Modeling the Cellular Response of Lung Cancer to Radiation Therapy for a Broad Range of Fractionation Schedules

Until recently, the standard treatment for lung cancer, even localized disease, has been to give 60 Gy total in 30 weekday fractions, with poor resulting local control rates (1, 2). However, the ongoing trend toward hypofractionated radiotherapy (called stereotactic body radiation therapy, or SBRT), for early-stage disease, has resulted in a range of higher rates of local disease control (3–7), often as high as 95%. In SBRT, advanced imaging localization and immobilization techniques are used to deliver dose distributions that are highly conformal to the disease target volume, whereas exposure to the surrounding normal tissues is reduced because of the use of multiple converging beams. SBRT is mostly prescribed for localized, early-stage tumors requiring limited treatment volumes, thereby resulting in tolerable normal tissue damage. However, the precise dose and number of fractions needed have not been well-established and a wide variety of dose-fraction schemes are currently employed.

The goal of this research is to address this need, at once providing a quantitative model to predict the likelihood of local control for different fractionation schemes while also moving toward a more mechanistic radiobiological understanding of tumor response to radiotherapy.

Previous efforts to model the likelihood of local control for early-stage lung cancer divide into two categories that can be labeled empirical or mechanistic. Empirical models usually invoke a fitted linear + quadratic dependence on fractional dose (the so-called L-Q model), sometimes adding an empirical proliferation effect (8–11). Empirical models typically yield shallow dose–response curves, resulting in limited clinical usefulness. The dispersion of data points around such models is typically large, indicating that the data cannot be drawn from (i.e., “explained by”) the proposed empirical distributions (12). Mechanistic models of the tumor cellular response to radiotherapy have been previously published for multiple tumor sites (13–16). However, none has been fit to extensive clinical datasets.

Our model introduces the driving assumption that hypoxia versus proliferation is mediated via a local competition for chemical resources (17). Furthermore, we explicitly introduce a category of cells experiencing intermediate levels of hypoxia in which glucose, but not oxygen, is adequately present to provide metabolic viability. Thus, we go beyond the model of Borkenstein and colleagues (14), who stated: “Intermediate levels of hypoxia, resulting in a gradient in proliferative activity and intermediate OER (oxygen enhancement ratio) values, are not accounted for, even though they are considered to be important for tumor response.”

In nonmechanistic models, a repopulation term is sometimes included in the cumulative dose effect calculation based on exponential clonogen repopulation for overall treatment times longer than an ad hoc “kick-off” time, typically fixed as 3 to 4 weeks (18). This repopulation effect emerges in a natural way in our model, which eliminates the need for the ad hoc assumption.

The ability of the L-Q model alone to describe cell kill at high fraction doses (greater than about 10 Gy), as used in SBRT, has been questioned, leading to proposed modifications of the basic cell kill equations (7, 19). However, when only a few fractions are given, our modeling predicts it is the hypoxic cells that are most important in determining response. In this case, the dose effect for hypoxic cells is scaled by 1/OER (20). Thus, if we take into account hypoxia, the L-Q equation can still be used (21–23).

Several important studies support the argument showing that there might be other tumoricidal effects at high fraction size, including direct vascular damage (24, 25) and potential immune induction effects (26). Our approach is to leave out these partially understood effects to see if they are required to quantitatively model early-stage lung cancer response.

In this study, we apply the previously developed tumor-response model with additional cell-cycle effect to the extensive clinical outcome data of lung cancer to see if the model based on classical radiobiological effects can accurately predict the tumor response likelihood of the various fractionation schemes.

A previously developed, time evolution state-driven tumor-response model was used to simulate a wide range of fractionation regimens (17). The model assumes that a given contiguous fraction of the tumor is fed by a supply of chemical resources, which remains constant over a course of radiotherapy. Cells are distributed between three compartments: proliferating (P), intermediate (I), and (extremely) hypoxic (H) compartments, as shown in Fig. 1A. Proliferation takes place in the P-compartment, which is assumed to receive enough oxygen and glucose to support intermittent cell-cycle progression. Cell loss takes place in the highly hypoxic H-compartment, which is assumed to receive no oxygen or glucose. In the I-compartment, which recieves glucose but not oxygen, neither proliferation nor cell loss is assumed to take place (27). A fraction of cells in the P-compartment constantly proliferate. Radiotherapy is assumed to doom cells consistent with the L-Q model. This results in “doomed” cells that have lost indefinite proliferation capacity after sterilization. Thus, each compartment also has a doomed cell subcompartment. As shown in Fig. 1B, following the postmiotic cell death and clearance of proliferating cells, cells in the intermediate compartment begin to receive oxygen and therefore move into the proliferative compartment. At the same time, any surviving hypoxic-compartment cells move into the intermediate compartment (Fig. 1C). This naturally results in the process of reoxygenation over a course of radiotherapy, allowing us to evaluate treatment response of various fractionation schemes based on the interplay between hypoxia and proliferation.

For the large fraction size in SBRT, known cell-cycle variations in radiosensitivity could potentially be important (28, 29) and have been added to the model in terms of effective radiosensitivity and effective OER, as described below.

The variability of radiosensitivity within the cell cycle has long been recognized (30–34). In general, cells in the S-phase (especially late S-phase) are known to be the most radioresistant, and cells near mitosis (G₂–M-phases) are known to be most radiosensitive. Although the radiosensitivity of the early G₁-phase can be as high as the S-phase for cells with long G₁-phase times, the radiosensitivity of the G₁-phase is generally in between those two phases (S and G₂–M; ref. 35).

To account for the cell-cycle effect on radiosensitivity, the population of proliferating tumor cells is decomposed into three subpopulations (G₁, S, and G₂–M). The standard L-Q model was used to calculate the survival fraction (SF) as shown in the following equation:

where α and β are the radiosensitivity parameters in the L-Q model and d is the fractional dose.

The total survival fraction for a given fractional dose is obtained as a weighted summation of survival fractions of the three subpopulations, from which the effective radiosensitivity parameters (α_eff and β_eff) can be estimated as shown in equation 2. The effective radiosensitivity is dependent on both the fraction of cells and the radiosensitivity in each cell cycle.

where, α_eff and β_eff are the effective L-Q parameters, d is the fractional dose, and f_X, α_X and β_X are the fraction of cells, linear parameter, and quadratic parameter for a given cell cycle X (G1, S, or G₂–M).

Increased radioresistance of hypoxic cells can be quantified in terms of the OER, which is the ratio of the required dose in hypoxic conditions to the dose in normoxic conditions (36). Carlson and colleagues (20) have shown that the dependence of radiosensitivity on alpha and beta coefficients is inversely related to the first and second order of the OER, respectively. In the current study, for simplicity, all the hypoxic cells are considered to be in a nonproliferating (quiescent) state (37), and therefore, have constant radiosensitivity. On the basis of the OER value applied to conventional 2 Gy/fx (OER_ref), the effective OER (OER_eff) can be found as a function of the fractional dose, as given in the following equation:

where SF_hyp is the survival fraction of hypoxic cells, α_eff and β_eff are the effective L-Q parameters derived from the normoxic cells in the cell cycle (Eq. 2), OER_eff is the dose dependent effective OER value, α_ref and β_ref are the reference L-Q parameters at 2 Gy/fx, and OER_ref is the reference OER value at 2 Gy/fx.

A few key variables are fitted to the data, but most model parameters do not have a significant impact on the model fit, and are therefore set once based on other publications or plausibility. In the model, the relative degree of proliferation versus hypoxia-caused cell death is determined by the initial size of each compartment, derived through exact algebraic relationships with the well-known parameters of initial growth fraction (GF) and initial volume doubling time (T_D). Representative GF and T_D values of 0.25 and 100 days, respectively, were used for the simulation, based on measured data for lung cancer (38, 39). The cell-cycle time (T_C) was assumed to be 2 days. The cell-cycle distribution was taken as 78%, 12% and 10% for G₁-, S-, G₂–M-phases, respectively, based on a flow cytometric analysis of 187 surgical specimens of non–small cell lung cancer (40). Because the exact cell-cycle–dependent radiosensitivity values are not available for lung cancer, the ratios of radiosensitivity of G₁- and G₂–M-phases, with respect to the most resistant S-phase, were taken to be 2 and 3, respectively (α_G1/α_S = 2 and α_G2/M/α_S = 3), based on the radiosensitivity analysis of synchronized cell populations (31, 32), The radiosensitivity of each cell-cycle phase was therefore derived from the overall fitted radiosensitivity value (41).

The OER value at 2 Gy/fx for the H-compartment cells was expected to be significantly less than the theoretical maximum of about three, considering the lower OER observed of G₀–G₁ phase and reduced repair capability of chronically hypoxic cells (42). Parameter values used for the model simulation were summarized in Table 1. The α value, the α/β ratio, and the OER value for intermediately hypoxic cells (OER_I) were derived by fitting the dataset.

Individual clinical outcome data included in the study of Mehta and colleagues (43) were reviewed and filtered with the goal of increasing consistency. Patient cohorts were separated into three groups: conventional fractionation RT (1.8–3 Gy/fx); SBRT with several fractions (3–10 fxs); and single-fraction SBRT. The cohorts of conventional RT included in Mehta and colleagues' (43) analysis exhibit excessively heterogeneous outcomes in a narrow dose range, which makes the comparison with other groups difficult. For consistency within the group, trials were excluded that: used fraction sizes larger than 3 Gy/fx; used twice-a-day fractionation (bid); or used split course RT, as indicated in Supplementary Table S1. This resulted in nine excluded cohorts. We also included three additional cohorts in the conventional group with two from our institutions. A total of 38 cohorts (2,701 patients) were included in the analysis, comprised of 10 conventional, 22 multifraction SBRT, and 6 single-fraction SBRT cohorts, as summarized in Supplementary Table S2.

From each cohort, detailed treatment and outcome information was extracted for model simulation and analysis, including the number of patients, total dose, fractional dose, number of fractions, treatment schedule, dose prescription method, and local control rate. Some cohorts employed multiple fractionation schemes, but reported only the integrated total outcome. For those, separate fractionation schemes were simulated with the model and the population-weighted mean was used to represent the whole cohort. When separate outcomes were available for different fractionation schemes, the patient cohort was divided into separate cohorts.

Because SBRT dose distributions are not uniform, fractional doses at the center and at the PTV margin were averaged and used as the representative dose in the model simulation, which assumed a homogeneously irradiated tumor. For example, when a 10 Gy fractional dose was prescribed to the 80% isodose encompassing the PTV, the maximum dose at the central PTV becomes 12.5 Gy, whereas the dose at the periphery of the PTV is 10 Gy. The prescribed dose inside the PTV is therefore between 10 and 12.5 Gy and we average these values (11.25 Gy) to represent the dose to the tumor. Analyses in which full dose-volume histograms are available would afford greater accuracy in this regard, but could only be carried out for smaller cohorts and a much more limited range of fractionation schedules.

To compare the outcomes of various fractionation schemes, the treatment effects of all nonstandard regimens were normalized in terms of a model-derived equivalent dose in conventional fractionation (2 Gy/fx, 5 fx/week), for variable α/β ratios, for example, for a ratio of 10 (EQD2_10,model). The estimation of the EQD2_α/β,model was carried out with two separate model simulations: first, an overall cell survival fraction was estimated on the basis of the fraction size and the fractionation schedule; then, a corresponding conventional 2 Gy-fractionation schedule was simulated to find the dose at which the same level of stem cell surviving fraction would be achieved, resulting in a model-derived equivalent dose at 2 Gy/fx. The process is shown schematically in Fig. 1D and E.

The maximum likelihood (MLE) method (see below) was used to find the best fit dose–response curve, either for the total group or for separate fractionation groups. The slope of the dose–response curve (γ₅₀) was fixed and assumed to be 1.5, which is known to be a clinically relevant value for NSCLC (41), which was found to adequately describe the data.

In radiotherapy, radiation dose is not the only factor that determines treatment efficacy. In reality, it might be difficult to achieve 100% of tumor control, even with a very high radiation dose, due to the potential to geographically miss a small fraction of occult cells. For example, Chao and colleagues (44) summarized pathology reports showing the fall off of occult disease with increasing distance from the gross disease boundary. We observed empirically that, even at the highest obtained effective doses, local control typically saturated at 95%, which was therefore set as an upper bound in the logistic model (45), as follows:

where TD₅₀ is the tumor dose at which 50% of TCP is expected, γ₅₀ is the slope of the curve at TD₅₀, and D is the total dose of the treatment, which is EQD2_α/β,_model in this analysis.

To test the validity of the fit, χ² tests were performed. A P value close to unity would indicate the dose–response curve was statistically representative for the fitted data; in contrast, small P values indicate that the dose–response curve is not statistically drawn from the observed data.

Among the parameter values in the model, we found that the simulation results were most sensitive to radiosensitivity-related parameters such as the α value, α/β ratio, and OER_I value. Therefore, data fitting focused on these assumed parameter values, as well as a TD₅₀ value. For possible ranges of these parameter values, simulations were performed to see if the dose responses of the different groups could be fit into a logistic function with a high log likelihood value in the scatter plot of the tumor control rate vs. EQD2_α/β,model. The tests were performed iteratively for α values versus α/β ratios and α/β ratios vs. OER_I values, until all the values were stabilized with a maximum log likelihood value. The 95% confidence intervals (95% CI) of the parameter estimates were found through the profile likelihood method. Optimal fit values and uncertainty ranges are reported.

For the m cohorts in each group, the overall log likelihood can be found from the following equation:

where, p_i is the predicted TCP by the logistic fit, n_i is the total number of patients, and y_i is the number of patients with local control in each i^th cohort.

For increasing TD₅₀ values of the logistic curve with a fixed γ₅₀ value of 1.5, the overall log likelihood values were found. The TD₅₀ value that maximizes the overall log likelihood was searched and the 95% CI was found according to the “profile likelihood method” (46, 47).

For proliferating tumor cells in the P-compartment, the radiosensitivity of each cell-cycle phase was estimated from equation 2, based on the relevant cell-cycle phase distribution for lung cancer and the assumed ratios of radiosensitivities (α_G1/α_S = 2 and α_G2/M/α_S = 3). For the reference radiosensitivity of 0.35 Gy⁻¹ at 2 Gy/fx, the radiosensitivity of each cell cycle was calculated to be 0.376, 0.188, and 0.564 for G1-, S-, and G₂–M-phases, respectively.

Using the calculated cell-cycle–dependent radiosensitivity values, the surviving fraction of cells in each cycle phase was estimated as shown in Fig. 2A, along with the total surviving fraction. As the fractional dose increases, relatively sensitive cells in G₂–M- and G₁-phases are preferentially killed, followed at higher doses by more resistant cells in the S-phase. Cells in the S-phase become dominant after about 5 Gy/fx and the total surviving fraction in the proliferative (P) compartment is mainly governed by the S-phase surviving fraction. From the overall surviving fraction, the effective alpha value was derived and presented as a function of the fractional dose in Gy as shown in Fig. 2B. As the fractional dose increases, the effective alpha values decrease and approach the alpha value of the most resistant S-phase (α_S).

Hypoxia was considered in the model with two hypoxic compartments (I- and H-compartments). Because hypoxic cells were assumed to be only in G₀–G₁ phase (37, 48, 49), the surviving fractions of hypoxic compartments are determined by a single radiosensitivity value, whereas the surviving fraction in the proliferating compartment is composed of three different cell-cycle phases (G₁, S and G₂–M) with different radiosensitivities. The survival fraction of each compartment is estimated as shown in Fig. 2C. Because of the relatively resistant S-phase in the P-compartment, the slope of the surviving fraction of the P-compartment becomes shallow as the fractional dose increases, and becomes similar to that of the H-compartment. Although the survival fraction of the I-compartment is still shallower than that of the P-compartment, the relative resistance decreases with the increasing fractional dose. This decreases the effective OER values for the hypoxic compartments, as shown in Fig. 2D.

Because α/β = 10 is often quoted as a reasonable parameter for tumors, we included it as a reference. EQD2_10,model values were estimated through model simulation for all the non-standard fractionation schemes. In Fig. 1D and E, an example of EQD2_10,model estimation was shown for a SBRT regimen from Takeda and colleagues (50). The model simulation was performed for the SBRT regimen (11.3 Gy × 5 fxs in 9 days), in which the survival fraction was estimated to be 1.39 × 10⁻⁸ (Fig. 1D). Another simulation with the conventional fractionation (2 Gy/fx, 5 fx/wk) was performed, and the same level of survival fraction could be achieved at about 77.6 Gy (Fig. 1E), from which the EQD2_10,model of the SBRT regimen was determined.

To compare the evaluated treatment efficacies of SBRT regimens between BED and EQD2_10,model, the ratio of EQD2_10,model/BED was computed with respect to the number of fractions, the treatment duration, and fraction sizes. As shown in Supplementary Fig. S1, the ratio of EQD2_10,model/BED increased with a larger number of fractions, longer treatment duration, and smaller fraction size, due to cell-cycle reassortment between fractions and increased reoxygenation with longer schedules. The largest differences were seen for single-fraction regimens, due to the impact of hypoxic cells, with ratios of about 0.5.

The EQD2_10,model values of the 38 cohorts were computed through model simulation, and tumor control rates were fitted on a logistic function via the MLE method for the separate groups or the total group. For comparison, in Fig. 3A, we plot results using values of α = 0.35 Gy, the commonly quoted value of α/β = 10/Gy, and an OER_I value of 2. A grid search was performed to find the best fits. As supported by the resulting small uncertainty intervals, overfitting was not an issue given a dataset this large. Although the different groups follow clear logistic dose–responses, with different TD₅₀ values, we find that combining all the fitted data points into a single logistic dose–response curve yields a χ²-test of the fit yielding a P value of much less than 0.001 (Supplementary Fig. S2A). This indicates that the data could not be drawn from the model with those parameters.

Figure 3B and C show the landscape of best-fit log likelihood values for varying parameters. As shown in Fig. 3D, when we choose optimal values of α, α/β, and OER_I, all the best fit curves from the fractionation groups are closely aligned, which indicates that the fit is not driven by any single group and represents all three groups well. The best fit radiobiological parameters were α = 0.305 Gy⁻¹ (95% CI, 0.120–0.365), α/β = 2.80 Gy (95% CI, 0.40–4.40), and OER_I = 1.7 (95% CI, 1.55–2.25). In units of EQD2_2.8,model, the TD₅₀ was 62.1 Gy (Supplementary Fig. S2B). Hence, a typical early-stage lung tumor had a 50% chance of local control if given 62 Gy in 2 Gy/weekday fractions. As shown in Supplementary Fig. S2B, these parameters result in an excellent fit when using a single curve for all fractionation groups, with a high resulting χ² test P value (P ≈ 1.00). Through the profile likelihood method, the 95% CIs of the TD₅₀ values were estimated for each group and the total cohorts, as shown in Supplementary Fig. S3.

The human lung is a very heterogeneous tissue, which causes secondary electrons set in motion by incident photons to be scattered with disparate distributions (51, 52). Simple, ray-trace algorithms are known to misestimate dose as well as underestimate required field widths (53, 54). Hence, we might expect that dose-calculation algorithms capable of modeling such differences (i.e., the convolution-superposition algorithm family) could provide dosimetric data that produce less residual error. Five cohorts were identified to have used more advanced dose-calculation algorithms such as convolution or superposition algorithms. As shown in Fig. 4A, those five cohorts showed better agreement with the dose–response curve, compared to other cohorts with a less accurate dose-calculation algorithm.

To test whether differences in follow-up time affected the results, a plot of median follow-up time versus the residual of the best logistic fit is shown in Fig. 4B for 28 cohorts with available follow-up time information. No discernible pattern was noted for all groups, which establishes that ignoring differences in follow-up time was appropriate.

For validation of the model analysis, more recently published outcome data were reviewed for stage I lung cancer treated with SBRT. Fifteen relevant studies were identified and included into the validation dataset, which is comprised of 23 different patient cohorts with 1628 patients (Supplementary Table S3). The best-fit parameter values derived from the original datasets were used for the modeling. As shown in Fig. 5, the dose response curve derived from the original datasets perfectly fits the validation datasets with the χ²P value of 1.00.

The motivation of this work is to establish the usefulness of a radiobiological model to describe and predict response to radiotherapy for localized lung cancer. The fitted results show that the model is successful in this regard, despite being an oversimplification of the response of complicated biological entities to non-uniform dose distributions given under widely varying conditions. Despite this, for the first time, a mathematical model robustly reproduces tumor dose-response across the complete range of clinical fractionation regimens, which has been further validated with additional dataset.

To quantify uncertainty in the resulting dose–response curve for single-fraction SBRT, we plot the change in log likelihood of the overall fit when the TD₅₀ is varied, assuming the other best-fit parameters are still valid. The result is that the 95% CI envelope is relatively wide (53.0–67.3 Gy); see Supplementary Fig. S3C. Thus, nonstandard “new biology” effects could potentially emerge as a component to the lung dose–response curves, but only with the collection of further single fraction results will be able to clarify this situation. Nonetheless, in the absence of any disagreement between the model and single-fraction clinical results, such effects should be considered theoretical.

The OER of hypoxic cells is a crucial variable in the model. Palcic and Skarsgard found that the OER is dependent on dose (55–57), and others have shown that the dose dependency of OER is a consequence of the variation of OER across cell-cycle phases, for example Freyer and colleagues (58) who showed that the OER of Chinese hamster ovary (CHO) cells was highest in S-phase (2.8–2.9) and lowest in G₁-phase (2.3–2.4). As dose increases, the most resistant S-phase cells dominate the surviving fraction; this causes an increase of OER at a higher fractional dose.

Our in situ-derived value (OER_I = 1.70) is lower than the commonly quoted maximum of about 3, as measured in vitro, where cell cultures are, typically, briefly exposed to nitrogen or argon gas (55, 57). However, there are several reasons to believe that the effective value of OER in a tumor is significantly less than the theoretical maximum of 3. Wouters and Brown (59) discuss the importance of intermediately hypoxic cells, with effective OERs between 1 and 2. In tumors, the vast majority of (chronically) hypoxic cells are thought to be in a quiescent, though metabolically active, phase, and cannot proliferate (37). Similarly, confluent (plateau phase) cell cultures, in which nutrient or space is limited to imitate the in vivo condition of the tumor, consist mostly of cells out of cell cycle (G₀–G₁-phase), and yields decreased OER values (37, 60). Other studies show that cells in G₀ might be more sensitive to radiation damage compared to cells in G₁ (61, 62). It has also been suggested that repair mechanisms for hypoxic cells are less effective than normoxic cells (42, 63, 64). Perhaps most saliently, cells experiencing chronic hypoxia have been shown to have OER values similar to what we find (65). Hence, our result of an OER of 1.7 for cells receiving glucose, but not oxygen, is consistent with established results.

In the model, the primary determinant of the surviving fraction following SBRT is the fraction of hypoxic cells in the intermediate compartment at the beginning of therapy, whereas 2 Gy/weekday regimens have the advantage of eventual reoxygenation and elimination of the hypoxic component. Because hypoxia reduces the effective dose seen by irradiated cells, departures from the linear-quadratic model that have been proposed to take effect at high doses are seemingly not relevant (66–68).

We freely acknowledge various uncertainties in model parameters. We assumed that a growth fraction of 25% and a tumor doubling time of 100 days would be representative. In fact, these data are not available for most human tumors and, although it is consistent with some publications (38, 39), there is also undoubtedly substantial heterogeneity in the parameters, within and between tumors.

Recently, it was argued that SBRT outcomes are consistent as conventional RT (using the L-Q model) without considering hypoxia or proliferation (43). However, we previously addressed this, having shown that the proposed dose–response curve was not a good statistical fit to that dataset (12). Our model P values show that the available data are entirely consistent with our mechanistic model. Other articles have attempted to model the same or similar datasets (69, 70). However, those articles show poor fits overall (with no computed fit p-values), with much wider uncertainties on fitted parameters. When those models were applied to our dataset, both models provided poor fits with very low P values (P < 0.001), as shown in Supplementary Fig. S4.

It has been proposed that vascular endothelial cell damage might play an important role in the SBRT regimen. If this takes place during therapy, blood supply may decrease and the assumption of invariable blood supply would not apply. Although it seems likely that there would be a vascular effect, we do not see it in our data analysis.

Despite the idealized nature of the model, the resulting radiological parameters have reasonable values and small uncertainty bands. The excellent fit results support the hypothesis that the model, incorporating standard radiobiological principles plus a novel chemical conservation principle, provides a rational basis to understand radiation dose-response for early-stage lung cancer across a very wide range of fractionation and dose prescription treatment regimens. The model may be useful in the rational selection of optimized lung cancer radiotherapy protocols, potentially extended even to personalized fractionation schedules and dose prescriptions. As seen in the fitted results, many clinical protocols either overtreat of undertreat early-stage lung disease.

In conclusion, we have shown for the first time that a mechanistic mathematical model can quantitatively predict the tremendous differences in response seen in early-stage lung cancer across the range of clinical dose and fractionation schemes. Fitting of the model to a large range of reported clinical cohorts results in reasonable radiobiological parameter values with small uncertainty intervals. The model can therefore be used to predict which fractionation schemes over- or undertreat lung tumors.

J. Sonke reports receiving commercial research grants from Elekta Oncology Systems Ltd. J.D. Bradley reports receiving speakers bureau honoraria from, is a consultant/advisory board member for and reports receiving commercial research grants from Mevion Medical Systems and ViewRay, Inc. J. Deasy reports receiving commercial research grants from Varian Corp. No potential conflicts of interest were disclosed by the other authors.

Conception and design: J. Jeong, J.D. Bradley, J.O. Deasy

Development of methodology: J. Jeong, A.N. Fontanella, J.O. Deasy

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J. Jeong, J.-J. Sonke, J. Belderbos, S.S. Rao

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J. Jeong, J.H. Oh, J. Belderbos, J.D. Bradley, A.N. Fontanella, J.O. Deasy

Writing, review, and/or revision of the manuscript: J. Jeong, J.D. Bradley, A.N. Fontanella, S.S. Rao, J.O. Deasy

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J. Jeong, S.S. Rao, J.O. Deasy

Study supervision: J.O. Deasy

We thank Alan Nahum for useful discussions. This research was supported by research grants from Varian Oncology and the NIH MSKCC Core Grant (P30 CA008748).

This research was supported by research grants from Varian Medical System (PI: Joseph O. Deasy) and the NIH MSKCC Core Grant (P30 CA008748; PI: Craig Thompson).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.