2 Gy fractions (EQD2) and is given by
where BED_{x} is the BED for a fractionated treatment schedule delivered with a dose per fraction “x” other than 2 Gy per fractions.
Conventional treatment protocols are delivered within 4–8 weeks and tumor cell proliferation become an important issue. In formulation or extension of the LQ mode for hypofractionation, the issue of tumor cell proliferation is not accounted because it is intended to focus on the investigation of the validity and applicability of the models in the doses used for SRS and SBRT.^{[31],[32],[33],[34],[35]}
In the high dose range, the sublethal damage repair rate, per lesion, decreases and the production rate of the lethal damage increases with increasing the dose. This phenomenon is explained by various researches, considering highdose saturation repair models.^{[36],[37],[38],[39],[40],[41]} This change in repair rate might be due to overloading of the repair enzymes, which may result in a different amount at different dose levels and might increase with increasing dose. Thereby at higher doses, the sublethal damage is not repaired due to overloading of the repair enzymes and hence the LQ model fails to explain doseresponse curve.
To explain radiobiological phenomenon, the experimental survival curves and clinical results in the high dose region, the LQ– L, and the USC models were developed by Carlone et al.^{[34]} and Park et al.,^{[31]} respectively.
The linear quadratic–linear model
To address the issue of doseresponse at high doses used in SRS and SBRT, Guerrero and Li ^{[33]} had modified the LQ model, and subsequently Carlone et al.^{[34]} explained the LQL behavior of the model. Astrahan ^{[29]} employed bipartite method, proposed by Park et al.,^{[31]} to the LQL model to explain the issues of doseresponse occurring at high doses. In the conventional fractionation dose range, the LQ model fits appropriately and smoothly transition to the linearity at some dose D_{t}, called transition dose. The survival fraction(s) can be written in bipartite form after single dose of radiation as
and
where α and α/β are the LQ parameters, as explained in the previous section, and γ is the coefficient of the damage in the final linear portion of the survival curve at high doses.
Equation (5b) reveals that the coefficient “γ” is the log_{e} cell kill per Gy dose in the linear portion of the survival curve at high doses.
The BED, of the model, can be given by
and
To derive the value of γ or , let us assume that at a dose of D = D_{t}+ δD, the BED of the LQ model approximately equals to the BED of the LQL model, where δD is infinitesimal amount of dose, and the solution is given by equating equations (6a) and (6b) as
and
By substituting equation (7a) into eqution (6b), BED can be calculated for SRS/SBRT doses by specifying only one additional parameter, the transition dose D_{t}, and the equation (5b) and equation (6b) can be written as
and
This is a simple expression to calculate BED for high dose single fraction.
Astrahan ^{[29]} used selfdeveloped software program which allowed interactive manipulation of the α, α/β and D_{t} parameters of the LQL model used in curve fitting for published doseresponse and multi fractionation isoeffect data of different cell lines. He saw that the line tangent to the LQ curve at transition dose Gy intersected the e^{−α}D and e^{−β}D^{2} curves at dose α/β Gy and also provided closely fit the linear response in the high dose region of some classic in vitro cell survival curves for which the value of α/β was low.^{[29]} This value of D_{t} hereinafter will be denoted by D_{t2}_{α/β}. For fractionated regimen where treatment is delivered in n number of fractions with D per fraction, the S and the BED can be written as
and
and
The EQD2 for high dose fractionation regimen is given by
The universal survival curve model
The experimental data for various cell lines have shown that there is a linear relationship between dose and log of the survival fraction at higher doses. The LQ model calculations predict a continuous bending of the survival curve, which reveals that the LQ model overestimates the effect in high dose region, because of its inability to account for the saturation of the repair of sublethal damage. The multitarget multihit (MT) model proposed by Tym and Todd ^{[42]} to fit survival curve data for different cell lines, provides a best fit. The survival fraction following a dose D is given by
where the first term, represents a single event to be responsible for an effect, corresponding to the lethal damage of the LQ model, and the second term in the square bracket, , represents that independent events are responsible to cause an effect, that is corresponding to the sublethal damage of the LQ model. The D_{1} and D_{0} are the initial slope ( first log cell kill) and final slope (constant fraction log cell kill) of survival curve, respectively.
In the low dose region where D << D_{0} and D → 0, the expression in equation (10) reduces to
By comparing equation (11) with the first term of the LQ model (equation (1)), we have . Using the value of into equation (10), the MT model can be written as
In the high dose region, where D >>D_{0}, the survival curve of the MT model approaches an asymptote and is given by
or
Park et al.^{[31]} have proposed a hybrid model, the USC model, by combining the LQ model and the MT model to enhance the fit of the survival curve data in a larger span.
In the USC model, the LQ model smoothly transitions into the asymptotic linear portion of the MT model at a transition dose D_{t}. Hence, the USC model for single dose fraction is given by
and
The BED for single dose fraction scheme is
and
In equation (15b) and equation (16b), is the asymptotic intercept on y–axis, and can be derived by equating equation (16a) with equation (16b) at the transition dose D_{t}
By substituting equation (16c) into equations (15b) and (16b), S and BED can be calculated for SRS/SBRT doses by specifying only two additional parameters, the transition dose D_{t} and D_{0}. equation (16b) can be written as
and
For multiple fractionation schemes, where n number of fractions are delivered with D(Gy) per fraction, the S for the USC model is given by
and
The BED for such fractionated scheme is
and
The EQD2 for high dose fractionation regimen is given by
Unified linear quadraticlinear model
Park et al.^{[31]} determined the D_{t} at which the S of the LQ model smoothly transitions into an asymptotic straight line of the S of the MT model, which is a tangential line to the LQ curve. At D_{t}, the S of the LQ model is equal to that of the MT model, hence by equating equation (15a) to equation (15b), or equation (16a) to (16b) at transition dose, the D_{t} can be given by
where D_{tmt} is the transition dose derived using the MT model. At D_{tmt}, the S of the LQ model also smoothly transitions into an asymptotic straight line of the S of the LQL model, which is tangential line to the LQ curve, and hence at this point of dose the S of the LQ model is equal to the S of the LQL model. Consequently, at transition dose D_{t}, the survival fractions of these three models are equal. Therefore, D_{tmt} from Eq.(20) can be used into equation (7c) and equation (7d) to describe the S and BED for high dose fraction schemes.
Data collection
Seven and 15 in vitro radiation survival plots for human lung cancer cell lines were digitized from the charts of reports of Carney et al.,^{[43]} consisting of 5 small cell (SC) and 2 large cell (LC) lines, and Carmichael et al.,^{[44]} consisting of 3 SC, 3 LC, 4 adenocarcinoma (Ad), 3 adenosquamous (AdSq) and 2 squamous (Sq) cell lines, respectively. In both the reports the charts had been plotted with more than one experimental data. Hence, the digitized data were averaged for same dose point. The cell survival parameters, D_{0} and , and the LQ parameters, a and b, were extracted from the reports of Carmichael et al.^{[44]} for 8 SC and 12 nonSC (NSC), and Krarup et al.^{[45]} for 15 SC cell lines. The plots, the cell survival and the LQ parameters were grouped into two classes of SC and NSC lung cancer cell lines. The LC, Ad, AdSq and Sq lung cancer cell lines are the parts of NSC lung cancer cell lines, but to avoid confusion between data sets of different researchers, we used same terminology as was used by original investigator.
Results   
The value of the LQ model parameter, a, for digitized cell survival data from the charts of the reports of Carney et al.^{[43]} and Carmichael et al.,^{[44]} was determined by the bestfit regression method for lowdose survival data and b was calculated with an interactive inspection and chisquare best fit to the initial curvature points with R^{2} ≥ 0.97. The values of D_{0} and were calculated by the bestfit regression method to the final slope survival data, and are used in equation (20) to calculated transition doses. [Table 1] enlists the LQ and radiation cell survival parameters for SC and NSC lines.  Table 1: Linear quadratic and radiation cell survival parameters for 22 human lung cancer cell lines
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The parameters from [Table 1] were used to calculate the values of D_{t2α/β} and , for digitized data set of the reports,^{[43],[44]} and to plot survival curves for the LQ, LQL, and USC models over the extrapolated range up to 30 Gy, shown in [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]. Two set of SF curves were plotted for the LQL model for transition dose denoted by D_{t2α/β}, and D_{tmt}, with same value of γ (=1/D_{0}), while for the USC model it was done only for D_{tmt}.  Figure 1: Cell survival curves plotted for the linear quadratic, linear quadraticlinear and universal survival curve models for small cell lung cancer cell lines data digitized from Carney et al.'s charts^{[43]}
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 Figure 2: Cell survival curves plotted for the linear quadratic, linear quadraticlinear and universal survival curve models for small cell lung cancer cell lines data digitized from Carmichael et al.' s charts^{[44]}
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 Figure 3: Cell survival curves plotted for the linear quadratic, linear quadraticlinear and universal survival curve models for large cell lung cancer cell lines data digitized from Carney et al.'s charts^{[43]}
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 Figure 4: Cell survival curves plotted for the linear quadratic, linear quadraticlinear and universal survival curve models for large cell lung cancer cell lines data digitized from Carmichael et al.'s charts^{[44]}
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 Figure 5: Cell survival curves plotted for the linear quadratic, linear quadraticlinear and universal survival curve models for adenocarcinoma lung cancer cell lines data digitized from Carmichael et al.'s charts^{[44]}
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 Figure 6: Cell survival curves plotted for the linear quadratic, linear quadraticlinear and universal survival curve models for adenosquamous lung cancer cell lines data digitized from Carmichael et al.'s charts^{[44]}
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 Figure 7: Cell survival curves plotted for the linear quadratic, linear quadraticlinear and universal survival curve models for squamous lung cancer cell lines data digitized from Carmichael et al.'s charts^{[44]}
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The mean values of α, β, D_{0} and , determined from the digitized data of Carney et al.'s report,^{[43]} were found to be 0.75 ± 0.17 Gy ^{−1} (ranged: 0.49–0.92 Gy ^{−1}), 0.06 ± 0.05 Gy ^{−2} (ranged: 0.030.15 Gy ^{−2}), 0.85 ± 0.28 Gy (ranged: 0.48–1.20 Gy, and 3.1 ± 1.93 (ranged: 1.58–6.2), respectively, for SC cell lines; 0.13 ± 0.03 Gy ^{−1} (ranged 0.11–0.15 Gy ^{−1}), 0.10 ± 0.01 Gy ^{−2} (ranged: 0.10–0.11 Gy ^{−2}), 0.83 ± 0.06 Gy (ranged 0.79–0.87 Gy) and 11.94 ± 5.10 (ranged 8.33–15.54), respectively, for LC cell lines. The mean values of these parameters for digitized data of Carmichael et al.'s report ^{[44]} were 0.16 ± 0.12 Gy ^{−1} (ranged: 0.07–0.30 Gy ^{−1}), 0.06 ± 0.03 Gy ^{−2} (ranged 0.04–0.09 Gy ^{−2}), 1.41 ± 0.42 Gy (ranged: 0.98–1.81 Gy) and 4.59 ± 1.18 (ranged 3.2–6.6), respectively, for SC cell lines; 0.20 ± 0.05 Gy ^{−1} (ranged: 0.15–0.24 Gy ^{−1}), 0.06 ± 0.03 Gy ^{−2} (ranged: 0.04–0.10 Gy ^{−2}), 1.18 ± 0.28 Gy (ranged: 0.91–1.47 Gy) and 7.22 ± 2.79 (ranged: 5.28–10.42), respectively, for LC cell lines; 0.39 ± 0.30 Gy ^{−1} (ranged: 0.14–0.79 Gy ^{−1}), 0.04 ± 0.01 Gy ^{−2} (ranged 0.02–0.06 Gy ^{−2}), 1.25 ± 0.38 Gy (ranged: 1.02–1.82 Gy) and 5.21 ± 3.95 (ranged: 1.28–9.13), respectively, for Ad cell lines; 0.25 ± 0.08 Gy ^{−1} (ranged 0.16–0.31 Gy ^{−1}), 0.07 ± 0.03 Gy ^{−2} (ranged: 0.05–0.10 Gy ^{−2}), 1.25 ± 0.24 Gy (ranged: 1.01–1.48 Gy) and 4.68 ± 2.49 (ranged: 2.04–7.0), respectively, for AdSq cell lines; 0.29 ± 0.01 Gy ^{−1} (ranged: 0.28–0.29 Gy ^{−1}), 0.05 ± 0.02 Gy ^{−2} (ranged: 0.03–0.06 Gy ^{−2}), 1.07 ± 0.13 Gy (ranged: 0.97–1.16 Gy) and 7.94 ± 1.55 (ranged: 6.86–9.03), respectively, for Sq cell lines.
Combined calculated values of α, β, D_{0} and parameters for SC and NSC cell lines for digitized data of both reports of Refs 43 and 44, are compared with that of extracted from the reports of Refs. 44 and 45, shown in [Table 2], with Student's ttest values.  Table 2: Linear quadratic and radiation survival parameters for combined data set
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Further the calculated values of α, β, D_{0} and are combined with that of obtained from the reports, to get representative values of the parameters and are given in [Table 3] along with calculated values of D_{tmt}.  Table 3: Representative parameters used to plot survival curves in the linear quadratic, linear quadraticL (D_{t2α/β}), linear quadraticL (D_{tmt}) and universalsurvivalcurve (D_{tmt}) models and to calculate biologically effective dose and equivalent dose in 2 Gy fractions for RTOG 0813 dose scheme
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The parameters from [Table 3] were used to plot survival curves for the LQ, LQL(D_{t2α/β}), LQL(D_{tmt}) and USC(D_{tmt}) models, shown in [Figure 8] and [Figure 9] for SC and NSC cell lines, respectively.  Figure 8: Cell survival curves for SC cells using representative parameters in the linear quadratic, linear quadraticlinear (_{t2}α/β), linear quadraticlinear (Dtmt) and universal survival curve (Dtmt) models
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 Figure 9: Cell survival curves for NSC cells using representative parameters in the linear quadratic, linear quadraticlinear (D_{t2}α/β), linear quadraticlinear (Dtmt) and universal survival curve (Dtmt) models
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The BED and EQD2 were calculated for fractionated SBRT dose scheme of the RTOG 0813^{[46]} protocol from Level 1 to Level 9 (8 Gy to 12 Gy per fraction delivered in 5 fractions) using the LQ, LQL(D_{tmt}) and USD(D_{tmt}) models, for SC and NSC lung cancers. The values of representative parameters [Table 3] are used and are given in [Table 4].  Table 4: Biologically effective dose and equivalent dose in 2 Gy fractions calculated for RTOG 0813 dose scheme of lung stereotactic body radiation therapy cases using representative parameters into the linear quadratic, linear quadraticL (D_{tmt}) and USD (D_{tmt}) models, for small cell and nonsmall cell lung cancers
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Discussion   
The LQ model is well documented in the literature that it adequately describe the survival curve in low dose fraction domain, and hence is regarded as a low dose approximation to the equation,^{[25]} in which the intrinsic radiosensitivity of the cells is represented by the coefficient of lethal damage, a which is the initial slope of the survival curve.^{[47]} Beyond the LQ model dose fraction domain, i.e. in the high dose fraction domain, an exponential function fit radiation cell survival data of most of the cell lines, and represented by the parameters, D_{0} and n–. In [Table 1], the LQ and radiation cell survival parameters for SC and NSC lines, differ than that in the original report, because of an inaccuracy occurred in the digitization of the data from the charts and the methods used by the authors.^{[43],[44]} The parameters derived by Carney et al.^{[43]} employing linear regression analysis of the exponential region of the curve and Carmichael et al.^{[44]} derived by fitting to the LQ and singlehit, MT models using a program written by Albright.^{[48]}
It is seen in [Table 1] that the radiation survival curves for the SC of Carney et al.'s charts ^{[43]} characterized by α, β, D_{0} and n– are considerably different than that of the counterpart SC of Carmichael et al.'s,^{[44]} except for NCI H 146. For NSC, comprising LC, Ad, AdSq and Sq cell lines, the survival curve for LC of Carney et al.'s data ^{[43]} characterized by α, β, D_{0} and n– are comparable to their counterparts in Carmichael et al's data.^{[44]} Intracomparison of the parameters of Carmichael et al.^{[44]} data, show that the values for Ad are comparable with that of AdSq and Sq, except for NCI H 23 and NCI H 358.
The values of α, β, D_{0}, and n–, for each cell line, were used to calculate the values of D_{t2α/β} and D_{t}[=(2D_{0} ln(n–))/(1aD_{0})], for digitized data set of the reports,^{[43],[44]} which were further used to plot survival curves for the LQ, LQL, and USC models, and the comparison of the goodness of fit with the digitized and extrapolated survival data was performed using the Chisquare goodness test. The LQ parameters a and b for 6 cell lines, 4 SC (NCI H249, NCI H187, NCI H209, NCI H69) and 2 Ad (NCI H23, NCI H358), provide higher values of D_{t2α/β}, than the dose range considered in this study, resulting in the LQL(D_{t2α/β}) curves to superimpose on the LQ curves, shown in [Figure 1]b, [Figure 1]c, [Figure 1]d, [Figure 1]e and [Figure 5]a, [Figure 5]b. A close inspection of [Figure 1]a (NCI H146), 2a (NCI H69 classic), 2b (NCI H526 variant), 3b (NCI H417), 4a (NCI H157), 5d (A549), 6a (NCI H322), 6b (NCI H596), 6c (NCI H647), and 7b (NCI H520) make clear that when D_{t2α/β} is close to D_{tmt}, the LQL(D_{t2α/β}) curve runs parallel to the LQL(D_{tmt}) and USC(D_{tmt}) curves. It has also been seen that as D_{tmt}/D_{t2α/β} ratio tends to reach 1, the LQL(D_{t2α/β}) curve approaches close to the LQL(D_{tmt}) curve. On the other hand, in [Figure 1]b (NCL H249) and 2a (NCI H69 classic), the USC(D_{tmt}) curves superimposed on the LQL(D_{tmt}) curves. In [Figure 2]b (NCI H 526 variant), 4a (NCI H157), 4b (NCI H46), 5a (NCI H23), 5d (A549), and 6c (NCI H647) the USC curves are slightly raised at D_{tmt}, i. e. the value of survival fraction slightly increased at D_{tmt}, and then progress parallel to the LQL(D_{tmt}). [Figure 1]a, [Figure 1]b, [Figure 1]c, [Figure 1]d, [Figure 1]e, [Figure 2]c, [Figure 3]a, [Figure 1]b, [Figure 4]c, [Figure 5]b, [Figure 1]c, [Figure 6]a, [Figure 1]b and [Figure 7]a, [Figure 1]b show that the USC curves drop slightly at D_{tmt} and then run parallel with LQL(D_{tmt}) curves. It is clear from [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7] that the LQL(D_{tmt}) curves, for cell lines studied in this work, do not show any raise or drop at D_{tmt} and transition smoothly to the linear asymptotic portion of the curve over the range of the dose fraction accounted in this study.
Combined calculated values of α, β, D_{0} and n– parameters for SC and NSC cell lines for digitized data of both reports, of Refs 43 and 44, are compared with that of extracted from the reports, of Refs 44 and 45, and are shown in [Table 2]. The Student's ttest reveals that the calculated values of α, β, D_{0} and n– are not significantly different than that of the values obtained from the reports. Since, the Student's ttest illustrates no statistical difference between the parameters of two groups, the calculated values of α, β, D_{0} and n– are combined with that obtained from the reports, and the results are given in [Table 3]. The value of D_{tmt} and D_{0}[Table 3] are comparable, while values of α, α/β and n– are different from that reported by Wennberg and Lax ^{[49]} for lung tumors, which were of NSC taken from Park et al.^{[31]}
Using resultant values of the parameter, the representative values the LQ and survival curve parameters, with calculated D_{tmt}, are given in [Table 3]. [Figure 8] and [Figure 9] show the plots for the LQ, LQL(D_{t2α/β}), LQL(D_{tmt}) and USC(D_{tmt}) models for representative values [Table 3] of α, β, α/β, D_{0}, n– and D_{tmt}, for SC and NSC cell lines, respectively. In [Figure 8] and [Figure 9], the LQL(D_{tmt}) curves, for both cell lines, transition smoothly to asymptotic linear portion at D_{tmt}, while the USC(D_{tmt}) curves show slight increase in survival fraction at D_{tmt}. In [Figure 8] the LQL(D_{t2α/β}) curve follows the LQ curve, even after passing initial slope of the curve, and then transitions to a linear portion with a significant increase in survival fraction which makes it biphasic, due to considerably higher value of D_{t2α/β} than D_{tmt}. On the other hand, in [Figure 9], for NSC cell lines, the LQL(D_{t2α/β}) curve drops at D_{t2α/β} that brings it below the LQ curve, and then intersects the LQ curve. The steepness of the survival curves of these models varied considerably at dose per fraction above transition dose and LQL(D_{tmt}) model showed smooth transition than the USC and the LQL(D_{t2α/β}) models. For some of the cell lines, the USC model is slightly less sensitive compared to the LQL (D_{tmt}) model, considerably less sensitive to the LQL(D_{t2α/β}) and the LQ models at high fraction doses.
The results of this study demonstrate that applicability of the transition dose D_{t}= 2α/β is valid only if its value is close to D_{tmt}. Astrahan ^{[29]} discussed that the LQL(D_{t2α/β}) curve using a simplification of the transition dose D_{t}= 2α/β can only be applied where there is smooth and gradual transition of the LQ curve to asymptotic linear portion.
To evaluate relative difference between the LQ, LQL(D_{tmt}) and USD(D_{tmt}) models for fractionated SBRT, the BED and EQD2 were calculated for the dose scheme used by the RTOG 0813^{[46]} protocol from level 1 to level 9 varying from 8 Gy to 12 Gy per fraction delivered in 5 fractions. The values of BED and EQD2 calculated using the LQ, LQL(D_{tmt}) and USD(D_{tmt}) models, for SC and NSC lung cancers, for representative parameters [Table 3], show that for the RTOG 0813^{[46]} dose levels, the BED (or EQD2) calculated by the LQ model gives unrealistic dose potency that varied from 1.05 to 1.22 times for SC lung cancer, and from 1.02 to 1.16 times for NSC lung cancers, higher than that calculated by the LQL(D_{tmt}) model. The BED (or EQD2) of LQ model was higher by 1.1 to 1.44 times for SC and by 1.05 to 1.24 times for NSC lung cancers than that calculated by the USC(D_{tmt}) model. Similarly the values of BED (or EQD2) calculated by the LQL(D_{tmt}) were 1.10–1.18 and 1.03–1.107 times higher than that calculated by the USC(D_{tmt}) model for SC and NSC lung cancers, respectively.
It is seen in the [Table 4] that the LQ model grossly overestimates the BED (or EQD2) for large dose per fraction treatment, and is likely flawed. The comparison between LQL(D_{tmt}) and USC(D_{tmt}), shows that the calculated survival fraction and BED (or EQD2), for RTOG 0813 dose scheme, is less sensitive for the USC(D_{tmt}) model and does not transition smoothly and gradually from the LQ model to asymptotic linear portion compared to the LQL(D_{tmt}) model. At the same time the USC(D_{tmt}) is unable to address radiobiological issues associated with SBRT treatment.
Conclusions   
The LQL(D_{tmt}) model provides best fit with smooth and gradual transition of the LQ model to linear portion of the survival curve at transition dose D_{tmt}. The D_{tmt} describes the dose at which the LQ model loses its validity and final linear portion of the curve begins. The fitting of the experimental dose response data in the range of high doses, used in SRS and SBRT, to the LQ, LQL(D_{t2α/β}), LQL(D_{tmt}) and USC(D_{tmt}) models illustrates that the LQL(D_{tmt}) model provides the best explanation of the problem. On the other hand, the LQ model overestimates the severity of response at high doses due to continuous bending of the curve, while the LQL(D_{t2α/β}) and USC(D_{tmt}) models do not transition smoothly to the linear portion of the curve. Results of this study show that the LQL(D_{tmt}) model is able to fit wide variety of cell survival data over a very wide range of doses. The transition dose D_{tmt} and final slope g, the log_{e} cell kill per unit dose in the final linear portion of the survival curve, can be calculated using D_{0} and obtained by the best fit exponential regression of experimental or multifraction dose response data. Plots of this study show that the LQL(D_{tmt}) model offers a best description of the cell survival data for SC and NSC cell lines in the high dose region well beyond the shoulder. With the LQL(D_{tmt}) model, the LQ model retains its all strengths in the lowdose range. The results of this study demonstrate that the LQL(D_{tmt}) would be the greatest clinical tool for intercomparison of conventional and hypofractionated treatment schemes.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References   
1.  Withers HR. Biologic basis for altered fractionation schemes. Cancer 1985;55:208695. [ PUBMED] 
2.  Ang KK, Peters LJ. Principles and practices of oncology. PPO Updates. 1994;4:115. 
3.  Douglas BG, Fowler JF. The effect of multiple small doses of x rays on skin reactions in the mouse and a basic interpretation. Radiat Res 1976;66:40126. [ PUBMED] 
4.  Wolbarst AB, Chin LM, Svensson GK. Optimization of radiation therapy: Integralresponse of a model biological system. Int J Radiat Oncol Biol Phys 1982;8:17619. [ PUBMED] 
5.  Yaes RJ, Kalend A. Local stem cell depletion model for radiation myelitis. Int J Radiat Oncol Biol Phys 1988;14:124759. [ PUBMED] 
6.  Amichetti M, Orrù P, Madeddu A, Murtas R, Carau B, Farigu R, et al. Comparative evaluation of two hypofractionated radiotherapy regimens for painful bone metastases. Tumori 2004;90:915. 
7.  Tong D, Gillick L, Hendrickson FR. The palliation of symptomatic osseous metastases: Final results of the study by the Radiation Therapy Oncology Group. Cancer 1982;50:8939. [ PUBMED] 
8.  Madsen EL. Painful bone metastasis: Efficacy of radiotherapy assessed by the patients: A randomized trial comparing 4 Gy X 6 versus 10 Gy X 2. Int J Radiat Oncol Biol Phys 1983;9:17759. [ PUBMED] 
9.  Lutz ST, Chow EL, Hartsell WF, Konski AA. A review of hypofractionated palliative radiotherapy. Cancer 2007;109:146270. [ PUBMED] 
10.  Ampil FL, Unger JB, Caldito G, Charrier A. Definitive and palliative radiotherapy for cervix cancer in the elderly. Eur J Gynaecol Oncol 2006;27:1158. [ PUBMED] 
11.  Arcangeli G, Giovinazzo G, Saracino B, D'Angelo L, Giannarelli D, Arcangeli G, et al. Radiation therapy in the management of symptomatic bone metastases: The effect of total dose and histology on pain relief and response duration. Int J Radiat Oncol Biol Phys 1998;42:111926. 
12.  Leksell L. Sterotaxic radiosurgery in trigeminal neuralgia. Acta Chir Scand 1971;137:3114. [ PUBMED] 
13.  Leksell L. Occasional review: Stereotactic radiosurgery. J Neurol Neurosurg Psychiatr 1983;46:797803. 
14.  Kurita H, Kawamoto S, Sasaki T, Shin M, Tago M, Terahara A, et al. Results of radiosurgery for brain stem arteriovenous malformations. J Neurol Neurosurg Psychiatry 2000;68:56370. [ PUBMED] 
15.  Benedict SH, Yenice KM, Followill D, Galvin JM, Hinson W, Kavanagh B, et al. Stereotactic body radiation therapy: The report of AAPM task group 101. Med Phys 2010;37:4078101. [ PUBMED] 
16.  Potters L, Kavanagh B, Galvin JM, Hevezi JM, Janjan NA, Larson DA, et al. American Society for Therapeutic Radiology and Oncology (ASTRO) and American College of Radiology (ACR) practice guideline for the performance of stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys 2010;76:32632. [ PUBMED] 
17.  Kirkbride P, Cooper T. Stereotactic body radiotherapy. Guidelines for commissioners, providers and clinicians: A national report. Clin Oncol (R Coll Radiol) 2011;23:1634. [ PUBMED] 
18.  Sahgal A, Roberge D, Schellenberg D, Purdie TG, Swaminath A, Pantarotto J, et al. The Canadian association of radiation oncology scope of practice guidelines for lung, liver and spine stereotactic body radiotherapy. Clin Oncol (R Coll Radiol) 2012;24:62939. [ PUBMED] 
19.  Hall EJ, Brenner DJ. The radiobiology of radiosurgery: Rationale for different treatment regimes for AVMs and malignancies. Int J Radiat Oncol Biol Phys 1993;25:3815. [ PUBMED] 
20.  Song CW, Kim MS, Cho LC, Dusenbery K, Sperduto PW. Radiobiological basis of SBRT and SRS. Int J Clin Oncol 2014;19:5708. [ PUBMED] 
21.  Withers HR. The 4R's of radiotherapy. In: Lett JT, Adler H, editors. Advances in Radiation Biology. Vol. V. New York: Academic Press; 1975. p. 2415. 
22.  Hall EJ. Time, dose and fractionation. In: Radiobiology for the Radiologist. 5 ^{th} ed. New York: Lippincott Williams Wilkins; 2000. p. 397418. 
23.  Barendsen GW. Dose fractionation, dose rate and isoeffect relationships for normal tissue responses. Int J Radiat Oncol Biol Phys 1982;8:198197. [ PUBMED] 
24.  Dale RG. The application of the linearquadratic doseeffect equation to fractionated and protracted radiotherapy. Br J Radiol 1985;58:51528. [ PUBMED] 
25.  Fowler JF. The linearquadratic formula and progress in fractionated radiotherapy. Br J Radiol 1989;62:67994. [ PUBMED] 
26.  Kirkpatrick JP, Meyer JJ, Marks LB. The linearquadratic model is inappropriate to model high dose per fraction effects in radiosurgery. Semin Radiat Oncol 2008;18:2403. [ PUBMED] 
27.  Joiner MC, Bentzen SM. Fractionation: The linearquadratic approach. In: Joiner M, van der Kogel A, editors. Basic Clinical Radiobiology. London: Hodder Arnold; 2009. p. 10219. 
28.  Dale R. Use of the linearquadratic radiobiological model for quantifying kidney response in targeted radiotherapy. Cancer Biother Radiopharm 2004;19:36370. [ PUBMED] 
29.  Astrahan M. Some implications of linearquadraticlinear radiation doseresponse with regard to hypofractionation. Med Phys 2008;35:416172. [ PUBMED] 
30.  Puck TT, Marcus PI. Action of xrays on mammalian cells. J Exp Med 1956;103:65366. [ PUBMED] 
31.  Park C, Papiez L, Zhang S, Story M, Timmerman RD. Universal survival curve and single fraction equivalent dose: Useful tools in understanding potency of ablative radiotherapy. Int J Radiat Oncol Biol Phys 2008;70:84752. [ PUBMED] 
32.  Park HJ, Griffin RJ, Hui S, Levitt SH, Song CW. Radiationinduced vascular damage in tumors: Implications of vascular damage in ablative hypofractionated radiotherapy (SBRT and SRS). Radiat Res 2012;177:31127. [ PUBMED] 
33.  Guerrero M, Li XA. Extending the linearquadratic model for large fraction doses pertinent to stereotactic radiotherapy. Phys Med Biol 2004;49:482535. [ PUBMED] 
34.  Carlone M, Wilkins D, Raaphorst P. The modified linearquadratic model of Guerrero and Li can be derived from a mechanistic basis and exhibits linearquadraticlinear behavior. Phys Med Biol 2005;50:L915. [ PUBMED] 
35.  Wennberg BM, Baumann P, Gagliardi G, Nyman J, Drugge N, Hoyer M, et al. NTCP modelling of lung toxicity after SBRT comparing the universal survival curve and the linear quadratic model for fractionation correction. Acta Oncol 2011;50:51827. [ PUBMED] 
36.  Kiefer J, editor. A repair fixation model. In: Quantitative Mathematical Models in Radiation Biology. New York: SpringerVerlag; 1988. 
37.  Haynes RH. The interpretation of microbial inactivation and recovery phenomena. Radiat Res Suppl 1966;6:129. 
38.  Laurie J, Orr JS, Foster CJ. Repair processes and cell survival. Br J Radiol 1972;45:3628. [ PUBMED] 
39.  Reddy NM, Mayer PJ, Lange CS. The saturated repair kinetics of Chinese hamster V79 cells suggests a damage accumulation – Interaction model of cell killing. Radiat Res 1990;121:30411. [ PUBMED] 
40.  Sontag W. A cell survival model with saturable repair after irradiation. Radiat Environ Biophys 1987;26:6379. [ PUBMED] 
41.  Goodhead DT. Saturable repair models of radiation action in mammalian cells. Radiat Res Suppl 1985;8:S5867. [ PUBMED] 
42.  Tym R, Todd PW. The sensitization by iododeoxyuridine of cultured human cells to the lethal effect of xrays and heavy ions. Int J Radiat Biol Relat Stud Phys Chem Med 1964;8:589603. [ PUBMED] 
43.  Carney DN, Mitchell JB, Kinsella TJ. In vitro radiation and chemotherapy sensitivity of established cell lines of human small cell lung cancer and its large cell morphological variants. Cancer Res 1983;43:280611. [ PUBMED] 
44.  Carmichael J, Degraff WG, Gamson J, Russo D, Gazdar AF, Levitt ML, et al. Radiation sensitivity of human lung cancer cell lines. Eur J Cancer Clin Oncol 1989;25:52734. [ PUBMED] 
45.  Krarup M, Poulsen HS, SpangThomsen M. Cellular radiosensitivity of smallcell lung cancer cell lines. Int J Radiat Oncol Biol Phys 1997;38:1916. [ PUBMED] 
46.  RTOG 0813. Seamless Phase I/II Study of Stereotactic Lung Radiotherapy (SBRT) for Early Stage, Centrally Located, NonSmall Cell Lung Cancer (NSCLC) in Medically Inoperable Patients. Available from: http://www.rtog.org. [Last accessed on 2016 Dec 15]. 
47.  Biade S, Stobbe CC, Chapman JD. The intrinsic radiosensitivity of some human tumor cells throughout their cell cycles. Radiat Res 1997;147:41621. [ PUBMED] 
48.  Albright N. Computer programs for the analysis of cellular survival data. Radiat Res 1987;112:33140. [ PUBMED] 
49.  Wennberg B, Lax I. The impact of fractionation in SBRT: Analysis with the linear quadratic model and the universal survival curve model. Acta Oncol 2013;52:9029. [ PUBMED] 
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9]
[Table 1], [Table 2], [Table 3], [Table 4]
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